Correlation between aggregated molecular cancer subtypes and selected clinical features

Head and Neck Squamous Cell Carcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

doi:10.7908/C1930S29

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1930S29

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 13 clinical features across 494 patients, 12 significant findings detected with P value < 0.05 and Q value < 0.25.

4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'NUMBERPACKYEARSSMOKED'.
7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE', 'GENDER', and 'NUMBER.OF.LYMPH.NODES'.
CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY.N.STAGE' and 'NUMBER.OF.LYMPH.NODES'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GENDER' and 'NUMBER.OF.LYMPH.NODES'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 12 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	0.783 (1.00)	0.224 (1.00)	0.628 (1.00)	0.148 (1.00)	0.526 (1.00)	0.606 (1.00)	0.503 (1.00)	0.0743 (1.00)	0.338 (1.00)	0.918 (1.00)
AGE	Kruskal-Wallis (anova)	0.0484 (1.00)	2.48e-08 (3.12e-06)	0.255 (1.00)	0.0759 (1.00)	0.322 (1.00)	0.00402 (0.45)	0.075 (1.00)	0.0811 (1.00)	0.851 (1.00)	0.636 (1.00)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.558 (1.00)	0.692 (1.00)	0.519 (1.00)	0.0292 (1.00)	0.184 (1.00)	0.61 (1.00)	0.438 (1.00)	0.16 (1.00)	0.134 (1.00)	0.341 (1.00)
PATHOLOGY T STAGE	Fisher's exact test	0.144 (1.00)	0.0168 (1.00)	0.168 (1.00)	0.192 (1.00)	0.299 (1.00)	0.0139 (1.00)	0.166 (1.00)	0.0165 (1.00)	0.0351 (1.00)	0.568 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	0.0801 (1.00)	0.0535 (1.00)	0.357 (1.00)	1e-05 (0.00124)	0.22 (1.00)	0.00392 (0.443)	0.0641 (1.00)	0.0445 (1.00)	0.588 (1.00)	0.797 (1.00)
PATHOLOGY M STAGE	Fisher's exact test	0.888 (1.00)	0.0445 (1.00)			0.318 (1.00)	0.128 (1.00)	0.315 (1.00)	0.203 (1.00)	0.0127 (1.00)	0.494 (1.00)
GENDER	Fisher's exact test	0.146 (1.00)	5e-05 (0.0061)	0.00377 (0.43)	0.218 (1.00)	0.00868 (0.903)	0.002 (0.23)	0.0807 (1.00)	0.0153 (1.00)	0.124 (1.00)	0.461 (1.00)
HISTOLOGICAL TYPE	Fisher's exact test	0.117 (1.00)	0.015 (1.00)			0.00128 (0.15)	0.00657 (0.703)	0.00819 (0.86)	0.217 (1.00)	0.00037 (0.044)	0.0955 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.524 (1.00)	0.515 (1.00)	0.00716 (0.759)	0.439 (1.00)	0.252 (1.00)	0.0219 (1.00)	0.0951 (1.00)	0.123 (1.00)	0.00631 (0.681)	0.0011 (0.13)
NUMBERPACKYEARSSMOKED	Kruskal-Wallis (anova)	0.000252 (0.0302)	0.0043 (0.477)	0.0841 (1.00)	0.746 (1.00)	0.0224 (1.00)	0.0443 (1.00)	0.336 (1.00)	0.119 (1.00)	0.0052 (0.566)	0.205 (1.00)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.328 (1.00)	0.000106 (0.0129)	0.208 (1.00)	4.76e-06 (0.000595)	0.0326 (1.00)	3.67e-05 (0.00451)	0.0197 (1.00)	0.0018 (0.209)	0.305 (1.00)	0.168 (1.00)
RACE	Fisher's exact test	0.253 (1.00)	0.0294 (1.00)	0.111 (1.00)	0.415 (1.00)	0.493 (1.00)	0.124 (1.00)	0.0236 (1.00)	0.0748 (1.00)	0.00501 (0.551)	0.0779 (1.00)
ETHNICITY	Fisher's exact test	0.673 (1.00)	0.732 (1.00)	0.739 (1.00)	0.542 (1.00)	0.308 (1.00)	0.778 (1.00)	0.388 (1.00)	0.434 (1.00)	0.912 (1.00)	0.681 (1.00)

