Correlation between aggregated molecular cancer subtypes and selected clinical features

Head and Neck Squamous Cell Carcinoma (Primary solid tumor)

15 January 2014 | analyses__2014_01_15

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1513WQ8

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 12 clinical features across 379 patients, 7 significant findings detected with P value < 0.05 and Q value < 0.25.

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

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 7 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	0.0222 (1.00)	0.00658 (0.691)	0.234 (1.00)	0.388 (1.00)	0.258 (1.00)	0.0305 (1.00)	0.0652 (1.00)	0.348 (1.00)	0.00367 (0.394)	0.00226 (0.249)
AGE	ANOVA	0.399 (1.00)	5.76e-06 (0.000668)	0.691 (1.00)	0.771 (1.00)	0.611 (1.00)	0.0658 (1.00)	0.324 (1.00)	0.279 (1.00)	0.439 (1.00)	0.599 (1.00)
NEOPLASM DISEASESTAGE	Chi-square test	0.0326 (1.00)	0.18 (1.00)	0.66 (1.00)	0.825 (1.00)	0.101 (1.00)	0.144 (1.00)	0.296 (1.00)	0.0125 (1.00)	0.00812 (0.837)	0.477 (1.00)
PATHOLOGY T STAGE	Chi-square test	0.239 (1.00)	0.0207 (1.00)	0.228 (1.00)	0.293 (1.00)	0.437 (1.00)	0.0293 (1.00)	0.188 (1.00)	0.0036 (0.393)	0.144 (1.00)	0.0919 (1.00)
PATHOLOGY N STAGE	Chi-square test	0.0212 (1.00)	0.0983 (1.00)	0.486 (1.00)	0.399 (1.00)	0.182 (1.00)	0.0558 (1.00)	0.227 (1.00)	0.825 (1.00)	0.0341 (1.00)	0.391 (1.00)
PATHOLOGY M STAGE	Chi-square test	1 (1.00)	0.0839 (1.00)			0.544 (1.00)	0.349 (1.00)	0.0495 (1.00)	0.0666 (1.00)	0.000247 (0.0277)	0.113 (1.00)
GENDER	Chi-square test	0.000202 (0.0228)	3.29e-05 (0.00378)	0.00386 (0.409)	0.286 (1.00)	0.0195 (1.00)	0.00772 (0.803)	0.265 (1.00)	0.435 (1.00)	0.00364 (0.394)	0.553 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.118 (1.00)	0.0984 (1.00)			0.0681 (1.00)	0.0915 (1.00)	0.131 (1.00)	0.841 (1.00)	0.413 (1.00)	0.0413 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Chi-square test	0.734 (1.00)	0.704 (1.00)	0.0135 (1.00)	0.141 (1.00)	0.292 (1.00)	0.469 (1.00)	0.213 (1.00)	0.144 (1.00)	0.188 (1.00)	0.166 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.0153 (1.00)	0.213 (1.00)	0.283 (1.00)	0.507 (1.00)	0.0246 (1.00)	0.103 (1.00)	0.396 (1.00)	0.356 (1.00)	0.13 (1.00)	0.0568 (1.00)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.0449 (1.00)	0.0458 (1.00)	0.502 (1.00)	0.844 (1.00)	0.0595 (1.00)	0.0837 (1.00)	0.0899 (1.00)	0.152 (1.00)	0.000943 (0.105)	0.45 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.0576 (1.00)	0.000186 (0.0212)	0.038 (1.00)	0.549 (1.00)	0.189 (1.00)	0.373 (1.00)	0.124 (1.00)	0.943 (1.00)	0.0727 (1.00)	0.249 (1.00)

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

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

Cluster Labels	1	2	3
Number of samples	191	144	38

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

P value = 0.0222 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	368	138	0.1 - 210.9 (15.8)
subtype1	191	83	0.1 - 142.5 (15.2)
subtype2	141	42	0.1 - 210.9 (18.6)
subtype3	36	13	0.1 - 76.5 (13.2)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.399 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	373	60.7 (12.3)
subtype1	191	60.0 (11.6)
subtype2	144	61.8 (13.0)
subtype3	38	60.7 (12.6)

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

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

P value = 0.0326 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	24	58	59	174	6
subtype1	9	28	27	100	3
subtype2	12	25	25	57	0
subtype3	3	5	7	17	3

