Correlation between aggregated molecular cancer subtypes and selected clinical features

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 522 patients, 47 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', '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 4 subtypes that correlate to 'GENDER' and 'RADIATIONS_RADIATION_REGIMENINDICATION'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_N_STAGE', and 'NUMBER_OF_LYMPH_NODES'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'GENDER', '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', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'NUMBER_PACK_YEARS_SMOKED', and 'NUMBER_OF_LYMPH_NODES'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'GENDER', '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', 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'GENDER', and 'NUMBER_OF_LYMPH_NODES'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', '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 '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 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 47 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	0.144 (0.283)	0.0157 (0.0708)	0.195 (0.347)	0.262 (0.407)	0.3 (0.441)	0.0157 (0.0708)	0.447 (0.58)	0.0395 (0.126)	0.159 (0.305)	0.0952 (0.222)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.0462 (0.144)	7.43e-10 (1.04e-07)	0.255 (0.405)	0.0759 (0.19)	0.281 (0.424)	0.00486 (0.0309)	0.0658 (0.177)	0.0499 (0.149)	0.774 (0.834)	0.741 (0.814)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.468 (0.601)	0.697 (0.798)	0.518 (0.648)	0.0299 (0.11)	0.215 (0.367)	0.0898 (0.213)	0.439 (0.574)	0.0655 (0.177)	0.134 (0.275)	0.484 (0.615)
PATHOLOGY T STAGE	Fisher's exact test	0.169 (0.316)	0.0071 (0.0432)	0.169 (0.316)	0.196 (0.347)	0.115 (0.252)	3e-05 (7e-04)	0.137 (0.277)	0.0021 (0.0184)	0.0479 (0.146)	0.745 (0.814)
PATHOLOGY N STAGE	Fisher's exact test	0.0619 (0.176)	0.0345 (0.116)	0.356 (0.495)	2e-05 (7e-04)	0.363 (0.498)	0.139 (0.278)	0.0752 (0.19)	0.0808 (0.198)	0.533 (0.655)	0.906 (0.96)
PATHOLOGY M STAGE	Fisher's exact test	0.602 (0.706)	0.549 (0.663)			1 (1.00)	0.251 (0.405)	1 (1.00)	0.278 (0.423)	0.56 (0.67)	1 (1.00)
GENDER	Fisher's exact test	0.0881 (0.213)	1e-05 (0.000467)	0.00367 (0.0257)	0.219 (0.369)	0.00237 (0.0195)	3e-05 (7e-04)	0.0384 (0.125)	0.0118 (0.057)	0.0264 (0.102)	0.565 (0.67)
HISTOLOGICAL TYPE	Fisher's exact test	0.112 (0.25)	0.021 (0.0893)			0.00028 (0.00436)	0.00034 (0.00476)	0.00923 (0.0479)	0.239 (0.394)	0.00065 (0.00758)	0.0994 (0.228)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.754 (0.819)	0.605 (0.706)	0.0075 (0.0437)	0.437 (0.574)	0.321 (0.462)	0.0237 (0.0966)	0.122 (0.261)	0.0628 (0.176)	0.00337 (0.0257)	0.00045 (0.00573)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.000105 (0.00183)	0.00464 (0.0309)	0.0672 (0.178)	0.743 (0.814)	0.0326 (0.114)	0.00105 (0.0105)	0.674 (0.78)	0.127 (0.264)	0.00911 (0.0479)	0.253 (0.405)
YEAR OF TOBACCO SMOKING ONSET	Kruskal-Wallis (anova)	0.000768 (0.00827)	0.00173 (0.0161)	0.488 (0.615)	0.238 (0.394)	0.207 (0.359)	0.258 (0.406)	0.181 (0.329)	0.0705 (0.183)	0.017 (0.0745)	0.31 (0.452)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.33 (0.467)	4.27e-05 (0.000854)	0.208 (0.359)	4.76e-06 (0.000333)	0.0348 (0.116)	0.00912 (0.0479)	0.027 (0.102)	0.00351 (0.0257)	0.357 (0.495)	0.289 (0.43)
RACE	Fisher's exact test	0.323 (0.462)	0.0241 (0.0966)	0.104 (0.235)	0.428 (0.574)	0.147 (0.286)	0.181 (0.329)	0.0315 (0.113)	0.0578 (0.169)	0.0117 (0.057)	0.123 (0.261)
ETHNICITY	Fisher's exact test	0.701 (0.798)	0.743 (0.814)	0.741 (0.814)	0.542 (0.66)	0.277 (0.423)	0.527 (0.653)	0.431 (0.574)	0.375 (0.51)	0.972 (1.00)	0.886 (0.947)

