Correlation between aggregated molecular cancer subtypes and selected clinical features

Head and Neck Squamous Cell Carcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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/C1TQ604T

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 410 patients, 5 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 do not correlate to any clinical features.
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 correlate to 'GENDER'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY.T.STAGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

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, 5 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	0.00803 (0.835)	0.0587 (1.00)	0.193 (1.00)	0.343 (1.00)	0.182 (1.00)	0.0629 (1.00)	0.0744 (1.00)	0.0205 (1.00)	0.0211 (1.00)	0.216 (1.00)
AGE	ANOVA	0.0407 (1.00)	4.26e-06 (0.000494)	0.683 (1.00)	0.767 (1.00)	0.697 (1.00)	0.427 (1.00)	0.192 (1.00)	0.11 (1.00)	0.562 (1.00)	0.942 (1.00)
NEOPLASM DISEASESTAGE	Chi-square test	0.664 (1.00)	0.485 (1.00)	0.66 (1.00)	0.825 (1.00)	0.128 (1.00)	0.488 (1.00)	0.445 (1.00)	0.0289 (1.00)	0.617 (1.00)	0.0873 (1.00)
PATHOLOGY T STAGE	Chi-square test	0.441 (1.00)	0.0283 (1.00)	0.228 (1.00)	0.293 (1.00)	0.352 (1.00)	0.393 (1.00)	0.187 (1.00)	0.00171 (0.191)	0.221 (1.00)	0.0143 (1.00)
PATHOLOGY N STAGE	Chi-square test	0.0749 (1.00)	0.283 (1.00)	0.486 (1.00)	0.399 (1.00)	0.176 (1.00)	0.0245 (1.00)	0.282 (1.00)	0.471 (1.00)	0.521 (1.00)	0.942 (1.00)
PATHOLOGY M STAGE	Fisher's exact test	0.831 (1.00)	0.0155 (1.00)			0.275 (1.00)	0.183 (1.00)	0.0635 (1.00)	0.0274 (1.00)	0.0235 (1.00)	0.00711 (0.754)
GENDER	Fisher's exact test	0.0241 (1.00)	2e-05 (0.0023)	0.00386 (0.424)	0.286 (1.00)	0.00546 (0.59)	0.000535 (0.0605)	0.43 (1.00)	0.374 (1.00)	0.0657 (1.00)	0.286 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.186 (1.00)	0.0737 (1.00)			0.0198 (1.00)	0.00873 (0.9)	0.171 (1.00)	0.346 (1.00)	0.488 (1.00)	0.591 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.186 (1.00)	0.448 (1.00)	0.00738 (0.775)	0.235 (1.00)	0.327 (1.00)	0.0282 (1.00)	0.0806 (1.00)	0.163 (1.00)	0.00492 (0.536)	0.0638 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.0186 (1.00)	0.101 (1.00)	0.283 (1.00)	0.507 (1.00)	0.0243 (1.00)	0.168 (1.00)	0.554 (1.00)	0.389 (1.00)	0.00354 (0.393)	0.684 (1.00)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.006 (0.642)	0.00979 (0.999)	0.502 (1.00)	0.844 (1.00)	0.26 (1.00)	0.101 (1.00)	0.115 (1.00)	0.0633 (1.00)	0.0522 (1.00)	0.48 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.0191 (1.00)	0.000123 (0.014)	0.038 (1.00)	0.549 (1.00)	0.165 (1.00)	0.215 (1.00)	0.104 (1.00)	0.564 (1.00)	0.0176 (1.00)	0.789 (1.00)

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

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

Cluster Labels	1	2	3	4
Number of samples	144	81	146	33

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

P value = 0.00803 (logrank test), Q value = 0.83

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	399	152	0.1 - 211.0 (16.1)
subtype1	144	69	0.1 - 139.4 (15.1)
subtype2	80	30	0.1 - 142.5 (16.9)
subtype3	143	42	0.1 - 211.0 (19.8)
subtype4	32	11	0.1 - 76.5 (13.9)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.0407 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	403	60.8 (12.2)
subtype1	144	58.9 (12.7)
subtype2	81	61.5 (10.1)
subtype3	145	61.3 (12.9)
subtype4	33	65.2 (10.3)

