Correlation between aggregated molecular cancer subtypes and selected clinical features

Head and Neck Squamous Cell Carcinoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1T72GMJ

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 14 clinical features across 528 patients, 52 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 'YEARS_TO_BIRTH', 'PATHOLOGY_N_STAGE', 'NUMBER_PACK_YEARS_SMOKED', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.
7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY_N_STAGE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', and 'NUMBER_OF_LYMPH_NODES'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'GENDER', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', 'NUMBER_PACK_YEARS_SMOKED', and 'NUMBER_OF_LYMPH_NODES'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'GENDER', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', 'NUMBER_PACK_YEARS_SMOKED', and 'NUMBER_OF_LYMPH_NODES'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'GENDER', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'PATHOLOGY_T_STAGE', 'GENDER', 'RADIATION_THERAPY', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE', 'GENDER', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'RADIATION_THERAPY'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 52 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	0.0973 (0.214)	0.0224 (0.0872)	0.447 (0.568)	0.168 (0.297)	0.167 (0.297)	0.00605 (0.0339)	0.338 (0.465)	0.0423 (0.118)	0.193 (0.323)	0.129 (0.249)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.0301 (0.0974)	3.38e-10 (4.74e-08)	0.957 (1.00)	0.0759 (0.185)	0.278 (0.414)	0.00439 (0.0267)	0.0645 (0.164)	0.0733 (0.183)	0.779 (0.854)	0.675 (0.781)
PATHOLOGIC STAGE	Fisher's exact test	0.421 (0.551)	0.742 (0.845)	0.288 (0.425)	0.0306 (0.0974)	0.197 (0.323)	0.1 (0.214)	0.318 (0.45)	0.0602 (0.156)	0.0766 (0.185)	0.501 (0.62)
PATHOLOGY T STAGE	Fisher's exact test	0.167 (0.297)	0.00522 (0.0304)	0.654 (0.763)	0.194 (0.323)	0.142 (0.269)	5e-05 (0.00117)	0.155 (0.286)	0.00153 (0.0134)	0.0431 (0.118)	0.785 (0.854)
PATHOLOGY N STAGE	Fisher's exact test	0.0431 (0.118)	0.0291 (0.0974)	0.00143 (0.0133)	2e-05 (7e-04)	0.313 (0.447)	0.124 (0.249)	0.0523 (0.138)	0.0856 (0.199)	0.469 (0.592)	0.916 (0.979)
PATHOLOGY M STAGE	Fisher's exact test	0.613 (0.721)	0.559 (0.671)			1 (1.00)	0.258 (0.393)	1 (1.00)	0.276 (0.414)	0.561 (0.671)	1 (1.00)
GENDER	Fisher's exact test	0.0983 (0.214)	2e-05 (7e-04)	0.19 (0.323)	0.219 (0.352)	0.00337 (0.0236)	8e-05 (0.0016)	0.0361 (0.108)	0.011 (0.0512)	0.0329 (0.103)	0.497 (0.62)
RADIATION THERAPY	Fisher's exact test	0.393 (0.525)	0.0868 (0.199)	0.231 (0.364)	0.443 (0.568)	0.00303 (0.023)	0.00712 (0.0383)	0.03 (0.0974)	0.0263 (0.0974)	0.00044 (0.0056)	0.00312 (0.023)
HISTOLOGICAL TYPE	Fisher's exact test	0.107 (0.224)	0.0214 (0.0857)			0.00023 (0.00358)	0.00029 (0.00406)	0.00884 (0.0442)	0.237 (0.369)	0.0007 (0.00817)	0.0928 (0.21)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	9.87e-05 (0.00173)	0.0146 (0.066)	0.719 (0.826)	0.586 (0.695)	0.0443 (0.119)	0.00186 (0.0153)	0.787 (0.854)	0.182 (0.315)	0.00805 (0.0417)	0.127 (0.249)
YEAR OF TOBACCO SMOKING ONSET	Kruskal-Wallis (anova)	0.0012 (0.012)	0.00115 (0.012)	0.752 (0.845)	0.223 (0.355)	0.151 (0.283)	0.199 (0.323)	0.13 (0.249)	0.0804 (0.191)	0.0167 (0.0729)	0.33 (0.462)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.297 (0.429)	2.74e-05 (0.000767)	0.00434 (0.0267)	4.76e-06 (0.000333)	0.0347 (0.106)	0.0102 (0.0491)	0.0304 (0.0974)	0.00425 (0.0267)	0.35 (0.476)	0.339 (0.465)
RACE	Fisher's exact test	0.252 (0.388)	0.0186 (0.0791)	0.0421 (0.118)	0.43 (0.558)	0.124 (0.249)	0.125 (0.249)	0.0198 (0.0814)	0.0277 (0.0974)	0.0283 (0.0974)	0.101 (0.214)
ETHNICITY	Fisher's exact test	0.555 (0.671)	0.755 (0.845)	0.172 (0.301)	0.541 (0.665)	0.291 (0.425)	0.775 (0.854)	0.416 (0.549)	0.378 (0.509)	0.969 (1.00)	0.885 (0.954)