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 = 0.239 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	37	95	76	118
subtype1	13	50	41	64
subtype2	20	38	24	41
subtype3	4	7	11	13

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 = 0.0212 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	125	45	117	5
subtype1	57	19	72	3
subtype2	55	20	32	0
subtype3	13	6	13	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 = 1 (Fisher's exact test), Q value = 1

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

nPatients	M0	MX
ALL	89	28
subtype1	37	12
subtype2	42	13
subtype3	10	3

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 = 0.000202 (Fisher's exact test), Q value = 0.023

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

nPatients	FEMALE	MALE
ALL	107	266
subtype1	37	154
subtype2	56	88
subtype3	14	24

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 = 0.118 (Chi-square test), Q value = 1

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

nPatients	HEAD AND NECK SQUAMOUS CELL CARCINOMA	HEAD AND NECK SQUAMOUS CELL CARCINOMA SPINDLE CELL VARIANT	HEAD AND NECK SQUAMOUS CELL CARCINOMA BASALOID TYPE
ALL	368	1	4
subtype1	190	1	0
subtype2	140	0	4
subtype3	38	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 = 0.734 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	74	299
subtype1	37	154
subtype2	31	113
subtype3	6	32

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0153 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	209	47.5 (38.8)
subtype1	113	53.9 (32.0)
subtype2	75	42.8 (49.0)
subtype3	21	30.3 (20.7)

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

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0449 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	208	1966.4 (12.7)
subtype1	112	1966.4 (11.6)
subtype2	74	1964.7 (13.9)
subtype3	22	1972.4 (12.9)

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

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

P value = 0.0576 (ANOVA), Q value = 1

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	281	2.5 (4.9)
subtype1	153	3.1 (6.2)
subtype2	94	1.6 (2.3)
subtype3	34	2.4 (3.1)

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S14. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	53	67	74	53	56	61	14

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00658 (logrank test), Q value = 0.69

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	373	140	0.1 - 210.9 (15.8)
subtype1	53	17	0.1 - 142.5 (17.8)
subtype2	66	25	0.1 - 126.1 (15.5)
subtype3	74	34	0.2 - 139.4 (14.4)
subtype4	53	10	0.8 - 95.0 (21.9)
subtype5	53	22	0.1 - 156.5 (13.4)
subtype6	60	24	0.1 - 210.9 (14.3)
subtype7	14	8	1.8 - 84.5 (13.8)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 5.76e-06 (ANOVA), Q value = 0.00067

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	378	60.8 (12.3)
subtype1	53	57.8 (10.5)
subtype2	67	56.5 (13.7)
subtype3	74	64.0 (10.8)
subtype4	53	57.6 (10.5)
subtype5	56	66.4 (11.4)
subtype6	61	60.8 (13.4)
subtype7	14	64.4 (10.2)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.18 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	24	59	59	178	6
subtype1	1	8	10	29	1
subtype2	6	12	15	26	0
subtype3	3	8	8	42	2
subtype4	2	5	9	15	0
subtype5	4	14	6	27	3
subtype6	8	8	9	32	0
subtype7	0	4	2	7	0

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0207 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	37	97	77	120
subtype1	3	12	13	21
subtype2	8	21	21	10
subtype3	5	13	14	32
subtype4	6	13	6	8
subtype5	4	16	10	24
subtype6	11	16	11	20
subtype7	0	6	2	5

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0983 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	126	45	120	5
subtype1	24	6	16	1
subtype2	22	7	23	0
subtype3	17	6	32	2
subtype4	11	8	11	0
subtype5	27	7	9	2
subtype6	22	9	23	0
subtype7	3	2	6	0

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0839 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	90	28
subtype1	9	6
subtype2	17	3
subtype3	12	5
subtype4	12	5
subtype5	11	7
subtype6	25	2
subtype7	4	0

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 3.29e-05 (Chi-square test), Q value = 0.0038

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	109	269
subtype1	10	43
subtype2	22	45
subtype3	17	57
subtype4	6	47
subtype5	29	27
subtype6	23	38
subtype7	2	12

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0984 (Chi-square test), Q value = 1