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 = 0.664 (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	62	66	189	8
subtype1	9	17	22	77	4
subtype2	3	11	11	41	1
subtype3	9	28	26	57	2
subtype4	3	6	7	14	1

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 = 0.441 (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	39	103	84	128
subtype1	14	34	27	55
subtype2	5	20	20	23
subtype3	16	42	26	41
subtype4	4	7	11	9

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 = 0.0749 (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	134	52	125	7
subtype1	39	18	58	3
subtype2	23	8	28	1
subtype3	59	20	30	2
subtype4	13	6	9	1

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 = 0.831 (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	103	43
subtype1	31	16
subtype2	15	5
subtype3	44	18
subtype4	13	4

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 = 0.0241 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	116	288
subtype1	34	110
subtype2	17	64
subtype3	52	94
subtype4	13	20

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 = 0.186 (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	397	1	6
subtype1	144	0	0
subtype2	78	1	2
subtype3	142	0	4
subtype4	33	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 = 0.186 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	79	325
subtype1	35	109
subtype2	13	68
subtype3	28	118
subtype4	3	30

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 = 0.0186 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	226	47.1 (38.2)
subtype1	82	50.0 (40.4)
subtype2	50	58.2 (35.3)
subtype3	75	40.1 (38.4)
subtype4	19	32.7 (25.1)

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 = 0.006 (ANOVA), Q value = 0.64

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

	nPatients	Mean (Std.Dev)
ALL	222	1966.6 (12.7)
subtype1	80	1969.2 (11.8)
subtype2	50	1963.3 (11.4)
subtype3	74	1964.5 (14.1)
subtype4	18	1972.2 (11.1)

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 = 0.0191 (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	309	2.3 (4.7)
subtype1	118	3.4 (5.8)
subtype2	62	2.1 (5.7)
subtype3	98	1.4 (2.2)
subtype4	31	1.8 (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	56	78	79	57	58	62	19

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0587 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	404	154	0.1 - 211.0 (16.2)
subtype1	56	18	0.1 - 142.5 (17.5)
subtype2	77	32	0.1 - 126.2 (15.9)
subtype3	79	36	0.2 - 139.4 (14.8)
subtype4	57	12	0.1 - 94.6 (21.1)
subtype5	56	22	0.1 - 169.4 (16.1)
subtype6	60	24	0.1 - 211.0 (15.8)
subtype7	19	10	1.8 - 84.5 (16.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 4.26e-06 (ANOVA), Q value = 0.00049

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

	nPatients	Mean (Std.Dev)
ALL	408	60.8 (12.2)
subtype1	56	58.1 (10.3)
subtype2	78	57.2 (13.7)
subtype3	79	63.5 (10.9)
subtype4	57	57.5 (10.3)
subtype5	58	66.4 (11.4)
subtype6	61	60.6 (13.2)
subtype7	19	66.5 (11.0)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.485 (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	63	66	193	8
subtype1	1	9	10	30	1
subtype2	9	13	16	31	1
subtype3	2	8	11	45	2
subtype4	2	5	9	18	1
subtype5	3	14	7	28	3
subtype6	6	11	11	30	0
subtype7	1	3	2	11	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 = 0.0283 (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	39	105	85	130
subtype1	3	13	13	22
subtype2	11	24	23	13
subtype3	4	15	16	34
subtype4	7	13	8	9
subtype5	3	16	11	25
subtype6	10	20	10	19
subtype7	1	4	4	8

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 = 0.283 (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	135	52	128	7
subtype1	24	6	17	1
subtype2	28	8	25	1
subtype3	17	9	34	2
subtype4	11	8	13	1
subtype5	27	7	11	2
subtype6	23	10	22	0
subtype7	5	4	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 = 0.0155 (Chi-square test), Q value = 1

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

nPatients	M0	MX
ALL	104	43
subtype1	11	8
subtype2	20	8
subtype3	13	7
subtype4	13	8
subtype5	12	10
subtype6	29	2
subtype7	6	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 = 2e-05 (Chi-square test), Q value = 0.0023

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

nPatients	FEMALE	MALE
ALL	118	291
subtype1	11	45
subtype2	29	49
subtype3	17	62
subtype4	6	51
subtype5	30	28
subtype6	20	42
subtype7	5	14