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	153	133	189	41

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

P value = 0.144 (logrank test), Q value = 0.28

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	513	200	0.1 - 211.0 (18.0)
subtype1	152	69	0.1 - 180.2 (17.4)
subtype2	133	48	0.1 - 153.9 (20.1)
subtype3	187	67	0.1 - 211.0 (19.0)
subtype4	41	16	2.7 - 94.9 (16.0)

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 'YEARS_TO_BIRTH'

P value = 0.0462 (Kruskal-Wallis (anova)), Q value = 0.14

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

	nPatients	Mean (Std.Dev)
ALL	515	60.9 (12.0)
subtype1	153	58.5 (13.1)
subtype2	133	62.4 (9.7)
subtype3	188	61.1 (12.4)
subtype4	41	63.6 (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 'NEOPLASM_DISEASESTAGE'

P value = 0.468 (Fisher's exact test), Q value = 0.6

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	27	77	77	252	12	1
subtype1	7	20	22	85	5	1
subtype2	6	16	19	69	4	0
subtype3	12	34	29	79	1	0
subtype4	2	7	7	19	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 = 0.169 (Fisher's exact test), Q value = 0.32

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	133	99	172
subtype1	12	39	32	59
subtype2	10	29	30	48
subtype3	25	54	26	53
subtype4	3	11	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 = 0.0619 (Fisher's exact test), Q value = 0.18

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

nPatients	N0	N1	N2	N3
ALL	178	66	165	8
subtype1	50	19	59	2
subtype2	44	13	47	3
subtype3	73	25	45	1
subtype4	11	9	14	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 = 0.602 (Fisher's exact test), Q value = 0.71

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

nPatients	0	1
ALL	183	1
subtype1	52	1
subtype2	38	0
subtype3	73	0
subtype4	20	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 = 0.0881 (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	139	377
subtype1	39	114
subtype2	27	106
subtype3	58	131
subtype4	15	26

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 = 0.112 (Fisher's exact test), Q value = 0.25

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	505	1	10
subtype1	153	0	0
subtype2	129	0	4
subtype3	182	1	6
subtype4	41	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 = 0.754 (Fisher's exact test), Q value = 0.82

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

nPatients	NO	YES
ALL	81	435
subtype1	24	129
subtype2	23	110
subtype3	30	159
subtype4	4	37

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 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.000105 (Kruskal-Wallis (anova)), Q value = 0.0018

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

	nPatients	Mean (Std.Dev)
ALL	287	45.2 (35.5)
subtype1	76	39.1 (21.3)
subtype2	88	55.7 (34.0)
subtype3	99	43.5 (44.8)
subtype4	24	33.2 (23.5)

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 = 0.000768 (Kruskal-Wallis (anova)), Q value = 0.0083

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

	nPatients	Mean (Std.Dev)
ALL	274	1967.2 (12.7)
subtype1	72	1970.9 (11.5)
subtype2	85	1963.5 (11.5)
subtype3	96	1966.6 (14.0)
subtype4	21	1972.3 (10.1)

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 = 0.33 (Kruskal-Wallis (anova)), Q value = 0.47

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

	nPatients	Mean (Std.Dev)
ALL	402	2.2 (4.3)
subtype1	128	2.2 (3.9)
subtype2	107	2.8 (6.4)
subtype3	129	1.7 (2.6)
subtype4	38	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 = 0.323 (Fisher's exact test), Q value = 0.46

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	45	441
subtype1	1	5	16	124
subtype2	0	1	16	113
subtype3	1	4	10	168
subtype4	0	1	3	36

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 = 0.701 (Fisher's exact test), Q value = 0.8

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	25	454
subtype1	10	133
subtype2	5	114
subtype3	9	168
subtype4	1	39

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	95	90	77	75	19

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0157 (logrank test), Q value = 0.071

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	519	203	0.1 - 211.0 (18.4)
subtype1	85	36	2.3 - 211.0 (16.1)
subtype2	79	24	0.1 - 153.9 (21.2)
subtype3	95	41	0.2 - 180.2 (18.9)
subtype4	90	41	0.5 - 139.4 (17.6)
subtype5	77	16	0.1 - 94.6 (20.4)
subtype6	74	35	0.1 - 169.4 (17.0)
subtype7	19	10	1.8 - 84.5 (16.2)

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 = 7.43e-10 (Kruskal-Wallis (anova)), Q value = 1e-07

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

	nPatients	Mean (Std.Dev)
ALL	521	60.9 (11.9)
subtype1	86	59.6 (12.4)
subtype2	79	58.8 (9.9)
subtype3	95	56.9 (13.5)
subtype4	90	63.9 (10.6)
subtype5	77	57.6 (9.6)
subtype6	75	67.6 (10.9)
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 'NEOPLASM_DISEASESTAGE'