Table S22. 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	373	1	4
subtype1	52	0	1
subtype2	66	1	0
subtype3	74	0	0
subtype4	50	0	3
subtype5	56	0	0
subtype6	61	0	0
subtype7	14	0	0

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

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

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	76	302
subtype1	10	43
subtype2	12	55
subtype3	16	58
subtype4	13	40
subtype5	7	49
subtype6	15	46
subtype7	3	11

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.213 (ANOVA), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	213	47.1 (38.6)
subtype1	37	52.0 (29.7)
subtype2	25	33.4 (15.5)
subtype3	53	54.8 (32.2)
subtype4	26	39.0 (41.7)
subtype5	22	50.6 (60.2)
subtype6	37	48.2 (49.0)
subtype7	13	35.8 (21.4)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0458 (ANOVA), Q value = 1

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	212	1966.4 (12.6)
subtype1	30	1967.1 (10.1)
subtype2	31	1972.4 (12.9)
subtype3	51	1966.0 (12.1)
subtype4	29	1965.6 (11.3)
subtype5	24	1961.3 (14.6)
subtype6	38	1966.6 (13.4)
subtype7	9	1961.3 (12.7)

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.000186 (ANOVA), Q value = 0.021

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	285	2.5 (4.9)
subtype1	46	1.3 (2.5)
subtype2	54	1.7 (2.2)
subtype3	57	5.3 (9.1)
subtype4	24	1.9 (2.3)
subtype5	44	1.2 (2.2)
subtype6	47	2.6 (3.7)
subtype7	13	2.2 (2.8)

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S27. 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 = 0.234 (logrank test), Q value = 1

Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	212	115	0.1 - 210.9 (17.1)
subtype1	55	33	1.5 - 126.1 (15.7)
subtype2	52	23	1.4 - 139.4 (17.1)
subtype3	37	21	3.7 - 156.5 (21.8)
subtype4	68	38	0.1 - 210.9 (18.1)

Figure S25. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.691 (ANOVA), Q value = 1

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.66 (Chi-square test), Q value = 1

Table S30. 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 S27. 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 = 0.228 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	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 S28. 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 = 0.486 (Chi-square test), Q value = 1

Table S32. 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 S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0135 (Fisher's exact test), Q value = 0.41

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	56	156
subtype1	20	35
subtype2	18	34
subtype3	4	33
subtype4	14	54

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.502 (ANOVA), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	116	1964.3 (12.4)
subtype1	27	1967.4 (11.2)
subtype2	32	1964.0 (9.8)
subtype3	24	1962.6 (16.5)
subtype4	33	1963.2 (12.2)

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.038 (ANOVA), Q value = 1

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: '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 S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S36. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	17	73	122

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.388 (logrank test), Q value = 1

Table S37. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	212	115	0.1 - 210.9 (17.1)
subtype1	17	5	2.1 - 139.4 (15.9)
subtype2	73	40	1.5 - 156.5 (17.2)
subtype3	122	70	0.1 - 210.9 (16.7)

Figure S33. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.771 (ANOVA), Q value = 1

Table S38. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	212	62.1 (12.2)
subtype1	17	62.0 (8.5)
subtype2	73	61.3 (13.3)
subtype3	122	62.6 (12.0)

Figure S34. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.825 (Chi-square test), Q value = 1

Table S39. 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	0	2	1	10	0
subtype2	3	17	12	37	1
subtype3	6	20	18	70	3

Figure S35. 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 = 0.293 (Chi-square test), Q value = 1

Table S40. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	13	59	53	79
subtype1	0	3	3	7
subtype2	5	25	12	29
subtype3	8	31	38	43

Figure S36. 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 = 0.399 (Chi-square test), Q value = 1

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	72	21	79	4
subtype1	4	0	9	0
subtype2	28	9	22	1
subtype3	40	12	48	3

Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.141 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	56	156
subtype1	8	9
subtype2	19	54
subtype3	29	93

Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.844 (ANOVA), Q value = 1

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	116	1964.3 (12.4)
subtype1	11	1962.3 (7.6)
subtype2	39	1964.7 (15.5)
subtype3	66	1964.3 (11.0)

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.549 (ANOVA), Q value = 1

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	170	2.9 (5.1)
subtype1	13	3.4 (5.0)
subtype2	57	2.3 (3.7)
subtype3	100	3.1 (5.8)