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

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

Cluster Labels	1	2	3	4
Number of samples	143	131	175	39

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

P value = 0.783 (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), Year
ALL	414	113	2.0 - 5480.0 (701.0)
subtype1	115	36	2.0 - 5480.0 (670.0)
subtype2	116	32	5.0 - 3295.0 (721.0)
subtype3	150	37	4.0 - 3835.0 (692.0)
subtype4	33	8	3.0 - 2886.0 (785.0)

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 = 0.0484 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	487	60.8 (12.0)
subtype1	143	58.4 (13.4)
subtype2	131	62.5 (9.6)
subtype3	174	60.8 (12.5)
subtype4	39	63.4 (10.7)

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

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

P value = 0.558 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	26	75	74	235	12	1
subtype1	7	20	22	77	5	1
subtype2	6	16	18	68	4	0
subtype3	11	32	27	73	1	0
subtype4	2	7	7	17	2	0

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

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

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

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	47	127	94	162
subtype1	12	38	30	53
subtype2	9	28	30	48
subtype3	23	51	23	50
subtype4	3	10	11	11

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

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

P value = 0.0801 (Fisher's exact test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	170	63	152	8
subtype1	48	18	53	2
subtype2	44	12	46	3
subtype3	67	24	41	1
subtype4	11	9	12	2

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

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

P value = 0.888 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	MX
ALL	167	1	58
subtype1	45	1	15
subtype2	38	0	15
subtype3	66	0	21
subtype4	18	0	7

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	132	356
subtype1	38	105
subtype2	27	104
subtype3	53	122
subtype4	14	25

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

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

P value = 0.117 (Fisher's exact test), Q value = 1

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

nPatients	HEAD AND NECK SQUAMOUS CELL CARCINOMA	HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT	HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE
ALL	477	1	10
subtype1	143	0	0
subtype2	127	0	4
subtype3	168	1	6
subtype4	39	0	0

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

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

P value = 0.524 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	79	409
subtype1	24	119
subtype2	23	108
subtype3	29	146
subtype4	3	36

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.000252 (Kruskal-Wallis (anova)), Q value = 0.03

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

	nPatients	Mean (Std.Dev)
ALL	272	45.8 (35.8)
subtype1	71	39.2 (21.5)
subtype2	88	55.7 (34.0)
subtype3	90	44.3 (45.7)
subtype4	23	34.0 (23.7)

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

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

P value = 0.328 (Kruskal-Wallis (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	382	2.2 (4.4)
subtype1	121	2.3 (4.0)
subtype2	106	2.8 (6.4)
subtype3	119	1.6 (2.4)
subtype4	36	2.0 (3.0)

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 'RACE'

P value = 0.253 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	44	415
subtype1	1	5	16	114
subtype2	0	1	16	112
subtype3	1	4	9	155
subtype4	0	1	3	34

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.673 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	431
subtype1	10	125
subtype2	5	112
subtype3	8	157
subtype4	1	37

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S15. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	85	76	91	87	68	70	17

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.224 (logrank test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	420	116	2.0 - 5480.0 (717.5)
subtype1	74	24	4.0 - 1984.0 (895.5)
subtype2	66	14	37.0 - 2886.0 (721.0)
subtype3	74	20	3.0 - 5480.0 (658.0)
subtype4	72	24	6.0 - 3295.0 (617.5)
subtype5	64	11	10.0 - 2878.0 (643.0)
subtype6	54	15	2.0 - 3835.0 (825.0)
subtype7	16	8	56.0 - 1970.0 (1223.5)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 2.48e-08 (Kruskal-Wallis (anova)), Q value = 3.1e-06

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	493	60.8 (12.0)
subtype1	84	59.5 (12.5)
subtype2	76	58.5 (10.0)
subtype3	91	56.8 (13.5)
subtype4	87	63.9 (10.7)
subtype5	68	58.1 (9.9)
subtype6	70	67.2 (11.0)
subtype7	17	67.8 (11.5)