P value = 0.697 (Fisher's exact test), Q value = 0.8

Table S19. 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	27	78	77	257	12	1
subtype1	9	15	12	45	0	0
subtype2	3	14	12	40	3	1
subtype3	6	14	18	46	2	0
subtype4	2	8	12	52	3	0
subtype5	2	9	11	25	1	0
subtype6	3	15	9	40	3	0
subtype7	2	3	3	9	0	0

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0071 (Fisher's exact test), Q value = 0.043

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

nPatients	T0+T1	T2	T3	T4
ALL	50	136	100	174
subtype1	15	27	14	26
subtype2	8	17	16	33
subtype3	8	27	24	28
subtype4	4	19	18	37
subtype5	10	22	11	8
subtype6	3	19	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 = 0.0345 (Fisher's exact test), Q value = 0.12

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

nPatients	N0	N1	N2	N3
ALL	179	66	169	8
subtype1	33	14	31	0
subtype2	40	10	20	1
subtype3	31	10	36	1
subtype4	22	9	37	3
subtype5	14	9	22	1
subtype6	34	10	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 = 0.549 (Fisher's exact test), Q value = 0.66

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

nPatients	0	1
ALL	184	1
subtype1	45	0
subtype2	30	1
subtype3	38	0
subtype4	20	0
subtype5	19	0
subtype6	24	0
subtype7	8	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 = 1e-05 (Fisher's exact test), Q value = 0.00047

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

nPatients	FEMALE	MALE
ALL	141	381
subtype1	29	58
subtype2	13	66
subtype3	30	65
subtype4	18	72
subtype5	9	68
subtype6	38	37
subtype7	4	15

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.021 (Fisher's exact test), Q value = 0.089

Table S24. 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	511	1	10
subtype1	87	0	0
subtype2	76	0	3
subtype3	94	1	0
subtype4	88	0	2
subtype5	72	0	5
subtype6	75	0	0
subtype7	19	0	0

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

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.605 (Fisher's exact test), Q value = 0.71

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	83	439
subtype1	16	71
subtype2	10	69
subtype3	14	81
subtype4	19	71
subtype5	13	64
subtype6	8	67
subtype7	3	16

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

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.00464 (Kruskal-Wallis (anova)), Q value = 0.031

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

	nPatients	Mean (Std.Dev)
ALL	292	45.1 (35.4)
subtype1	54	46.3 (43.9)
subtype2	52	50.5 (26.9)
subtype3	38	35.0 (19.0)
subtype4	62	51.6 (30.6)
subtype5	39	39.0 (38.0)
subtype6	31	46.9 (52.6)
subtype7	16	34.0 (18.7)

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 = 0.00173 (Kruskal-Wallis (anova)), Q value = 0.016

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

	nPatients	Mean (Std.Dev)
ALL	279	1967.1 (12.6)
subtype1	52	1968.1 (13.5)
subtype2	45	1964.8 (10.7)
subtype3	43	1972.7 (12.1)
subtype4	59	1967.4 (12.6)
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 = 4.27e-05 (Kruskal-Wallis (anova)), Q value = 0.00085

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

	nPatients	Mean (Std.Dev)
ALL	407	2.2 (4.3)
subtype1	70	2.3 (3.4)
subtype2	68	1.0 (2.2)
subtype3	81	1.8 (2.6)
subtype4	70	4.4 (8.2)
subtype5	39	2.3 (2.8)
subtype6	60	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 = 0.0241 (Fisher's exact test), Q value = 0.097

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	45	447
subtype1	1	4	6	71
subtype2	0	0	12	62
subtype3	1	1	4	85
subtype4	0	2	13	74
subtype5	0	0	3	74
subtype6	0	4	5	65
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 = 0.743 (Fisher's exact test), Q value = 0.81

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	25	460
subtype1	3	78
subtype2	2	66
subtype3	8	82
subtype4	4	79
subtype5	4	68
subtype6	3	69
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	4
Number of samples	55	52	37	68

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.195 (logrank test), Q value = 0.35

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	212	120	0.1 - 211.0 (20.7)
subtype1	55	34	2.5 - 126.2 (17.2)
subtype2	52	24	3.5 - 139.4 (21.5)
subtype3	37	21	4.0 - 156.5 (26.2)
subtype4	68	41	0.1 - 211.0 (20.6)

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 = 0.255 (Kruskal-Wallis (anova)), Q value = 0.41

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	55	60.4 (12.3)
subtype2	52	62.7 (9.1)
subtype3	37	62.6 (14.6)
subtype4	68	62.8 (12.9)