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0737 (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	402	1	6
subtype1	55	0	1
subtype2	77	1	0
subtype3	78	0	1
subtype4	53	0	4
subtype5	58	0	0
subtype6	62	0	0
subtype7	19	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 = 0.448 (Chi-square test), Q value = 1

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

nPatients	NO	YES
ALL	81	328
subtype1	11	45
subtype2	13	65
subtype3	19	60
subtype4	13	44
subtype5	6	52
subtype6	14	48
subtype7	5	14

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.101 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	230	46.7 (38.0)
subtype1	39	50.6 (29.9)
subtype2	31	35.4 (19.7)
subtype3	57	53.4 (30.4)
subtype4	28	38.3 (40.3)
subtype5	19	57.6 (62.9)
subtype6	40	48.9 (49.1)
subtype7	16	31.5 (24.2)

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

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.00979 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	226	1966.5 (12.7)
subtype1	33	1966.8 (9.9)
subtype2	35	1972.1 (12.7)
subtype3	53	1966.5 (12.0)
subtype4	30	1966.5 (12.2)
subtype5	19	1960.2 (13.5)
subtype6	41	1967.2 (13.6)
subtype7	15	1959.3 (13.0)

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 = 0.000123 (ANOVA), Q value = 0.014

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

	nPatients	Mean (Std.Dev)
ALL	313	2.3 (4.7)
subtype1	48	1.2 (2.5)
subtype2	64	1.6 (2.2)
subtype3	62	5.0 (8.8)
subtype4	26	2.0 (2.3)
subtype5	47	1.2 (2.1)
subtype6	48	2.4 (3.6)
subtype7	18	1.8 (2.5)

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 = 0.193 (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	117	0.1 - 211.0 (18.2)
subtype1	55	33	1.5 - 126.2 (15.8)
subtype2	52	23	2.1 - 139.4 (17.9)
subtype3	37	21	3.7 - 156.5 (25.4)
subtype4	68	40	0.1 - 211.0 (19.5)

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 = 0.683 (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 = 0.00738 (Fisher's exact test), Q value = 0.42

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

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

Figure 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 = 0.78

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 = 0.343 (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	117	0.1 - 211.0 (18.2)
subtype1	17	5	2.1 - 139.4 (20.1)
subtype2	73	40	1.5 - 156.5 (17.2)
subtype3	122	72	0.1 - 211.0 (18.5)

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 = 0.767 (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	62.1 (8.4)
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 = 0.235 (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	61	151
subtype1	8	9
subtype2	20	53
subtype3	33	89

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	157	120	122

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.182 (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	394	151	0.1 - 211.0 (16.7)
subtype1	156	53	0.1 - 211.0 (17.2)
subtype2	118	52	0.2 - 142.5 (16.1)
subtype3	120	46	0.1 - 169.4 (15.5)

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 = 0.697 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	398	60.8 (12.2)
subtype1	157	61.1 (10.9)
subtype2	119	60.0 (12.8)
subtype3	122	61.1 (13.1)

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 = 0.128 (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	24	61	65	187	8
subtype1	3	18	27	68	4
subtype2	14	20	16	60	2
subtype3	7	23	22	59	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 = 0.352 (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	38	103	83	126
subtype1	11	33	31	48
subtype2	17	38	26	32
subtype3	10	32	26	46

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 = 0.176 (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	131	51	125	7
subtype1	42	19	43	4
subtype2	39	14	51	2
subtype3	50	18	31	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 = 0.275 (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	100	40
subtype1	32	17
subtype2	36	9
subtype3	32	14

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 = 0.00546 (Fisher's exact test), Q value = 0.59

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

nPatients	FEMALE	MALE
ALL	112	287
subtype1	33	124
subtype2	32	88
subtype3	47	75

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 = 0.0198 (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	392	1	6
subtype1	151	0	6
subtype2	119	1	0
subtype3	122	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 = 0.327 (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	81	318
subtype1	32	125
subtype2	29	91
subtype3	20	102

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 = 0.0243 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	226	46.9 (38.2)
subtype1	103	53.8 (41.7)
subtype2	60	45.0 (43.6)
subtype3	63	37.5 (21.3)

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

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.26 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	224	1966.5 (12.7)
subtype1	96	1965.1 (11.8)
subtype2	66	1968.4 (11.8)
subtype3	62	1966.6 (14.7)