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.692 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	26	76	74	240	12	1
subtype1	9	14	12	44	0	0
subtype2	3	14	11	38	3	1
subtype3	6	14	18	42	2	0
subtype4	2	8	12	50	3	0
subtype5	2	8	10	20	1	0
subtype6	3	15	8	37	3	0
subtype7	1	3	3	9	0	0

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0168 (Fisher's exact test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	47	130	95	164
subtype1	15	26	14	25
subtype2	7	17	16	31
subtype3	8	27	24	24
subtype4	4	18	17	37
subtype5	9	18	9	8
subtype6	3	19	12	32
subtype7	1	5	3	7

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0535 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	171	63	156	8
subtype1	32	13	31	0
subtype2	39	8	20	1
subtype3	31	10	32	1
subtype4	21	9	36	3
subtype5	13	9	16	1
subtype6	31	10	14	2
subtype7	4	4	7	0

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0445 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	168	1	58
subtype1	44	0	8
subtype2	28	1	9
subtype3	33	0	9
subtype4	19	0	9
subtype5	16	0	14
subtype6	21	0	9
subtype7	7	0	0

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 5e-05 (Fisher's exact test), Q value = 0.0061

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	134	360
subtype1	28	57
subtype2	13	63
subtype3	29	62
subtype4	18	69
subtype5	9	59
subtype6	34	36
subtype7	3	14

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.015 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	HEAD AND NECK SQUAMOUS CELL CARCINOMA	HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT	HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE
ALL	483	1	10
subtype1	85	0	0
subtype2	73	0	3
subtype3	90	1	0
subtype4	85	0	2
subtype5	63	0	5
subtype6	70	0	0
subtype7	17	0	0

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.515 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	81	413
subtype1	15	70
subtype2	10	66
subtype3	14	77
subtype4	19	68
subtype5	13	55
subtype6	7	63
subtype7	3	14

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0043 (Kruskal-Wallis (anova)), Q value = 0.48

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	277	45.7 (35.6)
subtype1	52	46.4 (44.5)
subtype2	52	50.5 (26.9)
subtype3	38	35.0 (19.0)
subtype4	60	52.3 (30.8)
subtype5	32	38.8 (38.0)
subtype6	28	50.0 (54.5)
subtype7	15	33.6 (19.3)

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

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

P value = 0.000106 (Kruskal-Wallis (anova)), Q value = 0.013

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	387	2.2 (4.4)
subtype1	69	2.3 (3.4)
subtype2	65	1.1 (2.2)
subtype3	77	1.8 (2.6)
subtype4	69	4.4 (8.3)
subtype5	33	2.0 (2.2)
subtype6	57	1.3 (2.2)
subtype7	17	2.0 (2.5)

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0294 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	44	421
subtype1	1	4	5	70
subtype2	0	0	12	60
subtype3	1	1	4	81
subtype4	0	2	13	71
subtype5	0	0	3	65
subtype6	0	4	5	60
subtype7	0	0	2	14

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.732 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	437
subtype1	3	76
subtype2	2	63
subtype3	8	79
subtype4	4	77
subtype5	3	60
subtype6	3	66
subtype7	1	16

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

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S29. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	55	52	37	68

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.628 (logrank test), Q value = 1

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	169	77	45.0 - 3837.0 (1207.0)
subtype1	42	21	45.0 - 3837.0 (1193.5)
subtype2	45	17	63.0 - 3295.0 (1110.0)
subtype3	30	14	113.0 - 2327.0 (1752.5)
subtype4	52	25	46.0 - 2641.0 (996.5)

Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.255 (Kruskal-Wallis (anova)), Q value = 1

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	212	62.1 (12.2)
subtype1	55	60.4 (12.3)
subtype2	52	62.7 (9.1)
subtype3	37	62.6 (14.6)
subtype4	68	62.8 (12.9)

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.519 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	9	39	31	117	4
subtype1	1	14	9	28	1
subtype2	2	4	7	33	1
subtype3	3	8	6	16	1
subtype4	3	13	9	40	1