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S45. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	138	111	111

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.258 (logrank test), Q value = 1

Table S46. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	356	137	0.1 - 210.9 (16.1)
subtype1	138	50	0.1 - 210.9 (17.0)
subtype2	110	48	0.2 - 142.5 (16.0)
subtype3	108	39	0.1 - 156.5 (15.0)

Figure S41. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.611 (ANOVA), Q value = 1

Table S47. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	360	60.6 (12.3)
subtype1	138	61.2 (11.1)
subtype2	111	59.7 (12.8)
subtype3	111	60.9 (13.2)

Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.101 (Chi-square test), Q value = 1

Table S48. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	23	54	58	168	6
subtype1	3	16	23	57	3
subtype2	14	16	14	58	1
subtype3	6	22	21	53	2

Figure S43. 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 = 0.437 (Chi-square test), Q value = 1

Table S49. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	35	91	74	114
subtype1	9	29	26	41
subtype2	17	32	24	31
subtype3	9	30	24	42

Figure S44. 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 = 0.182 (Chi-square test), Q value = 1

Table S50. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	118	44	114	5
subtype1	38	16	36	3
subtype2	35	12	49	1
subtype3	45	16	29	1

Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.544 (Fisher's exact test), Q value = 1

Table S51. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	83	20
subtype1	24	8
subtype2	30	5
subtype3	29	7

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0195 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	259
subtype1	28	110
subtype2	33	78
subtype3	40	71

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0681 (Chi-square test), Q value = 1

Table S53. 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	355	1	4
subtype1	134	0	4
subtype2	110	1	0
subtype3	111	0	0

Figure S48. 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 = 0.292 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	76	284
subtype1	27	111
subtype2	29	82
subtype3	20	91

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0246 (ANOVA), Q value = 1

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	205	47.5 (39.2)
subtype1	92	55.1 (43.4)
subtype2	54	45.3 (44.4)
subtype3	59	37.7 (21.2)

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0595 (ANOVA), Q value = 1

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	207	1966.2 (12.6)
subtype1	86	1963.9 (11.2)
subtype2	62	1968.8 (12.1)
subtype3	59	1966.7 (14.6)

Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.189 (ANOVA), Q value = 1

Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	271	2.5 (5.0)
subtype1	82	2.8 (6.1)
subtype2	99	3.0 (5.3)
subtype3	90	1.8 (3.1)

Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S58. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	139	82	139

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0305 (logrank test), Q value = 1

Table S59. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	356	137	0.1 - 210.9 (16.1)
subtype1	139	44	0.1 - 139.4 (17.6)
subtype2	79	32	0.1 - 210.9 (14.5)
subtype3	138	61	0.1 - 142.5 (15.4)

Figure S53. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

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

Table S60. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	360	60.6 (12.3)
subtype1	139	61.1 (10.4)
subtype2	82	62.7 (13.2)
subtype3	139	58.9 (13.2)

Figure S54. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.144 (Chi-square test), Q value = 1

Table S61. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	23	54	58	168	6
subtype1	4	19	22	59	3
subtype2	3	17	15	37	2
subtype3	16	18	21	72	1

Figure S55. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0293 (Chi-square test), Q value = 1

Table S62. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	35	91	74	114
subtype1	9	29	26	45
subtype2	4	23	14	34
subtype3	22	39	34	35

Figure S56. 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 = 0.0558 (Chi-square test), Q value = 1

Table S63. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	118	44	114	5
subtype1	43	14	36	3
subtype2	34	12	17	1
subtype3	41	18	61	1

Figure S57. 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 = 0.349 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	83	20
subtype1	24	9
subtype2	22	5
subtype3	37	6

Figure S58. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00772 (Fisher's exact test), Q value = 0.8

Table S65. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	259
subtype1	27	112
subtype2	31	51
subtype3	43	96

Figure S59. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0915 (Chi-square test), Q value = 1

Table S66. 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	355	1	4
subtype1	135	0	4
subtype2	82	0	0
subtype3	138	1	0

Figure S60. 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 = 0.469 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	76	284
subtype1	27	112
subtype2	15	67
subtype3	34	105

Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.103 (ANOVA), Q value = 1

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	205	47.5 (39.2)
subtype1	91	52.4 (34.4)
subtype2	44	37.0 (20.8)
subtype3	70	47.8 (51.2)