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

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.518 (Fisher's exact test), Q value = 0.65

Table S34. 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 S31. 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 = 0.169 (Fisher's exact test), Q value = 0.32

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	3	19	12	19
subtype2	3	6	14	25
subtype3	3	13	6	13
subtype4	4	21	21	22

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 = 0.356 (Fisher's exact test), Q value = 0.49

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	20	5	19	1
subtype2	15	3	25	1
subtype3	17	5	8	1
subtype4	20	8	27	1

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 = 0.00367 (Fisher's exact test), Q value = 0.026

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

nPatients	FEMALE	MALE
ALL	62	150
subtype1	13	42
subtype2	7	45
subtype3	15	22
subtype4	27	41

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

'RPPA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0075 (Fisher's exact test), Q value = 0.044

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0672 (Kruskal-Wallis (anova)), Q value = 0.18

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

	nPatients	Mean (Std.Dev)
ALL	109	48.5 (38.2)
subtype1	26	47.7 (27.9)
subtype2	31	57.9 (34.7)
subtype3	22	48.9 (60.9)
subtype4	30	39.0 (25.1)

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 = 0.488 (Kruskal-Wallis (anova)), Q value = 0.62

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

	nPatients	Mean (Std.Dev)
ALL	116	1964.5 (11.9)
subtype1	27	1967.4 (11.2)
subtype2	32	1964.0 (9.8)
subtype3	24	1963.9 (14.8)
subtype4	33	1963.2 (12.2)

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 = 0.208 (Kruskal-Wallis (anova)), Q value = 0.36

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	42	2.9 (4.2)
subtype2	44	4.5 (8.3)
subtype3	30	1.2 (1.7)
subtype4	54	2.4 (2.8)

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 = 0.104 (Fisher's exact test), Q value = 0.24

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	183
subtype1	1	0	8	44
subtype2	0	1	7	43
subtype3	0	0	1	36
subtype4	0	2	3	60

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.741 (Fisher's exact test), Q value = 0.81

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	5	47
subtype2	2	46
subtype3	2	35
subtype4	4	62

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 = 0.262 (logrank test), Q value = 0.41

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	212	120	0.1 - 211.0 (20.7)
subtype1	78	43	2.5 - 156.5 (20.6)
subtype2	85	54	0.1 - 211.0 (19.8)
subtype3	49	23	2.1 - 111.2 (26.2)

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 = 0.19

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 'NEOPLASM_DISEASESTAGE'

P value = 0.0299 (Fisher's exact test), Q value = 0.11

Table S47. 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 S43. 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 = 0.196 (Fisher's exact test), Q value = 0.35

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 = 0.37

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 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.437 (Fisher's exact test), Q value = 0.57

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.743 (Kruskal-Wallis (anova)), Q value = 0.81

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

	nPatients	Mean (Std.Dev)
ALL	109	48.5 (38.2)
subtype1	38	51.3 (49.3)
subtype2	44	50.6 (35.6)
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 = 0.238 (Kruskal-Wallis (anova)), Q value = 0.39

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

	nPatients	Mean (Std.Dev)
ALL	116	1964.5 (11.9)
subtype1	42	1965.3 (13.8)
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 = 0.428 (Fisher's exact test), Q value = 0.57

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	183
subtype1	1	0	10	65
subtype2	0	2	6	74
subtype3	0	1	3	44

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.542 (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	210	160

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.3 (logrank test), Q value = 0.44

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	511	200	0.1 - 211.0 (18.5)
subtype1	142	63	0.2 - 180.2 (18.2)
subtype2	210	71	0.1 - 139.4 (19.7)
subtype3	159	66	0.1 - 211.0 (17.9)

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 = 0.281 (Kruskal-Wallis (anova)), Q value = 0.42

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

	nPatients	Mean (Std.Dev)
ALL	513	60.9 (11.9)
subtype1	143	59.5 (12.5)
subtype2	210	61.4 (10.3)
subtype3	160	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 'NEOPLASM_DISEASESTAGE'

P value = 0.215 (Fisher's exact test), Q value = 0.37

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	27	75	76	253	12	1
subtype1	15	24	20	71	4	0
subtype2	4	25	30	97	5	1
subtype3	8	26	26	85	3	0

Figure S55. 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 = 0.115 (Fisher's exact test), Q value = 0.25

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

nPatients	T0+T1	T2	T3	T4
ALL	49	132	98	173
subtype1	19	49	27	41
subtype2	18	45	39	64
subtype3	12	38	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 = 0.363 (Fisher's exact test), Q value = 0.5