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.168 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	13	59	53	79
subtype1	3	19	12	19
subtype2	3	6	14	25
subtype3	3	13	6	13
subtype4	4	21	21	22

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.357 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	72	21	79	4
subtype1	20	5	19	1
subtype2	15	3	25	1
subtype3	17	5	8	1
subtype4	20	8	27	1

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.00716 (Fisher's exact test), Q value = 0.43

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	61	151
subtype1	21	34
subtype2	20	32
subtype3	4	33
subtype4	16	52

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.208 (Kruskal-Wallis (anova)), Q value = 0.76

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	170	2.9 (5.1)
subtype1	42	2.9 (4.2)
subtype2	44	4.5 (8.3)
subtype3	30	1.2 (1.7)
subtype4	54	2.4 (2.8)

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.111 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	19	182
subtype1	1	0	8	44
subtype2	0	1	7	43
subtype3	0	0	1	35
subtype4	0	2	3	60

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.739 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	190
subtype1	5	47
subtype2	2	46
subtype3	2	35
subtype4	4	62

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S39. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	78	85	49

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.148 (logrank test), Q value = 1

Table S40. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	169	77	45.0 - 3837.0 (1207.0)
subtype1	62	27	45.0 - 3837.0 (1312.0)
subtype2	67	36	46.0 - 3295.0 (1151.0)
subtype3	40	14	77.0 - 2641.0 (1137.0)

Figure S36. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0759 (Kruskal-Wallis (anova)), Q value = 1

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	212	62.1 (12.2)
subtype1	78	61.2 (13.2)
subtype2	85	64.3 (11.8)
subtype3	49	59.9 (10.8)

Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0292 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	9	39	31	117	4
subtype1	3	18	12	41	1
subtype2	2	11	7	57	2
subtype3	4	10	12	19	1

Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.192 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	13	59	53	79
subtype1	5	26	12	33
subtype2	4	19	27	31
subtype3	4	14	14	15

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	72	21	79	4
subtype1	29	9	26	1
subtype2	15	5	45	2
subtype3	28	7	8	1

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.439 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	61	151
subtype1	22	56
subtype2	28	57
subtype3	11	38

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 4.76e-06 (Kruskal-Wallis (anova)), Q value = 1

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	170	2.9 (5.1)
subtype1	62	2.5 (3.8)
subtype2	70	4.4 (6.8)
subtype3	38	0.7 (1.3)

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.415 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	19	182
subtype1	1	0	10	64
subtype2	0	2	6	74
subtype3	0	1	3	44

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.542 (Fisher's exact test), Q value = 6e-04

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	190
subtype1	7	69
subtype2	4	75
subtype3	2	46

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S49. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	189	134	142

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.526 (logrank test), Q value = 1

Table S50. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	395	113	2.0 - 5480.0 (775.0)
subtype1	165	43	10.0 - 3835.0 (799.0)
subtype2	115	40	5.0 - 5480.0 (759.0)
subtype3	115	30	2.0 - 3837.0 (758.0)

Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.322 (Kruskal-Wallis (anova)), Q value = 1

Table S51. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	464	60.7 (12.1)
subtype1	189	61.3 (10.5)
subtype2	133	59.4 (12.7)
subtype3	142	61.1 (13.4)

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.184 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	24	73	71	223	11
subtype1	3	25	29	84	5
subtype2	14	23	18	67	3
subtype3	7	25	24	72	3

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.299 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	44	121	91	153
subtype1	15	41	34	61
subtype2	18	44	27	37
subtype3	11	36	30	55

Figure S48. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.22 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	161	58	146	7
subtype1	59	21	51	4
subtype2	43	17	57	2
subtype3	59	20	38	1

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.318 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	153	50
subtype1	56	24
subtype2	48	11
subtype3	49	15

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00868 (Fisher's exact test), Q value = 0.9

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	125	340
subtype1	39	150
subtype2	35	99
subtype3	51	91

Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00128 (Fisher's exact test), Q value = 0.15

Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	HEAD AND NECK SQUAMOUS CELL CARCINOMA	HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT	HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE
ALL	456	1	8
subtype1	181	0	8
subtype2	133	1	0
subtype3	142	0	0

Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.252 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	81	384
subtype1	32	157
subtype2	29	105
subtype3	20	122

Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0224 (Kruskal-Wallis (anova)), Q value = 1

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	263	46.1 (36.1)
subtype1	120	51.9 (39.2)
subtype2	69	43.4 (41.6)
subtype3	74	39.1 (21.2)

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

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

P value = 0.0326 (Kruskal-Wallis (anova)), Q value = 1

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	365	2.2 (4.5)
subtype1	124	2.3 (5.1)
subtype2	121	2.7 (4.9)
subtype3	120	1.7 (2.9)

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.493 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	34	402
subtype1	1	2	17	164
subtype2	1	4	7	114
subtype3	0	5	10	124

Figure S56. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

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

Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	23	410
subtype1	8	161
subtype2	10	117
subtype3	5	132

Figure S57. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S63. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	48	156	143	118

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.606 (logrank test), Q value = 1

Table S64. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	395	113	2.0 - 5480.0 (775.0)
subtype1	45	9	156.0 - 2886.0 (817.0)
subtype2	133	46	4.0 - 5480.0 (908.0)
subtype3	113	29	2.0 - 3837.0 (685.0)
subtype4	104	29	10.0 - 2878.0 (879.5)

Figure S58. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00402 (Kruskal-Wallis (anova)), Q value = 0.45

Table S65. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	464	60.7 (12.1)
subtype1	48	57.4 (8.6)
subtype2	155	58.8 (13.3)
subtype3	143	62.9 (12.3)
subtype4	118	61.9 (10.8)

Figure S59. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.61 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	24	73	71	223	11
subtype1	0	7	10	24	1
subtype2	15	25	25	79	3
subtype3	6	25	20	73	4
subtype4	3	16	16	47	3

Figure S60. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0139 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	44	121	91	153
subtype1	2	10	11	20
subtype2	23	47	36	42
subtype3	9	34	22	64
subtype4	10	30	22	27

Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00392 (Fisher's exact test), Q value = 0.44

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	161	58	146	7
subtype1	23	5	12	1
subtype2	50	21	66	2
subtype3	65	18	32	1
subtype4	23	14	36	3

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.128 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	153	50
subtype1	13	6
subtype2	58	11
subtype3	53	18
subtype4	29	15

Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.002 (Fisher's exact test), Q value = 0.23

Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	125	340
subtype1	7	41
subtype2	46	110
subtype3	51	92
subtype4	21	97

Figure S64. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00657 (Fisher's exact test), Q value = 0.7

Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	HEAD AND NECK SQUAMOUS CELL CARCINOMA	HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT	HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE
ALL	456	1	8
subtype1	47	0	1
subtype2	155	0	1
subtype3	143	0	0
subtype4	111	1	6

Figure S65. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0219 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	81	384
subtype1	6	42
subtype2	34	122
subtype3	15	128
subtype4	26	92

Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0443 (Kruskal-Wallis (anova)), Q value = 1

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	263	46.1 (36.1)
subtype1	35	52.0 (29.8)
subtype2	78	41.7 (39.8)
subtype3	77	44.0 (36.3)
subtype4	73	50.0 (34.3)

Figure S67. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

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

P value = 3.67e-05 (Kruskal-Wallis (anova)), Q value = 0.0045

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	365	2.2 (4.5)
subtype1	40	0.9 (1.6)
subtype2	138	2.5 (4.5)
subtype3	116	1.7 (3.3)
subtype4	71	3.2 (6.5)

Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.124 (Fisher's exact test), Q value = 1

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	34	402
subtype1	0	0	7	39
subtype2	1	8	8	131
subtype3	1	2	9	126
subtype4	0	1	10	106

Figure S69. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.778 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	23	410
subtype1	1	41
subtype2	10	138
subtype3	7	126
subtype4	5	105

Figure S70. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S77. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	205	142	145

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.503 (logrank test), Q value = 1