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0837 (ANOVA), Q value = 1

Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	207	1966.2 (12.6)
subtype1	85	1964.2 (10.7)
subtype2	45	1965.7 (15.2)
subtype3	77	1968.6 (12.7)

Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.373 (ANOVA), Q value = 1

Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	271	2.5 (5.0)
subtype1	87	2.6 (6.0)
subtype2	65	1.8 (3.4)
subtype3	119	2.9 (4.9)

Figure S64. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S71. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	103	157	117

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0652 (logrank test), Q value = 1

Table S72. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	372	140	0.1 - 210.9 (15.9)
subtype1	100	25	0.1 - 210.9 (13.1)
subtype2	156	63	0.1 - 142.5 (16.8)
subtype3	116	52	0.5 - 126.1 (16.5)

Figure S65. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.324 (ANOVA), Q value = 1

Table S73. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	377	60.8 (12.3)
subtype1	103	61.0 (12.0)
subtype2	157	61.6 (11.4)
subtype3	117	59.4 (13.5)

Figure S66. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.296 (Chi-square test), Q value = 1

Table S74. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	24	58	58	179	6
subtype1	8	17	19	43	1
subtype2	5	18	24	80	3
subtype3	11	23	15	56	2

Figure S67. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.188 (Chi-square test), Q value = 1

Table S75. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	37	97	76	120
subtype1	11	29	20	31
subtype2	11	30	33	57
subtype3	15	38	23	32

Figure S68. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.227 (Chi-square test), Q value = 1

Table S76. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	124	45	121	5
subtype1	41	15	24	1
subtype2	49	15	51	3
subtype3	34	15	46	1

Figure S69. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0495 (Fisher's exact test), Q value = 1

Table S77. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	89	28
subtype1	32	16
subtype2	29	9
subtype3	28	3

Figure S70. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

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

Table S78. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	109	268
subtype1	35	68
subtype2	39	118
subtype3	35	82

Figure S71. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.131 (Chi-square test), Q value = 1

Table S79. 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	372	1	4
subtype1	103	0	0
subtype2	152	1	4
subtype3	117	0	0

Figure S72. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.213 (Fisher's exact test), Q value = 1

Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	75	302
subtype1	15	88
subtype2	32	125
subtype3	28	89

Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.396 (ANOVA), Q value = 1

Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	213	47.4 (38.7)
subtype1	66	43.0 (38.4)
subtype2	92	51.3 (34.6)
subtype3	55	46.0 (45.0)

Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0899 (ANOVA), Q value = 1

Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	212	1966.3 (12.6)
subtype1	58	1968.5 (14.3)
subtype2	89	1964.2 (11.7)
subtype3	65	1967.4 (12.0)

Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.124 (ANOVA), Q value = 1

Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	284	2.5 (4.9)
subtype1	73	1.5 (2.5)
subtype2	113	2.8 (5.7)
subtype3	98	2.9 (5.1)

Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S84. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	14	151	212

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.348 (logrank test), Q value = 1

Table S85. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	372	140	0.1 - 210.9 (15.9)
subtype1	14	6	3.5 - 90.1 (22.5)
subtype2	149	61	0.1 - 129.2 (15.9)
subtype3	209	73	0.1 - 210.9 (15.8)

Figure S77. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.279 (ANOVA), Q value = 1

Table S86. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	377	60.8 (12.3)
subtype1	14	60.2 (14.5)
subtype2	151	59.6 (13.2)
subtype3	212	61.7 (11.4)

Figure S78. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0125 (Chi-square test), Q value = 1

Table S87. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	24	58	58	179	6
subtype1	0	6	2	6	0
subtype2	18	23	27	71	1
subtype3	6	29	29	102	5

Figure S79. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0036 (Chi-square test), Q value = 0.39

Table S88. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	37	97	76	120
subtype1	0	7	2	5
subtype2	25	42	36	38
subtype3	12	48	38	77

Figure S80. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.825 (Chi-square test), Q value = 1

Table S89. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	124	45	121	5
subtype1	7	2	4	0
subtype2	51	21	55	1
subtype3	66	22	62	4

Figure S81. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0666 (Fisher's exact test), Q value = 1

Table S90. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	89	28
subtype1	3	0
subtype2	44	8
subtype3	42	20