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

nPatients	N0	N1	N2	N3
ALL	175	65	166	8
subtype1	47	19	59	3
subtype2	62	23	61	4
subtype3	66	23	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	179	1
subtype1	53	0
subtype2	64	1
subtype3	62	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 = 0.00237 (Fisher's exact test), Q value = 0.02

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

nPatients	FEMALE	MALE
ALL	135	379
subtype1	37	107
subtype2	41	169
subtype3	57	103

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00028 (Fisher's exact test), Q value = 0.0044

Table S65. 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	503	1	10
subtype1	143	1	0
subtype2	200	0	10
subtype3	160	0	0

Figure S60. 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 = 0.321 (Fisher's exact test), Q value = 0.46

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	83	431
subtype1	29	115
subtype2	31	179
subtype3	23	137

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

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0326 (Kruskal-Wallis (anova)), Q value = 0.11

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

	nPatients	Mean (Std.Dev)
ALL	290	45.2 (35.5)
subtype1	77	42.6 (39.8)
subtype2	131	50.9 (39.1)
subtype3	82	38.6 (21.0)

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 = 0.207 (Kruskal-Wallis (anova)), Q value = 0.36

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

	nPatients	Mean (Std.Dev)
ALL	278	1967.2 (12.6)
subtype1	81	1969.2 (12.3)
subtype2	120	1965.6 (12.1)
subtype3	77	1967.5 (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 = 0.0348 (Kruskal-Wallis (anova)), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	399	2.2 (4.3)
subtype1	128	2.7 (4.8)
subtype2	135	2.3 (5.0)
subtype3	136	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 = 0.147 (Fisher's exact test), Q value = 0.29

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	45	439
subtype1	1	4	11	121
subtype2	1	1	23	179
subtype3	0	6	11	139

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.277 (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	24	453
subtype1	10	126
subtype2	9	179
subtype3	5	148

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	102	169	64

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0157 (logrank test), Q value = 0.071

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	511	200	0.1 - 211.0 (18.5)
subtype1	177	82	0.2 - 180.2 (19.0)
subtype2	102	41	0.1 - 129.2 (20.7)
subtype3	168	66	0.1 - 211.0 (15.9)
subtype4	64	11	0.1 - 139.4 (20.4)

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 = 0.00486 (Kruskal-Wallis (anova)), Q value = 0.031

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

	nPatients	Mean (Std.Dev)
ALL	513	60.9 (11.9)
subtype1	178	59.2 (13.0)
subtype2	102	61.8 (9.7)
subtype3	169	63.0 (12.0)
subtype4	64	58.1 (10.4)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0898 (Fisher's exact test), Q value = 0.21

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB	STAGE IVC
ALL	27	75	76	253	12	1
subtype1	17	26	31	88	4	0
subtype2	0	15	17	54	3	1
subtype3	6	29	22	90	4	0
subtype4	4	5	6	21	1	0

Figure S69. 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 = 3e-05 (Fisher's exact test), Q value = 7e-04

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

nPatients	T0+T1	T2	T3	T4
ALL	49	132	98	173
subtype1	25	54	42	47
subtype2	5	22	23	41
subtype3	9	42	25	77
subtype4	10	14	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 = 0.139 (Fisher's exact test), Q value = 0.28

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

nPatients	N0	N1	N2	N3
ALL	175	65	166	8
subtype1	58	25	71	3
subtype2	37	12	31	3
subtype3	70	23	45	1
subtype4	10	5	19	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 = 0.251 (Fisher's exact test), Q value = 0.41

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

nPatients	0	1
ALL	179	1
subtype1	68	0
subtype2	27	1
subtype3	67	0
subtype4	17	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 = 3e-05 (Fisher's exact test), Q value = 7e-04

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

nPatients	FEMALE	MALE
ALL	135	379
subtype1	53	126
subtype2	22	80
subtype3	56	113
subtype4	4	60

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00034 (Fisher's exact test), Q value = 0.0048

Table S80. 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	503	1	10
subtype1	178	0	1
subtype2	98	0	4
subtype3	168	1	0
subtype4	59	0	5

Figure S74. 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 = 0.0237 (Fisher's exact test), Q value = 0.097

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	83	431
subtype1	35	144
subtype2	14	88
subtype3	18	151
subtype4	16	48

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

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.00105 (Kruskal-Wallis (anova)), Q value = 0.011

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

	nPatients	Mean (Std.Dev)
ALL	290	45.2 (35.5)
subtype1	94	42.1 (37.4)
subtype2	71	54.2 (29.0)
subtype3	91	41.0 (34.6)
subtype4	34	46.1 (42.0)

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 = 0.258 (Kruskal-Wallis (anova)), Q value = 0.41