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	155	135	189	43

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

P value = 0.0973 (logrank test), Q value = 0.21

Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	521	218	0.1 - 211.0 (20.9)
subtype1	155	73	0.1 - 180.2 (19.0)
subtype2	135	57	0.1 - 153.9 (24.9)
subtype3	188	69	0.1 - 211.0 (21.2)
subtype4	43	19	2.7 - 87.5 (17.9)

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 'YEARS_TO_BIRTH'

P value = 0.0301 (Kruskal-Wallis (anova)), Q value = 0.097

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

	nPatients	Mean (Std.Dev)
ALL	521	60.9 (11.9)
subtype1	155	58.5 (13.0)
subtype2	135	62.3 (9.7)
subtype3	188	61.1 (12.4)
subtype4	43	64.2 (10.9)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.421 (Fisher's exact test), Q value = 0.55

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	27	76	82	253	12	1
subtype1	7	20	24	84	5	1
subtype2	6	16	19	71	4	0
subtype3	12	34	30	78	1	0
subtype4	2	6	9	20	2	0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.167 (Fisher's exact test), Q value = 0.3

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

nPatients	T0+T1	T2	T3	T4
ALL	50	137	100	173
subtype1	12	40	33	59
subtype2	10	30	30	49
subtype3	25	54	26	53
subtype4	3	13	11	12

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 = 0.0431 (Fisher's exact test), Q value = 0.12

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

nPatients	N0	N1	N2	N3
ALL	179	68	168	8
subtype1	51	20	59	2
subtype2	44	13	49	3
subtype3	73	25	45	1
subtype4	11	10	15	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 = 0.613 (Fisher's exact test), Q value = 0.72

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

nPatients	0	1
ALL	189	1
subtype1	55	1
subtype2	39	0
subtype3	74	0
subtype4	21	0

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 = 0.0983 (Fisher's exact test), Q value = 0.21

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

nPatients	FEMALE	MALE
ALL	140	382
subtype1	40	115
subtype2	27	108
subtype3	58	131
subtype4	15	28

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 'RADIATION_THERAPY'

P value = 0.393 (Fisher's exact test), Q value = 0.52

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

nPatients	NO	YES
ALL	151	283
subtype1	45	79
subtype2	31	79
subtype3	61	100
subtype4	14	25

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.107 (Fisher's exact test), Q value = 0.22

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: '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	511	1	10
subtype1	155	0	0
subtype2	131	0	4
subtype3	182	1	6
subtype4	43	0	0

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 9.87e-05 (Kruskal-Wallis (anova)), Q value = 0.0017

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

	nPatients	Mean (Std.Dev)
ALL	293	45.9 (35.3)
subtype1	77	41.1 (22.9)
subtype2	91	55.9 (33.3)
subtype3	100	43.4 (44.6)
subtype4	25	33.9 (23.3)

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

'Copy Number Ratio CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0012 (Kruskal-Wallis (anova)), Q value = 0.012

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	277	1967.4 (12.8)
subtype1	73	1971.1 (11.5)
subtype2	86	1963.9 (12.1)
subtype3	96	1966.6 (14.0)
subtype4	22	1971.9 (10.0)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.297 (Kruskal-Wallis (anova)), Q value = 0.43

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

	nPatients	Mean (Std.Dev)
ALL	408	2.2 (4.3)
subtype1	130	2.2 (3.8)
subtype2	109	2.8 (6.3)
subtype3	129	1.7 (2.6)
subtype4	40	2.1 (2.9)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.252 (Fisher's exact test), Q value = 0.39

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	48	446
subtype1	1	5	17	126
subtype2	0	1	17	114
subtype3	1	4	10	169
subtype4	0	1	4	37

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.555 (Fisher's exact test), Q value = 0.67

Table S15. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	26	459
subtype1	11	134
subtype2	5	116
subtype3	9	168
subtype4	1	41

Figure S14. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3	4	5	6	7
Number of samples	87	79	96	93	78	76	19

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0224 (logrank test), Q value = 0.087

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	527	222	0.1 - 211.0 (21.1)
subtype1	86	37	2.3 - 211.0 (17.8)
subtype2	79	30	0.1 - 153.9 (24.9)
subtype3	96	43	1.8 - 180.2 (19.7)
subtype4	93	46	0.5 - 139.4 (20.5)
subtype5	78	19	0.1 - 105.9 (23.5)
subtype6	76	36	0.1 - 169.4 (19.5)
subtype7	19	11	1.8 - 84.5 (16.7)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 3.38e-10 (Kruskal-Wallis (anova)), Q value = 4.7e-08