Figure S82. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.435 (Fisher's exact test), Q value = 1

Table S91. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	109	268
subtype1	3	11
subtype2	49	102
subtype3	57	155

Figure S83. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.841 (Chi-square test), Q value = 1

Table S92. 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	372	1	4
subtype1	14	0	0
subtype2	150	0	1
subtype3	208	1	3

Figure S84. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

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

Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	75	302
subtype1	4	10
subtype2	36	115
subtype3	35	177

Figure S85. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.356 (ANOVA), Q value = 1

Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	213	47.4 (38.7)
subtype1	9	62.2 (21.5)
subtype2	72	43.7 (39.7)
subtype3	132	48.4 (38.9)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.152 (ANOVA), Q value = 1

Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	212	1966.3 (12.6)
subtype1	7	1965.4 (12.9)
subtype2	76	1968.6 (12.5)
subtype3	129	1965.1 (12.6)

Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.943 (ANOVA), Q value = 1

Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	284	2.5 (4.9)
subtype1	9	3.0 (4.7)
subtype2	128	2.5 (4.6)
subtype3	147	2.4 (5.2)

Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S97. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	78	77	108	77

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

P value = 0.00367 (logrank test), Q value = 0.39

Table S98. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	335	125	0.1 - 210.9 (15.9)
subtype1	75	17	0.1 - 156.5 (12.9)
subtype2	77	22	0.1 - 139.4 (17.0)
subtype3	107	42	0.1 - 142.5 (19.9)
subtype4	76	44	1.0 - 210.9 (15.7)

Figure S89. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.439 (ANOVA), Q value = 1

Table S99. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	340	60.9 (12.1)
subtype1	78	60.1 (12.2)
subtype2	77	61.9 (10.7)
subtype3	108	59.7 (13.0)
subtype4	77	62.1 (12.1)

Figure S90. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.00812 (Chi-square test), Q value = 0.84

Table S100. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	23	56	55	155	4
subtype1	8	18	18	27	0
subtype2	0	5	14	32	2
subtype3	12	18	11	54	1
subtype4	3	15	12	42	1

Figure S91. 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 = 0.144 (Chi-square test), Q value = 1

Table S101. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	35	87	71	103
subtype1	11	26	18	17
subtype2	4	13	17	20
subtype3	15	23	19	40
subtype4	5	25	17	26

Figure S92. 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 = 0.0341 (Chi-square test), Q value = 1

Table S102. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	111	43	107	3
subtype1	33	12	18	0
subtype2	18	5	26	2
subtype3	41	13	31	0
subtype4	19	13	32	1

Figure S93. 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 = 0.000247 (Fisher's exact test), Q value = 0.028

Table S103. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	76	28
subtype1	31	14
subtype2	7	8
subtype3	26	0
subtype4	12	6

Figure S94. 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 = 0.00364 (Fisher's exact test), Q value = 0.39

Table S104. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	100	240
subtype1	33	45
subtype2	14	63
subtype3	26	82
subtype4	27	50

Figure S95. 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 = 0.413 (Chi-square test), Q value = 1

Table S105. 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	335	1	4
subtype1	77	0	1
subtype2	74	1	2
subtype3	108	0	0
subtype4	76	0	1

Figure S96. 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 = 0.188 (Fisher's exact test), Q value = 1

Table S106. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	58	282
subtype1	8	70
subtype2	12	65
subtype3	24	84
subtype4	14	63

Figure S97. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.13 (ANOVA), Q value = 1

Table S107. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	186	47.8 (40.0)
subtype1	43	40.7 (43.9)
subtype2	51	55.8 (36.3)
subtype3	58	51.4 (44.2)
subtype4	34	38.7 (29.8)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.000943 (ANOVA), Q value = 0.1

Table S108. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	193	1966.2 (12.7)
subtype1	40	1972.0 (12.5)
subtype2	48	1961.4 (10.0)
subtype3	67	1965.4 (13.7)
subtype4	38	1967.6 (12.0)

Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.0727 (ANOVA), Q value = 1

Table S109. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	256	2.5 (5.1)
subtype1	58	1.4 (2.0)
subtype2	52	3.7 (7.7)
subtype3	81	2.1 (3.4)
subtype4	65	3.1 (5.9)