Table S78. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	418	115	2.0 - 5480.0 (717.5)
subtype1	173	46	10.0 - 2878.0 (735.0)
subtype2	122	24	2.0 - 2886.0 (547.0)
subtype3	123	45	14.0 - 5480.0 (985.0)

Figure S71. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.075 (Kruskal-Wallis (anova)), Q value = 1

Table S79. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	491	60.8 (12.0)
subtype1	205	61.8 (10.7)
subtype2	142	61.8 (11.7)
subtype3	144	58.5 (13.6)

Figure S72. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.438 (Fisher's exact test), Q value = 1

Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	26	75	73	240	12	1
subtype1	6	27	25	105	7	1
subtype2	9	23	25	63	2	0
subtype3	11	25	23	72	3	0

Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.166 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	47	129	94	164
subtype1	14	43	39	77
subtype2	15	38	28	45
subtype3	18	48	27	42

Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0641 (Fisher's exact test), Q value = 1

Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	169	63	156	8
subtype1	74	19	63	5
subtype2	54	21	36	2
subtype3	41	23	57	1

Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.315 (Fisher's exact test), Q value = 1

Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	167	1	58
subtype1	63	1	27
subtype2	58	0	21
subtype3	46	0	10

Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0807 (Fisher's exact test), Q value = 1

Table S84. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	134	358
subtype1	45	160
subtype2	43	99
subtype3	46	99

Figure S77. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00819 (Fisher's exact test), Q value = 0.86

Table S85. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	HEAD AND NECK SQUAMOUS CELL CARCINOMA	HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT	HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE
ALL	481	1	10
subtype1	195	1	9
subtype2	142	0	0
subtype3	144	0	1

Figure S78. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0951 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	412
subtype1	32	173
subtype2	17	125
subtype3	31	114

Figure S79. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.336 (Kruskal-Wallis (anova)), Q value = 1

Table S87. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	276	45.7 (35.7)
subtype1	122	47.4 (31.5)
subtype2	85	44.8 (35.8)
subtype3	69	43.9 (42.2)

Figure S80. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

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

P value = 0.0197 (Kruskal-Wallis (anova)), Q value = 1

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	385	2.2 (4.4)
subtype1	151	2.3 (5.1)
subtype2	111	1.6 (2.6)
subtype3	123	2.6 (4.7)

Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.0236 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	43	420
subtype1	1	1	25	175
subtype2	0	7	10	120
subtype3	1	3	8	125

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.388 (Fisher's exact test), Q value = 1

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	435
subtype1	7	179
subtype2	7	127
subtype3	10	129

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S91. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	189	131	172

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0743 (logrank test), Q value = 1

Table S92. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	418	115	2.0 - 5480.0 (717.5)
subtype1	158	51	4.0 - 5480.0 (772.0)
subtype2	108	29	11.0 - 2886.0 (600.5)
subtype3	152	35	2.0 - 3837.0 (774.0)

Figure S84. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0811 (Kruskal-Wallis (anova)), Q value = 1

Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	491	60.8 (12.0)
subtype1	188	59.4 (13.5)
subtype2	131	62.7 (11.0)
subtype3	172	60.9 (10.8)

Figure S85. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.16 (Fisher's exact test), Q value = 1

Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	26	75	73	240	12	1
subtype1	18	31	30	92	5	0
subtype2	3	18	18	69	5	1
subtype3	5	26	25	79	2	0

Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0165 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	47	129	94	164
subtype1	26	56	41	54
subtype2	5	29	25	57
subtype3	16	44	28	53

Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0445 (Fisher's exact test), Q value = 1

Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	169	63	156	8
subtype1	64	25	72	3
subtype2	58	12	34	4
subtype3	47	26	50	1

Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.203 (Fisher's exact test), Q value = 1

Table S97. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	167	1	58
subtype1	72	0	19
subtype2	41	1	14
subtype3	54	0	25

Figure S89. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S98. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	134	358
subtype1	56	133
subtype2	44	87
subtype3	34	138

Figure S90. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.217 (Fisher's exact test), Q value = 1

Table S99. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	HEAD AND NECK SQUAMOUS CELL CARCINOMA	HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT	HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE
ALL	481	1	10
subtype1	187	0	2
subtype2	128	1	2
subtype3	166	0	6