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

	nPatients	Mean (Std.Dev)
ALL	527	60.9 (11.9)
subtype1	86	59.6 (12.4)
subtype2	79	58.8 (9.9)
subtype3	96	56.9 (13.4)
subtype4	93	63.9 (10.6)
subtype5	78	57.6 (9.6)
subtype6	76	67.7 (10.8)
subtype7	19	67.7 (11.9)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.742 (Fisher's exact test), Q value = 0.84

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	27	77	82	258	12	1
subtype1	9	14	13	45	0	0
subtype2	3	14	12	40	3	1
subtype3	6	14	19	46	2	0
subtype4	2	8	13	53	3	0
subtype5	2	9	11	26	1	0
subtype6	3	15	11	39	3	0
subtype7	2	3	3	9	0	0

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00522 (Fisher's exact test), Q value = 0.03

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

nPatients	T0+T1	T2	T3	T4
ALL	50	140	101	175
subtype1	15	27	14	26
subtype2	8	17	16	33
subtype3	8	28	24	28
subtype4	4	20	19	38
subtype5	10	23	11	8
subtype6	3	20	13	35
subtype7	2	5	4	7

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0291 (Fisher's exact test), Q value = 0.097

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	180	68	172	8
subtype1	33	14	31	0
subtype2	40	10	20	1
subtype3	31	11	36	1
subtype4	23	9	39	3
subtype5	14	9	23	1
subtype6	34	11	15	2
subtype7	5	4	8	0

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

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.559 (Fisher's exact test), Q value = 0.67

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

nPatients	0	1
ALL	190	1
subtype1	45	0
subtype2	30	1
subtype3	39	0
subtype4	21	0
subtype5	21	0
subtype6	25	0
subtype7	9	0

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

'METHLYATION CNMF' versus 'GENDER'

P value = 2e-05 (Fisher's exact test), Q value = 7e-04

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

nPatients	FEMALE	MALE
ALL	142	386
subtype1	29	58
subtype2	13	66
subtype3	30	66
subtype4	19	74
subtype5	9	69
subtype6	38	38
subtype7	4	15

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.0868 (Fisher's exact test), Q value = 0.2

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

nPatients	NO	YES
ALL	152	285
subtype1	32	43
subtype2	24	44
subtype3	28	51
subtype4	19	45
subtype5	15	54
subtype6	25	40
subtype7	9	8

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0214 (Fisher's exact test), Q value = 0.086

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: '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	517	1	10
subtype1	87	0	0
subtype2	76	0	3
subtype3	95	1	0
subtype4	91	0	2
subtype5	73	0	5
subtype6	76	0	0
subtype7	19	0	0

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

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0146 (Kruskal-Wallis (anova)), Q value = 0.066

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	298	45.8 (35.2)
subtype1	54	46.3 (43.9)
subtype2	52	50.5 (26.9)
subtype3	39	37.1 (23.2)
subtype4	65	51.1 (30.1)
subtype5	42	39.8 (37.0)
subtype6	31	46.9 (52.6)
subtype7	15	41.1 (16.9)

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

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.00115 (Kruskal-Wallis (anova)), Q value = 0.012

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	282	1967.3 (12.7)
subtype1	52	1968.1 (13.5)
subtype2	45	1964.8 (10.7)
subtype3	44	1972.9 (12.1)
subtype4	61	1967.9 (13.1)
subtype5	40	1968.8 (12.7)
subtype6	28	1960.8 (11.4)
subtype7	12	1959.9 (10.3)

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

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 2.74e-05 (Kruskal-Wallis (anova)), Q value = 0.00077

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	413	2.2 (4.3)
subtype1	70	2.3 (3.4)
subtype2	68	1.0 (2.2)
subtype3	82	1.8 (2.6)
subtype4	73	4.4 (8.1)
subtype5	40	2.2 (2.8)
subtype6	61	1.3 (2.1)
subtype7	19	1.9 (2.4)

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0186 (Fisher's exact test), Q value = 0.079

Table S29. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	48	452
subtype1	1	4	6	71
subtype2	0	0	12	63
subtype3	1	1	5	85
subtype4	0	2	15	75
subtype5	0	0	3	75
subtype6	0	4	5	67
subtype7	0	0	2	16

Figure S27. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.755 (Fisher's exact test), Q value = 0.85

Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	26	465
subtype1	3	78
subtype2	2	66
subtype3	8	83
subtype4	5	81
subtype5	4	69
subtype6	3	70
subtype7	1	18

Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	67	64	81

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.447 (logrank test), Q value = 0.57