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

	nPatients	Mean (Std.Dev)
ALL	278	1967.2 (12.6)
subtype1	97	1968.7 (12.8)
subtype2	64	1964.7 (10.6)
subtype3	84	1968.0 (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 = 0.00912 (Kruskal-Wallis (anova)), Q value = 0.048

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

	nPatients	Mean (Std.Dev)
ALL	399	2.2 (4.3)
subtype1	155	2.4 (4.3)
subtype2	82	2.4 (6.1)
subtype3	133	1.8 (3.1)
subtype4	29	2.8 (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 = 0.181 (Fisher's exact test), Q value = 0.33

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	45	439
subtype1	1	8	11	152
subtype2	0	1	14	83
subtype3	1	2	15	145
subtype4	0	0	5	59

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.527 (Fisher's exact test), Q value = 0.65

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	453
subtype1	10	159
subtype2	2	88
subtype3	8	149
subtype4	4	57

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	219	151	148

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.447 (logrank test), Q value = 0.58

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	515	201	0.1 - 211.0 (18.4)
subtype1	219	82	0.1 - 153.9 (18.7)
subtype2	150	52	0.1 - 211.0 (17.7)
subtype3	146	67	0.5 - 180.2 (18.9)

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 = 0.0658 (Kruskal-Wallis (anova)), Q value = 0.18

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

	nPatients	Mean (Std.Dev)
ALL	517	60.9 (12.0)
subtype1	219	61.8 (10.7)
subtype2	151	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 'NEOPLASM_DISEASESTAGE'

P value = 0.439 (Fisher's exact test), Q value = 0.57

Table S90. 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	27	77	75	257	12	1
subtype1	6	28	27	113	7	1
subtype2	10	23	25	70	2	0
subtype3	11	26	23	74	3	0

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

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.137 (Fisher's exact test), Q value = 0.28

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

nPatients	T0+T1	T2	T3	T4
ALL	49	134	99	174
subtype1	15	45	43	81
subtype2	16	39	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 = 0.0752 (Fisher's exact test), Q value = 0.19

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

nPatients	N0	N1	N2	N3
ALL	176	66	168	8
subtype1	78	20	69	5
subtype2	56	23	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	183	1
subtype1	69	1
subtype2	67	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 = 0.0384 (Fisher's exact test), Q value = 0.12

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

nPatients	FEMALE	MALE
ALL	141	377
subtype1	47	172
subtype2	48	103
subtype3	46	102

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.00923 (Fisher's exact test), Q value = 0.048

Table S95. 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	507	1	10
subtype1	209	1	9
subtype2	151	0	0
subtype3	147	0	1

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

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.122 (Fisher's exact test), Q value = 0.26

Table S96. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	82	436
subtype1	32	187
subtype2	19	132
subtype3	31	117

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

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.674 (Kruskal-Wallis (anova)), Q value = 0.78

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

	nPatients	Mean (Std.Dev)
ALL	289	45.0 (35.3)
subtype1	130	45.8 (31.3)
subtype2	87	44.3 (35.6)
subtype3	72	44.6 (41.8)

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 = 0.181 (Kruskal-Wallis (anova)), Q value = 0.33

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

	nPatients	Mean (Std.Dev)
ALL	276	1967.0 (12.6)
subtype1	119	1965.4 (12.4)
subtype2	77	1968.1 (12.7)
subtype3	80	1968.5 (12.7)

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 = 0.027 (Kruskal-Wallis (anova)), Q value = 0.1

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

	nPatients	Mean (Std.Dev)
ALL	405	2.2 (4.3)
subtype1	161	2.3 (5.0)
subtype2	119	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 = 0.0315 (Fisher's exact test), Q value = 0.11

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	44	444
subtype1	1	1	25	188
subtype2	0	7	11	128
subtype3	1	3	8	128

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.431 (Fisher's exact test), Q value = 0.57

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	25	456
subtype1	8	190
subtype2	7	135
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	142	183

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0395 (logrank test), Q value = 0.13

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	515	201	0.1 - 211.0 (18.4)
subtype1	191	88	0.2 - 180.2 (17.9)
subtype2	142	55	0.1 - 153.9 (17.9)
subtype3	182	58	0.1 - 211.0 (19.6)

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 = 0.0499 (Kruskal-Wallis (anova)), Q value = 0.15

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

	nPatients	Mean (Std.Dev)
ALL	517	60.9 (12.0)
subtype1	192	59.6 (13.5)
subtype2	142	63.0 (11.1)
subtype3	183	60.7 (10.7)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0655 (Fisher's exact test), Q value = 0.18