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 = 0.165 (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	306	2.3 (4.7)
subtype1	97	2.6 (5.7)
subtype2	108	2.8 (5.1)
subtype3	101	1.6 (3.0)

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	124	136	139

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0629 (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	394	151	0.1 - 211.0 (16.7)
subtype1	122	55	0.4 - 142.5 (16.1)
subtype2	134	53	0.1 - 211.0 (14.8)
subtype3	138	43	0.1 - 139.4 (18.1)

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 = 0.427 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	398	60.8 (12.2)
subtype1	123	59.9 (12.9)
subtype2	136	61.8 (13.0)
subtype3	139	60.5 (10.5)

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 = 0.488 (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	24	61	65	187	8
subtype1	13	20	18	65	2
subtype2	7	23	26	65	2
subtype3	4	18	21	57	4

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 = 0.393 (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	38	103	83	126
subtype1	17	38	30	34
subtype2	11	33	27	53
subtype3	10	32	26	39

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 = 0.0245 (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	131	51	125	7
subtype1	39	15	55	2
subtype2	57	21	31	1
subtype3	35	15	39	4

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 = 0.183 (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	100	40
subtype1	38	9
subtype2	37	17
subtype3	25	14

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 = 0.000535 (Fisher's exact test), Q value = 0.061

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

nPatients	FEMALE	MALE
ALL	112	287
subtype1	34	90
subtype2	53	83
subtype3	25	114

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 = 0.00873 (Chi-square test), Q value = 0.9

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	392	1	6
subtype1	123	1	0
subtype2	136	0	0
subtype3	133	0	6

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 = 0.0282 (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	81	318
subtype1	32	92
subtype2	18	118
subtype3	31	108

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 = 0.168 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	226	46.9 (38.2)
subtype1	62	45.0 (43.0)
subtype2	72	41.5 (37.7)
subtype3	92	52.5 (34.8)

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

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.101 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	224	1966.5 (12.7)
subtype1	68	1968.4 (12.0)
subtype2	69	1967.4 (14.3)
subtype3	87	1964.3 (11.6)

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 = 0.215 (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	306	2.3 (4.7)
subtype1	112	2.7 (4.9)
subtype2	108	1.7 (3.2)
subtype3	86	2.6 (6.0)

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	113	165	130

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0744 (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	403	154	0.1 - 211.0 (16.2)
subtype1	111	28	0.1 - 211.0 (13.6)
subtype2	164	64	0.1 - 142.5 (17.1)
subtype3	128	62	0.5 - 169.4 (17.1)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.192 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	407	60.9 (12.2)
subtype1	113	61.9 (11.8)
subtype2	165	61.4 (11.2)
subtype3	129	59.3 (13.6)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.445 (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	62	65	194	8
subtype1	8	19	22	47	2
subtype2	5	20	24	83	4
subtype3	11	23	19	64	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 = 0.187 (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	39	105	84	130
subtype1	12	30	25	33
subtype2	10	34	34	60
subtype3	17	41	25	37

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 = 0.282 (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	133	52	129	7
subtype1	43	17	29	2
subtype2	53	16	50	4
subtype3	37	19	50	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 = 0.0635 (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	103	43
subtype1	39	19
subtype2	31	18
subtype3	33	6

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.43 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	118	290
subtype1	36	77
subtype2	42	123
subtype3	40	90

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.171 (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	401	1	6
subtype1	113	0	0
subtype2	159	1	5
subtype3	129	0	1

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 = 0.0806 (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	80	328
subtype1	15	98
subtype2	33	132
subtype3	32	98

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

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.554 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	230	46.9 (38.1)
subtype1	70	43.9 (38.7)
subtype2	99	50.0 (33.6)
subtype3	61	45.5 (43.9)

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

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.115 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	226	1966.5 (12.7)
subtype1	62	1968.3 (14.2)
subtype2	93	1964.4 (11.4)
subtype3	71	1967.6 (12.6)

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 = 0.104 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	312	2.3 (4.7)
subtype1	85	1.4 (2.4)
subtype2	117	2.6 (5.6)
subtype3	110	2.8 (4.9)

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
Number of samples	165	243

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0205 (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	403	154	0.1 - 211.0 (16.2)
subtype1	162	73	0.1 - 169.4 (15.9)
subtype2	241	81	0.1 - 211.0 (17.0)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.11 (t-test), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	407	60.9 (12.2)
subtype1	164	59.6 (13.3)
subtype2	243	61.7 (11.3)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0289 (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	62	65	194	8
subtype1	17	28	31	75	2
subtype2	7	34	34	119	6