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	212	128	0.1 - 211.0 (23.1)
subtype1	67	45	2.5 - 140.8 (21.1)
subtype2	64	35	2.9 - 156.5 (24.6)
subtype3	81	48	0.1 - 211.0 (25.1)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.957 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	212	62.1 (12.2)
subtype1	67	62.6 (10.5)
subtype2	64	62.2 (13.6)
subtype3	81	61.6 (12.5)

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

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.288 (Fisher's exact test), Q value = 0.42

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	9	39	31	117	4
subtype1	1	11	7	42	1
subtype2	3	15	13	29	0
subtype3	5	13	11	46	3

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.654 (Fisher's exact test), Q value = 0.76

Table S35. 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	2	16	18	26
subtype2	4	21	12	24
subtype3	7	22	23	29

Figure S32. 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 = 0.00143 (Fisher's exact test), Q value = 0.013

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

nPatients	N0	N1	N2	N3
ALL	72	21	79	4
subtype1	15	5	33	1
subtype2	34	5	15	0
subtype3	23	11	31	3

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.19 (Fisher's exact test), Q value = 0.32

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	62	150
subtype1	14	53
subtype2	21	43
subtype3	27	54

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.231 (Fisher's exact test), Q value = 0.36

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	64	74
subtype1	17	30
subtype2	23	21
subtype3	24	23

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.719 (Kruskal-Wallis (anova)), Q value = 0.83

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	110	49.5 (37.5)
subtype1	34	46.9 (34.7)
subtype2	36	50.8 (48.1)
subtype3	40	50.5 (28.8)

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.752 (Kruskal-Wallis (anova)), Q value = 0.85

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	117	1964.9 (12.3)
subtype1	36	1964.8 (9.1)
subtype2	38	1966.1 (15.5)
subtype3	43	1963.8 (11.7)

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00434 (Kruskal-Wallis (anova)), Q value = 0.027

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	170	2.9 (5.1)
subtype1	56	3.9 (6.6)
subtype2	50	1.8 (3.6)
subtype3	64	2.8 (4.5)

Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0421 (Fisher's exact test), Q value = 0.12

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	19	185
subtype1	1	1	11	51
subtype2	0	0	4	60
subtype3	0	2	4	74

Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.172 (Fisher's exact test), Q value = 0.3

Table S43. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	190
subtype1	7	53
subtype2	2	61
subtype3	4	76

Figure S40. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S44. 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 = 0.168 (logrank test), Q value = 0.3

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	212	128	0.1 - 211.0 (23.1)
subtype1	78	46	2.5 - 156.5 (22.6)
subtype2	85	59	0.1 - 211.0 (21.5)
subtype3	49	23	2.1 - 111.2 (31.4)

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0759 (Kruskal-Wallis (anova)), Q value = 0.18

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	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 S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0306 (Fisher's exact test), Q value = 0.097

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

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 S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.194 (Fisher's exact test), Q value = 0.32

Table S48. 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 S44. 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 = 2e-05 (Fisher's exact test), Q value = 7e-04

Table S49. 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 S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.219 (Fisher's exact test), Q value = 0.35

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	62	150
subtype1	22	56
subtype2	21	64
subtype3	19	30

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.443 (Fisher's exact test), Q value = 0.57

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	64	74
subtype1	29	29
subtype2	20	31
subtype3	15	14

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.586 (Kruskal-Wallis (anova)), Q value = 0.7

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	110	49.5 (37.5)
subtype1	38	52.6 (48.6)
subtype2	45	52.0 (34.6)
subtype3	27	40.9 (20.5)

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.223 (Kruskal-Wallis (anova)), Q value = 0.36

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	117	1964.9 (12.3)
subtype1	43	1966.1 (14.7)
subtype2	50	1962.6 (11.2)
subtype3	24	1967.2 (9.4)

Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 4.76e-06 (Kruskal-Wallis (anova)), Q value = 0.00033

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: '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 S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.43 (Fisher's exact test), Q value = 0.56

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	19	185
subtype1	1	0	10	66
subtype2	0	2	6	74
subtype3	0	1	3	45

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.541 (Fisher's exact test), Q value = 0.66

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

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

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S57. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	144	214	162

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.167 (logrank test), Q value = 0.3

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	519	219	0.1 - 211.0 (21.1)
subtype1	143	71	0.5 - 180.2 (20.6)
subtype2	214	80	0.1 - 140.8 (22.3)
subtype3	162	68	0.1 - 211.0 (19.4)

Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.278 (Kruskal-Wallis (anova)), Q value = 0.41

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	519	60.9 (11.9)
subtype1	143	59.5 (12.5)
subtype2	214	61.4 (10.3)
subtype3	162	61.4 (13.2)

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.197 (Fisher's exact test), Q value = 0.32

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	27	74	81	254	12	1
subtype1	15	23	21	71	4	0
subtype2	4	25	31	99	5	1
subtype3	8	26	29	84	3	0

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.142 (Fisher's exact test), Q value = 0.27