Table S105. 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	27	77	75	257	12	1
subtype1	19	32	30	94	5	0
subtype2	3	18	19	79	5	1
subtype3	5	27	26	84	2	0

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.0021 (Fisher's exact test), Q value = 0.018

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

nPatients	T0+T1	T2	T3	T4
ALL	49	134	99	174
subtype1	27	58	41	55
subtype2	5	29	27	65
subtype3	17	47	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 = 0.0808 (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	66	168	8
subtype1	67	25	73	3
subtype2	61	14	39	4
subtype3	48	27	56	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 = 0.278 (Fisher's exact test), Q value = 0.42

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

nPatients	0	1
ALL	183	1
subtype1	74	0
subtype2	50	1
subtype3	59	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 = 0.0118 (Fisher's exact test), Q value = 0.057

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

nPatients	FEMALE	MALE
ALL	141	377
subtype1	58	135
subtype2	47	95
subtype3	36	147

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.239 (Fisher's exact test), Q value = 0.39

Table S110. 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	507	1	10
subtype1	191	0	2
subtype2	139	1	2
subtype3	177	0	6

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0628 (Fisher's exact test), Q value = 0.18

Table S111. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	82	436
subtype1	35	158
subtype2	14	128
subtype3	33	150

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.127 (Kruskal-Wallis (anova)), Q value = 0.26

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

	nPatients	Mean (Std.Dev)
ALL	289	45.0 (35.3)
subtype1	94	43.3 (37.3)
subtype2	75	49.4 (29.2)
subtype3	120	43.8 (37.2)

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 = 0.0705 (Kruskal-Wallis (anova)), Q value = 0.18

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

	nPatients	Mean (Std.Dev)
ALL	276	1967.0 (12.6)
subtype1	98	1969.0 (12.9)
subtype2	71	1964.4 (10.3)
subtype3	107	1967.0 (13.5)

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 = 0.00351 (Kruskal-Wallis (anova)), Q value = 0.026

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

	nPatients	Mean (Std.Dev)
ALL	405	2.2 (4.3)
subtype1	167	2.4 (4.2)
subtype2	117	1.6 (3.5)
subtype3	121	2.5 (5.0)

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 = 0.0578 (Fisher's exact test), Q value = 0.17

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	44	444
subtype1	1	8	12	164
subtype2	0	2	18	118
subtype3	1	1	14	162

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.375 (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	456
subtype1	12	169
subtype2	4	126
subtype3	9	161

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	74	137	98	116	47

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

P value = 0.159 (logrank test), Q value = 0.3

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	469	180	0.1 - 211.0 (18.0)
subtype1	73	34	0.1 - 211.0 (16.7)
subtype2	137	49	0.2 - 180.2 (15.2)
subtype3	98	26	0.5 - 139.4 (20.6)
subtype4	114	52	0.1 - 153.9 (22.6)
subtype5	47	19	0.1 - 159.7 (17.2)

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 = 0.774 (Kruskal-Wallis (anova)), Q value = 0.83

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

	nPatients	Mean (Std.Dev)
ALL	471	61.0 (11.8)
subtype1	73	61.9 (9.6)
subtype2	137	61.4 (12.8)
subtype3	98	61.4 (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 'NEOPLASM_DISEASESTAGE'

P value = 0.134 (Fisher's exact test), Q value = 0.28

Table S120. 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	25	73	70	230	10	1
subtype1	2	14	9	44	3	0
subtype2	7	21	25	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: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0479 (Fisher's exact test), Q value = 0.15

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

nPatients	T0+T1	T2	T3	T4
ALL	46	121	90	157
subtype1	4	22	13	33
subtype2	12	37	28	45
subtype3	6	18	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 = 0.533 (Fisher's exact test), Q value = 0.66

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

nPatients	N0	N1	N2	N3
ALL	161	63	151	6
subtype1	29	10	28	1
subtype2	50	22	40	1
subtype3	27	12	30	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 = 0.56 (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	170	1
subtype1	21	0
subtype2	75	0
subtype3	29	1
subtype4	30	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 = 0.0264 (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	342
subtype1	20	54
subtype2	50	87
subtype3	17	81
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 'HISTOLOGICAL_TYPE'

P value = 0.00065 (Fisher's exact test), Q value = 0.0076

Table S125. 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	461	1	10
subtype1	73	0	1
subtype2	136	1	0
subtype3	91	0	7
subtype4	116	0	0
subtype5	45	0	2

Figure S116. 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 = 0.00337 (Fisher's exact test), Q value = 0.026

Table S126. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	63	409
subtype1	8	66
subtype2	8	129
subtype3	13	85
subtype4	25	91
subtype5	9	38