Figure S91. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.123 (Fisher's exact test), Q value = 1

Table S100. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	412
subtype1	35	154
subtype2	14	117
subtype3	31	141

Figure S92. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.119 (Kruskal-Wallis (anova)), Q value = 1

Table S101. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	276	45.7 (35.7)
subtype1	91	42.7 (37.5)
subtype2	73	49.8 (29.5)
subtype3	112	45.5 (37.8)

Figure S93. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

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

P value = 0.0018 (Kruskal-Wallis (anova)), Q value = 0.21

Table S102. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	385	2.2 (4.4)
subtype1	164	2.4 (4.3)
subtype2	108	1.6 (3.7)
subtype3	113	2.5 (5.0)

Figure S94. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0748 (Fisher's exact test), Q value = 1

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	43	420
subtype1	1	8	12	159
subtype2	0	2	17	109
subtype3	1	1	14	152

Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.434 (Fisher's exact test), Q value = 1

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	435
subtype1	12	166
subtype2	4	118
subtype3	8	151

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S105. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	72	123	91	116	45

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

P value = 0.338 (logrank test), Q value = 1

Table S106. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	377	99	2.0 - 5480.0 (687.0)
subtype1	60	21	25.0 - 3295.0 (551.5)
subtype2	102	17	3.0 - 5480.0 (481.0)
subtype3	80	14	10.0 - 2641.0 (666.0)
subtype4	96	34	2.0 - 3837.0 (1185.0)
subtype5	39	13	112.0 - 1977.0 (641.0)

Figure S97. 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 = 0.851 (Kruskal-Wallis (anova)), Q value = 1

Table S107. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	446	60.9 (11.8)
subtype1	71	61.9 (9.7)
subtype2	123	60.8 (12.8)
subtype3	91	61.4 (10.1)
subtype4	116	60.4 (13.3)
subtype5	45	59.5 (11.8)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.134 (Fisher's exact test), Q value = 1

Table S108. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	24	71	68	214	10	1
subtype1	2	13	9	43	3	0
subtype2	6	20	24	55	2	0
subtype3	2	7	14	43	3	1
subtype4	11	20	18	58	1	0
subtype5	3	11	3	15	1	0

Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0351 (Fisher's exact test), Q value = 1

Table S109. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	44	117	85	147
subtype1	4	21	13	32
subtype2	11	36	25	37
subtype3	5	17	21	28
subtype4	13	31	21	43
subtype5	11	12	5	7

Figure S100. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.588 (Fisher's exact test), Q value = 1

Table S110. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	154	60	140	6
subtype1	28	10	27	1
subtype2	45	20	34	1
subtype3	26	11	28	3
subtype4	41	17	36	0
subtype5	14	2	15	1

Figure S101. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0127 (Fisher's exact test), Q value = 1

Table S111. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	154	1	57
subtype1	20	0	9
subtype2	64	0	30
subtype3	27	1	12
subtype4	29	0	1
subtype5	14	0	5

Figure S102. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.124 (Fisher's exact test), Q value = 1

Table S112. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	123	324
subtype1	20	52
subtype2	43	80
subtype3	17	74
subtype4	32	84
subtype5	11	34

Figure S103. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.00037 (Fisher's exact test), Q value = 0.044

Table S113. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	HEAD AND NECK SQUAMOUS CELL CARCINOMA	HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT	HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE
ALL	436	1	10
subtype1	71	0	1
subtype2	122	1	0
subtype3	84	0	7
subtype4	116	0	0
subtype5	43	0	2

Figure S104. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.00631 (Fisher's exact test), Q value = 0.68

Table S114. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	61	386
subtype1	8	64
subtype2	7	116
subtype3	13	78
subtype4	24	92
subtype5	9	36

Figure S105. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0052 (Kruskal-Wallis (anova)), Q value = 0.57

Table S115. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	243	45.9 (36.8)
subtype1	38	43.1 (29.9)
subtype2	66	34.9 (19.5)
subtype3	58	55.1 (36.6)
subtype4	60	51.0 (41.9)
subtype5	21	45.6 (61.4)