Table S61. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	49	136	99	174
subtype1	19	49	27	41
subtype2	18	47	40	65
subtype3	12	40	32	68

Figure S56. 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 = 0.313 (Fisher's exact test), Q value = 0.45

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

nPatients	N0	N1	N2	N3
ALL	176	67	169	8
subtype1	47	19	59	3
subtype2	63	23	64	4
subtype3	66	25	46	1

Figure S57. 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 = 1 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	185	1
subtype1	53	0
subtype2	67	1
subtype3	65	0

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00337 (Fisher's exact test), Q value = 0.024

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	136	384
subtype1	37	107
subtype2	42	172
subtype3	57	105

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.00303 (Fisher's exact test), Q value = 0.023

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	150	279
subtype1	54	63
subtype2	48	130
subtype3	48	86

Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00023 (Fisher's exact test), Q value = 0.0036

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: '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	509	1	10
subtype1	143	1	0
subtype2	204	0	10
subtype3	162	0	0

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0443 (Kruskal-Wallis (anova)), Q value = 0.12

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	296	45.8 (35.3)
subtype1	79	43.6 (39.2)
subtype2	134	51.1 (38.6)
subtype3	83	39.5 (22.7)

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.151 (Kruskal-Wallis (anova)), Q value = 0.28

Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	281	1967.4 (12.7)
subtype1	82	1969.6 (12.7)
subtype2	121	1965.6 (12.0)
subtype3	78	1967.7 (13.5)

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0347 (Kruskal-Wallis (anova)), Q value = 0.11

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	405	2.2 (4.3)
subtype1	128	2.7 (4.8)
subtype2	139	2.3 (5.0)
subtype3	138	1.6 (2.8)

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.124 (Fisher's exact test), Q value = 0.25

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	48	444
subtype1	1	4	11	122
subtype2	1	1	25	182
subtype3	0	6	12	140

Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.291 (Fisher's exact test), Q value = 0.42

Table S71. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	25	458
subtype1	10	126
subtype2	10	182
subtype3	5	150

Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S72. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	179	104	172	65

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00605 (logrank test), Q value = 0.034

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	519	219	0.1 - 211.0 (21.1)
subtype1	178	88	0.5 - 180.2 (20.9)
subtype2	104	47	0.1 - 129.2 (25.4)
subtype3	172	72	0.1 - 211.0 (18.1)
subtype4	65	12	0.1 - 139.4 (23.1)

Figure S67. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00439 (Kruskal-Wallis (anova)), Q value = 0.027

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	519	60.9 (11.9)
subtype1	178	59.2 (13.0)
subtype2	104	61.9 (9.8)
subtype3	172	63.0 (12.0)
subtype4	65	58.1 (10.3)

Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.1 (Fisher's exact test), Q value = 0.21

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	27	74	81	254	12	1
subtype1	17	25	32	88	4	0
subtype2	0	15	18	54	3	1
subtype3	6	29	25	90	4	0
subtype4	4	5	6	22	1	0

Figure S69. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.0012

Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	49	136	99	174
subtype1	25	54	42	47
subtype2	5	23	24	41
subtype3	9	44	25	78
subtype4	10	15	8	8

Figure S70. 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 = 0.124 (Fisher's exact test), Q value = 0.25

Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	176	67	169	8
subtype1	58	25	71	3
subtype2	38	12	32	3
subtype3	70	25	46	1
subtype4	10	5	20	1

Figure S71. 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 = 0.258 (Fisher's exact test), Q value = 0.39

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

nPatients	0	1
ALL	185	1
subtype1	68	0
subtype2	28	1
subtype3	70	0
subtype4	19	0

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 8e-05 (Fisher's exact test), Q value = 0.0016

Table S79. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	136	384
subtype1	53	126
subtype2	23	81
subtype3	56	116
subtype4	4	61

Figure S73. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00712 (Fisher's exact test), Q value = 0.038

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	150	279
subtype1	61	88
subtype2	27	50
subtype3	52	91
subtype4	10	50

Figure S74. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00029 (Fisher's exact test), Q value = 0.0041

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: '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	509	1	10
subtype1	178	0	1
subtype2	100	0	4
subtype3	171	1	0
subtype4	60	0	5

Figure S75. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.00186 (Kruskal-Wallis (anova)), Q value = 0.015

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	296	45.8 (35.3)
subtype1	96	42.9 (36.9)
subtype2	72	54.1 (28.8)
subtype3	92	42.1 (35.2)
subtype4	36	46.6 (41.1)

Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.199 (Kruskal-Wallis (anova)), Q value = 0.32

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	281	1967.4 (12.7)
subtype1	98	1969.0 (13.1)
subtype2	65	1964.7 (10.5)
subtype3	85	1968.2 (13.2)
subtype4	33	1965.6 (13.7)