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

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.00911 (Kruskal-Wallis (anova)), Q value = 0.048

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

	nPatients	Mean (Std.Dev)
ALL	256	45.1 (36.4)
subtype1	40	43.3 (29.5)
subtype2	71	34.4 (19.2)
subtype3	61	53.5 (36.5)
subtype4	61	50.3 (41.9)
subtype5	23	46.0 (59.8)

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 = 0.017 (Kruskal-Wallis (anova)), Q value = 0.074

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

	nPatients	Mean (Std.Dev)
ALL	252	1967.0 (12.7)
subtype1	42	1965.0 (11.8)
subtype2	60	1971.3 (12.4)
subtype3	54	1965.0 (11.9)
subtype4	72	1965.7 (13.0)
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 = 0.357 (Kruskal-Wallis (anova)), Q value = 0.49

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

	nPatients	Mean (Std.Dev)
ALL	371	2.2 (4.3)
subtype1	61	3.4 (7.9)
subtype2	115	1.6 (2.6)
subtype3	71	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 = 0.0117 (Fisher's exact test), Q value = 0.057

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	41	401
subtype1	0	0	9	60
subtype2	1	8	5	120
subtype3	0	1	13	81
subtype4	0	2	8	101
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 = 0.972 (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	412
subtype1	3	63
subtype2	7	121
subtype3	4	82
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	241	88

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

P value = 0.0952 (logrank test), Q value = 0.22

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	469	180	0.1 - 211.0 (18.0)
subtype1	142	64	0.5 - 153.9 (18.9)
subtype2	240	77	0.1 - 156.5 (18.9)
subtype3	87	39	0.1 - 211.0 (15.1)

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 = 0.741 (Kruskal-Wallis (anova)), Q value = 0.81

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

	nPatients	Mean (Std.Dev)
ALL	471	61.0 (11.8)
subtype1	143	60.4 (13.1)
subtype2	241	61.7 (10.2)
subtype3	87	60.2 (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 'NEOPLASM_DISEASESTAGE'

P value = 0.484 (Fisher's exact test), Q value = 0.62

Table S135. 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	25	73	70	230	10	1
subtype1	14	23	22	73	3	0
subtype2	7	34	33	116	4	1
subtype3	4	16	15	41	3	0

Figure S125. 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 = 0.745 (Fisher's exact test), Q value = 0.81

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

nPatients	T0+T1	T2	T3	T4
ALL	46	121	90	157
subtype1	18	42	31	44
subtype2	20	55	40	83
subtype3	8	24	19	30

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 = 0.906 (Fisher's exact test), Q value = 0.96

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

nPatients	N0	N1	N2	N3
ALL	161	63	151	6
subtype1	49	21	54	1
subtype2	81	31	67	4
subtype3	31	11	30	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	170	1
subtype1	44	0
subtype2	76	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 = 0.565 (Fisher's exact test), Q value = 0.67

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

nPatients	FEMALE	MALE
ALL	130	342
subtype1	44	99
subtype2	62	179
subtype3	24	64

Figure S129. 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 = 0.0994 (Fisher's exact test), Q value = 0.23

Table S140. 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	461	1	10
subtype1	141	0	2
subtype2	233	0	8
subtype3	87	1	0

Figure S130. 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 = 0.00045 (Fisher's exact test), Q value = 0.0057

Table S141. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	63	409
subtype1	26	117
subtype2	35	206
subtype3	2	86

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

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.253 (Kruskal-Wallis (anova)), Q value = 0.41

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

	nPatients	Mean (Std.Dev)
ALL	256	45.1 (36.4)
subtype1	66	48.1 (43.0)
subtype2	146	46.2 (36.7)
subtype3	44	37.0 (21.0)

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 = 0.31 (Kruskal-Wallis (anova)), Q value = 0.45

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

	nPatients	Mean (Std.Dev)
ALL	252	1967.0 (12.7)
subtype1	77	1967.4 (13.0)
subtype2	138	1966.0 (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 = 0.289 (Kruskal-Wallis (anova)), Q value = 0.43

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

	nPatients	Mean (Std.Dev)
ALL	371	2.2 (4.3)
subtype1	121	2.3 (4.4)
subtype2	176	2.1 (4.6)
subtype3	74	2.1 (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 = 0.123 (Fisher's exact test), Q value = 0.26

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	41	401
subtype1	0	2	9	127
subtype2	1	4	26	204
subtype3	1	5	6	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 = 0.886 (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	412
subtype1	8	129
subtype2	11	209
subtype3	5	74

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/15111022/HNSC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/HNSC-TP/15078978/HNSC-TP.merged_data.txt
Number of patients = 522
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.

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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