Figure S106. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

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

P value = 0.305 (Kruskal-Wallis (anova)), Q value = 1

Table S116. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	351	2.2 (4.4)
subtype1	60	3.5 (8.0)
subtype2	104	1.5 (2.7)
subtype3	65	2.4 (4.4)
subtype4	94	1.9 (3.0)
subtype5	28	1.8 (2.2)

Figure S107. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.00501 (Fisher's exact test), Q value = 0.55

Table S117. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	40	378
subtype1	0	0	9	59
subtype2	1	8	4	107
subtype3	0	1	13	75
subtype4	0	2	8	100
subtype5	1	0	6	37

Figure S108. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.912 (Fisher's exact test), Q value = 1

Table S118. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	23	392
subtype1	3	62
subtype2	7	109
subtype3	3	77
subtype4	7	105
subtype5	3	39

Figure S109. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S119. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	141	227	79

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

P value = 0.918 (logrank test), Q value = 1

Table S120. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	377	99	2.0 - 5480.0 (687.0)
subtype1	117	41	25.0 - 3837.0 (913.0)
subtype2	200	47	2.0 - 3835.0 (652.0)
subtype3	60	11	4.0 - 5480.0 (484.5)

Figure S110. 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 = 0.636 (Kruskal-Wallis (anova)), Q value = 1

Table S121. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	446	60.9 (11.8)
subtype1	141	60.3 (13.2)
subtype2	227	61.6 (10.2)
subtype3	78	59.7 (13.6)

Figure S111. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

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

Table S122. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	24	71	68	214	10	1
subtype1	14	22	22	72	3	0
subtype2	7	33	31	108	4	1
subtype3	3	16	15	34	3	0

Figure S112. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.568 (Fisher's exact test), Q value = 1

Table S123. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	44	117	85	147
subtype1	18	41	31	43
subtype2	19	52	37	80
subtype3	7	24	17	24

Figure S113. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.797 (Fisher's exact test), Q value = 1

Table S124. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	154	60	140	6
subtype1	47	21	54	1
subtype2	78	29	62	4
subtype3	29	10	24	1

Figure S114. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.494 (Fisher's exact test), Q value = 1

Table S125. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	154	1	57
subtype1	42	0	10
subtype2	70	1	30
subtype3	42	0	17

Figure S115. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.461 (Fisher's exact test), Q value = 1

Table S126. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	123	324
subtype1	44	97
subtype2	60	167
subtype3	19	60

Figure S116. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0955 (Fisher's exact test), Q value = 1

Table S127. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	HEAD AND NECK SQUAMOUS CELL CARCINOMA	HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT	HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE
ALL	436	1	10
subtype1	139	0	2
subtype2	219	0	8
subtype3	78	1	0

Figure S117. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0011 (Fisher's exact test), Q value = 0.13

Table S128. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	61	386
subtype1	26	115
subtype2	33	194
subtype3	2	77

Figure S118. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.205 (Kruskal-Wallis (anova)), Q value = 1

Table S129. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	243	45.9 (36.8)
subtype1	64	48.2 (43.5)
subtype2	138	47.4 (36.9)
subtype3	41	37.3 (21.6)

Figure S119. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

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

P value = 0.168 (Kruskal-Wallis (anova)), Q value = 1

Table S130. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	351	2.2 (4.4)
subtype1	120	2.4 (4.4)
subtype2	165	2.0 (4.6)
subtype3	66	2.1 (3.7)

Figure S120. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0779 (Fisher's exact test), Q value = 1

Table S131. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	40	378
subtype1	0	2	9	124
subtype2	1	4	26	191
subtype3	1	5	5	63

Figure S121. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.681 (Fisher's exact test), Q value = 1

Table S132. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	23	392
subtype1	8	127
subtype2	10	198
subtype3	5	67

Figure S122. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Methods & Data

Input

Cluster data file = HNSC-TP.mergedcluster.txt
Clinical data file = HNSC-TP.merged_data.txt
Number of patients = 494
Number of clustering approaches = 10
Number of selected clinical features = 13
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

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

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

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

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