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0102 (Kruskal-Wallis (anova)), Q value = 0.049

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	405	2.2 (4.3)
subtype1	155	2.4 (4.3)
subtype2	84	2.4 (6.1)
subtype3	136	1.8 (3.1)
subtype4	30	2.7 (3.1)

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.125 (Fisher's exact test), Q value = 0.25

Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	48	444
subtype1	1	8	11	153
subtype2	0	1	15	84
subtype3	1	2	17	147
subtype4	0	0	5	60

Figure S79. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.775 (Fisher's exact test), Q value = 0.85

Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	25	458
subtype1	10	159
subtype2	3	89
subtype3	8	152
subtype4	4	58

Figure S80. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S87. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	222	153	148

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.338 (logrank test), Q value = 0.47

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	522	220	0.1 - 211.0 (21.1)
subtype1	222	92	0.1 - 153.9 (21.2)
subtype2	153	55	0.1 - 211.0 (20.2)
subtype3	147	73	0.5 - 180.2 (21.2)

Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0645 (Kruskal-Wallis (anova)), Q value = 0.16

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	522	60.9 (12.0)
subtype1	222	61.8 (10.7)
subtype2	153	62.0 (11.9)
subtype3	147	58.6 (13.5)

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.318 (Fisher's exact test), Q value = 0.45

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	27	76	79	258	12	1
subtype1	6	28	27	115	7	1
subtype2	10	23	28	69	2	0
subtype3	11	25	24	74	3	0

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.155 (Fisher's exact test), Q value = 0.29

Table S91. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	49	138	99	175
subtype1	15	47	43	82
subtype2	16	41	29	50
subtype3	18	50	27	43

Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0523 (Fisher's exact test), Q value = 0.14

Table S92. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	176	68	171	8
subtype1	78	20	72	5
subtype2	56	25	40	2
subtype3	42	23	59	1

Figure S85. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

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

Table S93. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	188	1
subtype1	71	1
subtype2	70	0
subtype3	47	0

Figure S86. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0361 (Fisher's exact test), Q value = 0.11

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

nPatients	FEMALE	MALE
ALL	141	382
subtype1	47	175
subtype2	48	105
subtype3	46	102

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.03 (Fisher's exact test), Q value = 0.097

Table S95. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	150	282
subtype1	57	126
subtype2	41	92
subtype3	52	64

Figure S88. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.00884 (Fisher's exact test), Q value = 0.044

Table S96. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: '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	512	1	10
subtype1	212	1	9
subtype2	153	0	0
subtype3	147	0	1

Figure S89. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.787 (Kruskal-Wallis (anova)), Q value = 0.85

Table S97. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	295	45.7 (35.2)
subtype1	133	46.1 (30.9)
subtype2	88	45.2 (36.3)
subtype3	74	45.6 (41.1)

Figure S90. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.13 (Kruskal-Wallis (anova)), Q value = 0.25

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	279	1967.2 (12.7)
subtype1	120	1965.3 (12.3)
subtype2	78	1968.3 (12.8)
subtype3	81	1968.9 (13.1)

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0304 (Kruskal-Wallis (anova)), Q value = 0.097

Table S99. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	410	2.2 (4.3)
subtype1	164	2.4 (5.0)
subtype2	121	1.6 (2.5)
subtype3	125	2.6 (4.7)

Figure S92. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.0198 (Fisher's exact test), Q value = 0.081

Table S100. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	47	448
subtype1	1	1	27	189
subtype2	0	7	12	129
subtype3	1	3	8	130

Figure S93. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.416 (Fisher's exact test), Q value = 0.55

Table S101. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	25	461
subtype1	8	193
subtype2	7	137
subtype3	10	131

Figure S94. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S102. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	193	144	186

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0423 (logrank test), Q value = 0.12

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	522	220	0.1 - 211.0 (21.1)
subtype1	192	96	0.5 - 180.2 (20.2)
subtype2	144	59	0.1 - 153.9 (20.5)
subtype3	186	65	0.1 - 211.0 (22.5)

Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.0733 (Kruskal-Wallis (anova)), Q value = 0.18

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	522	60.9 (12.0)
subtype1	192	59.6 (13.5)
subtype2	144	62.8 (11.1)
subtype3	186	60.8 (10.8)

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.0602 (Fisher's exact test), Q value = 0.16

Table S105. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	27	76	79	258	12	1
subtype1	19	31	31	94	5	0
subtype2	3	18	20	80	5	1
subtype3	5	27	28	84	2	0

Figure S97. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00153 (Fisher's exact test), Q value = 0.013

Table S106. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	49	138	99	175
subtype1	27	58	41	55
subtype2	5	30	27	66
subtype3	17	50	31	54

Figure S98. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.0856 (Fisher's exact test), Q value = 0.2

Table S107. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	176	68	171	8
subtype1	67	25	73	3
subtype2	61	15	40	4
subtype3	48	28	58	1