Figure S100. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S110. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	4	20	68	46	132	70

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

P value = 0.00226 (logrank test), Q value = 0.25

Table S111. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	335	125	0.1 - 210.9 (15.9)
subtype1	4	3	3.5 - 13.9 (10.8)
subtype2	18	9	0.1 - 210.9 (13.8)
subtype3	68	11	0.1 - 156.5 (16.5)
subtype4	45	24	1.0 - 126.1 (20.5)
subtype5	130	49	0.1 - 129.2 (16.1)
subtype6	70	29	0.8 - 142.5 (14.9)

Figure S101. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.599 (ANOVA), Q value = 1

Table S112. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	340	60.9 (12.1)
subtype1	4	53.2 (15.2)
subtype2	20	63.6 (15.6)
subtype3	68	60.7 (11.2)
subtype4	46	61.0 (12.1)
subtype5	132	60.1 (13.2)
subtype6	70	62.0 (9.5)

Figure S102. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.477 (Chi-square test), Q value = 1

Table S113. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVB
ALL	23	56	55	155	4
subtype1	0	1	1	2	0
subtype2	0	3	3	11	0
subtype3	4	8	13	21	1
subtype4	1	10	5	26	1
subtype5	16	24	23	60	0
subtype6	2	10	10	35	2

Figure S103. 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 = 0.0919 (Chi-square test), Q value = 1

Table S114. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	35	87	71	103
subtype1	0	1	1	2
subtype2	0	6	2	10
subtype3	7	14	10	18
subtype4	2	11	8	22
subtype5	21	40	33	29
subtype6	5	15	17	22

Figure S104. 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 = 0.391 (Chi-square test), Q value = 1

Table S115. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	111	43	107	3
subtype1	2	0	2	0
subtype2	8	1	5	0
subtype3	21	12	11	1
subtype4	13	6	16	0
subtype5	45	18	48	0
subtype6	22	6	25	2

Figure S105. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.113 (Chi-square test), Q value = 1

Table S116. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	76	28
subtype1	0	0
subtype2	6	5
subtype3	16	11
subtype4	6	1
subtype5	39	8
subtype6	9	3

Figure S106. 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 = 0.553 (Chi-square test), Q value = 1

Table S117. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	100	240
subtype1	2	2
subtype2	6	14
subtype3	18	50
subtype4	10	36
subtype5	45	87
subtype6	19	51

Figure S107. 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 = 0.0413 (Chi-square test), Q value = 1

Table S118. 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	335	1	4
subtype1	4	0	0
subtype2	19	1	0
subtype3	66	0	2
subtype4	46	0	0
subtype5	131	0	1
subtype6	69	0	1

Figure S108. 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 = 0.166 (Chi-square test), Q value = 1

Table S119. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	58	282
subtype1	0	4
subtype2	0	20
subtype3	11	57
subtype4	12	34
subtype5	24	108
subtype6	11	59

Figure S109. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0568 (ANOVA), Q value = 1

Table S120. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	186	47.8 (40.0)
subtype1	1	75.0 (NA)
subtype2	9	28.2 (18.9)
subtype3	43	43.7 (49.3)
subtype4	22	42.7 (18.5)
subtype5	62	44.1 (42.0)
subtype6	49	61.4 (35.8)

Figure S110. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.45 (ANOVA), Q value = 1

Table S121. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	193	1966.2 (12.7)
subtype1	1	1969.0 (NA)
subtype2	8	1964.1 (15.1)
subtype3	42	1967.0 (13.1)
subtype4	26	1965.3 (14.7)
subtype5	70	1968.0 (13.0)
subtype6	46	1963.7 (10.2)

Figure S111. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.249 (ANOVA), Q value = 1

Table S122. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	256	2.5 (5.1)
subtype1	3	6.3 (7.1)
subtype2	15	2.0 (3.2)
subtype3	41	1.3 (2.1)
subtype4	36	2.6 (4.3)
subtype5	110	2.4 (4.7)
subtype6	51	3.6 (7.7)

Figure S112. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Cluster data file = HNSC-TP.mergedcluster.txt
Clinical data file = HNSC-TP.merged_data.txt
Number of patients = 379
Number of clustering approaches = 10
Number of selected clinical features = 12
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

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

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

[6] 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)

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

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