Figure S99. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.276 (Fisher's exact test), Q value = 0.41

Table S108. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	188	1
subtype1	74	0
subtype2	51	1
subtype3	63	0

Figure S100. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.011 (Fisher's exact test), Q value = 0.051

Table S109. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	141	382
subtype1	58	135
subtype2	47	97
subtype3	36	150

Figure S101. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.0263 (Fisher's exact test), Q value = 0.097

Table S110. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	150	282
subtype1	67	91
subtype2	38	75
subtype3	45	116

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.237 (Fisher's exact test), Q value = 0.37

Table S111. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: '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	512	1	10
subtype1	191	0	2
subtype2	141	1	2
subtype3	180	0	6

Figure S103. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.182 (Kruskal-Wallis (anova)), Q value = 0.31

Table S112. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	295	45.7 (35.2)
subtype1	96	44.0 (36.8)
subtype2	77	49.9 (30.1)
subtype3	122	44.3 (36.8)

Figure S104. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0804 (Kruskal-Wallis (anova)), Q value = 0.19

Table S113. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	279	1967.2 (12.7)
subtype1	99	1969.4 (13.2)
subtype2	72	1964.6 (10.5)
subtype3	108	1966.9 (13.4)

Figure S105. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00425 (Kruskal-Wallis (anova)), Q value = 0.027

Table S114. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	410	2.2 (4.3)
subtype1	167	2.4 (4.2)
subtype2	119	1.6 (3.5)
subtype3	124	2.5 (4.9)

Figure S106. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0277 (Fisher's exact test), Q value = 0.097

Table S115. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	47	448
subtype1	1	8	12	165
subtype2	0	2	20	118
subtype3	1	1	15	165

Figure S107. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.378 (Fisher's exact test), Q value = 0.51

Table S116. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	25	461
subtype1	12	169
subtype2	4	128
subtype3	9	164

Figure S108. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S117. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	75	140	99	116	47

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

P value = 0.193 (logrank test), Q value = 0.32

Table S118. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	476	196	0.1 - 211.0 (21.1)
subtype1	74	37	0.1 - 211.0 (19.9)
subtype2	140	53	0.4 - 180.2 (16.9)
subtype3	99	30	0.5 - 139.4 (25.9)
subtype4	116	55	0.1 - 153.9 (25.4)
subtype5	47	21	0.1 - 159.7 (20.9)

Figure S109. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.779 (Kruskal-Wallis (anova)), Q value = 0.85

Table S119. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	476	61.0 (11.8)
subtype1	74	61.8 (9.6)
subtype2	140	61.6 (12.8)
subtype3	99	61.3 (9.9)
subtype4	116	60.4 (13.3)
subtype5	47	59.5 (11.6)

Figure S110. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0766 (Fisher's exact test), Q value = 0.18

Table S120. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	25	72	74	231	10	1
subtype1	2	14	9	45	3	0
subtype2	7	20	29	64	2	0
subtype3	2	7	15	47	3	1
subtype4	11	20	18	58	1	0
subtype5	3	11	3	17	1	0

Figure S111. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0431 (Fisher's exact test), Q value = 0.12

Table S121. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	46	125	90	158
subtype1	4	22	13	34
subtype2	12	40	28	45
subtype3	6	19	22	29
subtype4	13	31	21	43
subtype5	11	13	6	7

Figure S112. 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 = 0.469 (Fisher's exact test), Q value = 0.59

Table S122. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	161	65	154	6
subtype1	29	10	29	1
subtype2	50	24	41	1
subtype3	27	12	31	3
subtype4	41	17	36	0
subtype5	14	2	17	1

Figure S113. 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 = 0.561 (Fisher's exact test), Q value = 0.67

Table S123. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	174	1
subtype1	21	0
subtype2	77	0
subtype3	30	1
subtype4	31	0
subtype5	15	0

Figure S114. 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 = 0.0329 (Fisher's exact test), Q value = 0.1

Table S124. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	130	347
subtype1	20	55
subtype2	50	90
subtype3	17	82
subtype4	32	84
subtype5	11	36

Figure S115. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.00044 (Fisher's exact test), Q value = 0.0056

Table S125. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	137	261
subtype1	27	30
subtype2	39	89
subtype3	16	69
subtype4	41	48
subtype5	14	25

Figure S116. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 7e-04 (Fisher's exact test), Q value = 0.0082

Table S126. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: '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	466	1	10
subtype1	74	0	1
subtype2	139	1	0
subtype3	92	0	7
subtype4	116	0	0
subtype5	45	0	2

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

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.00805 (Kruskal-Wallis (anova)), Q value = 0.042

Table S127. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	262	45.9 (36.2)
subtype1	42	44.3 (28.7)
subtype2	73	35.7 (21.5)
subtype3	62	53.8 (36.3)
subtype4	61	51.1 (41.4)
subtype5	24	45.5 (58.5)

Figure S118. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0167 (Kruskal-Wallis (anova)), Q value = 0.073

Table S128. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	255	1967.1 (12.9)
subtype1	42	1965.0 (11.8)
subtype2	62	1971.4 (12.3)
subtype3	54	1965.0 (11.9)
subtype4	73	1966.2 (13.6)
subtype5	24	1967.9 (14.4)

Figure S119. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.35 (Kruskal-Wallis (anova)), Q value = 0.48

Table S129. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	376	2.2 (4.3)
subtype1	62	3.5 (7.8)
subtype2	118	1.6 (2.6)
subtype3	72	2.5 (4.4)
subtype4	94	1.9 (3.0)
subtype5	30	1.8 (2.1)

Figure S120. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.0283 (Fisher's exact test), Q value = 0.097

Table S130. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	44	405
subtype1	0	0	10	60
subtype2	1	8	7	121
subtype3	0	1	13	82
subtype4	0	2	8	103
subtype5	1	0	6	39

Figure S121. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.969 (Fisher's exact test), Q value = 1

Table S131. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	417
subtype1	3	64
subtype2	7	124
subtype3	4	83
subtype4	7	105
subtype5	3	41

Figure S122. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S132. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	143	245	89

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

P value = 0.129 (logrank test), Q value = 0.25

Table S133. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	476	196	0.1 - 211.0 (21.1)
subtype1	143	68	0.5 - 153.9 (21.1)
subtype2	245	87	0.1 - 156.5 (21.9)
subtype3	88	41	0.1 - 211.0 (17.8)

Figure S123. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.675 (Kruskal-Wallis (anova)), Q value = 0.78

Table S134. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	476	61.0 (11.8)
subtype1	143	60.4 (13.1)
subtype2	245	61.8 (10.2)
subtype3	88	60.1 (13.4)

Figure S124. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.501 (Fisher's exact test), Q value = 0.62

Table S135. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	25	72	74	231	10	1
subtype1	14	22	23	73	3	0
subtype2	7	34	36	116	4	1
subtype3	4	16	15	42	3	0

Figure S125. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.785 (Fisher's exact test), Q value = 0.85

Table S136. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	46	125	90	158
subtype1	18	42	31	44
subtype2	20	59	40	83
subtype3	8	24	19	31

Figure S126. 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 = 0.916 (Fisher's exact test), Q value = 0.98

Table S137. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	161	65	154	6
subtype1	49	21	54	1
subtype2	81	33	69	4
subtype3	31	11	31	1

Figure S127. 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 = 1 (Fisher's exact test), Q value = 1

Table S138. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	174	1
subtype1	44	0
subtype2	80	1
subtype3	50	0

Figure S128. 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 = 0.497 (Fisher's exact test), Q value = 0.62

Table S139. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	130	347
subtype1	44	99
subtype2	62	183
subtype3	24	65

Figure S129. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00312 (Fisher's exact test), Q value = 0.023

Table S140. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	137	261
subtype1	52	58
subtype2	65	145
subtype3	20	58

Figure S130. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0928 (Fisher's exact test), Q value = 0.21

Table S141. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: '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	466	1	10
subtype1	141	0	2
subtype2	237	0	8
subtype3	88	1	0

Figure S131. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.127 (Kruskal-Wallis (anova)), Q value = 0.25

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

	nPatients	Mean (Std.Dev)
ALL	262	45.9 (36.2)
subtype1	68	49.1 (42.1)
subtype2	149	47.2 (36.7)
subtype3	45	36.6 (20.9)

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

'MIRseq Mature cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.33 (Kruskal-Wallis (anova)), Q value = 0.46

Table S143. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	255	1967.1 (12.9)
subtype1	78	1967.9 (13.4)
subtype2	140	1966.1 (12.3)
subtype3	37	1969.4 (13.7)

Figure S133. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.339 (Kruskal-Wallis (anova)), Q value = 0.47

Table S144. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	376	2.2 (4.3)
subtype1	121	2.3 (4.4)
subtype2	180	2.1 (4.6)
subtype3	75	2.2 (3.5)

Figure S134. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.101 (Fisher's exact test), Q value = 0.21

Table S145. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	11	44	405
subtype1	0	2	9	128
subtype2	1	4	28	207
subtype3	1	5	7	70

Figure S135. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.885 (Fisher's exact test), Q value = 0.95

Table S146. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	417
subtype1	8	129
subtype2	11	213
subtype3	5	75

Figure S136. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/HNSC-TP/20125733/HNSC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/HNSC-TP/19775211/HNSC-TP.merged_data.txt
Number of patients = 528
Number of clustering approaches = 10
Number of selected clinical features = 14
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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