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 = 0.00171 (Chi-square test), Q value = 0.19

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

nPatients	T1	T2	T3	T4
ALL	39	105	84	130
subtype1	24	49	41	40
subtype2	15	56	43	90

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 = 0.471 (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	133	52	129	7
subtype1	53	25	61	2
subtype2	80	27	68	5

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 = 0.0274 (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	103	43
subtype1	50	12
subtype2	53	31

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.374 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	118	290
subtype1	52	113
subtype2	66	177

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.346 (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	401	1	6
subtype1	164	0	1
subtype2	237	1	5

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 = 0.163 (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	80	328
subtype1	38	127
subtype2	42	201

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.389 (t-test), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	230	46.9 (38.1)
subtype1	79	43.9 (39.4)
subtype2	151	48.5 (37.4)

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

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0633 (t-test), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	226	1966.5 (12.7)
subtype1	81	1968.5 (12.4)
subtype2	145	1965.3 (12.7)

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 = 0.564 (t-test), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	312	2.3 (4.7)
subtype1	143	2.5 (4.5)
subtype2	169	2.2 (4.9)

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
Number of samples	115	137	119

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

P value = 0.0211 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	366	139	0.1 - 211.0 (16.7)
subtype1	112	29	0.1 - 169.4 (13.1)
subtype2	136	49	0.1 - 142.5 (20.6)
subtype3	118	61	0.1 - 211.0 (16.9)

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 = 0.562 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	370	60.9 (12.0)
subtype1	114	60.5 (12.2)
subtype2	137	60.5 (11.8)
subtype3	119	61.9 (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 = 0.617 (Chi-square test), Q value = 1

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	60	62	170	6
subtype1	7	18	27	47	2
subtype2	8	18	16	62	2
subtype3	8	24	19	61	2

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 = 0.221 (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	37	95	79	113
subtype1	13	32	29	29
subtype2	12	23	27	45
subtype3	12	40	23	39

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 = 0.521 (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	120	50	115	5
subtype1	40	17	33	2
subtype2	46	13	37	2
subtype3	34	20	45	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 = 0.0235 (Fisher's exact test), Q value = 1

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

nPatients	M0	MX
ALL	90	43
subtype1	43	30
subtype2	25	4
subtype3	22	9

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 = 0.0657 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	109	262
subtype1	41	74
subtype2	31	106
subtype3	37	82

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 = 0.488 (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	364	1	6
subtype1	111	1	3
subtype2	135	0	2
subtype3	118	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 = 0.00492 (Fisher's exact test), Q value = 0.54

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

nPatients	NO	YES
ALL	62	309
subtype1	9	106
subtype2	30	107
subtype3	23	96

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 = 0.00354 (ANOVA), Q value = 0.39

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

	nPatients	Mean (Std.Dev)
ALL	203	47.3 (39.2)
subtype1	60	35.4 (19.8)
subtype2	87	57.0 (50.0)
subtype3	56	44.8 (32.0)

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 = 0.0522 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	207	1966.4 (12.7)
subtype1	55	1969.7 (12.8)
subtype2	88	1964.4 (13.0)
subtype3	64	1966.3 (11.8)

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 = 0.0176 (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	284	2.3 (4.9)
subtype1	92	1.6 (2.5)
subtype2	94	2.0 (3.9)
subtype3	98	3.4 (6.8)

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
Number of samples	140	180	51

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

P value = 0.216 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	366	139	0.1 - 211.0 (16.7)
subtype1	137	57	0.1 - 169.4 (16.1)
subtype2	179	58	0.1 - 156.5 (17.0)
subtype3	50	24	0.4 - 211.0 (16.4)

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 = 0.942 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	370	60.9 (12.0)
subtype1	139	60.8 (13.0)
subtype2	180	60.9 (10.7)
subtype3	51	61.5 (13.8)

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 = 0.0873 (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	60	62	170	6
subtype1	16	27	24	62	1
subtype2	7	26	29	79	3
subtype3	0	7	9	29	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 = 0.0143 (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	37	95	79	113
subtype1	20	44	33	33
subtype2	15	41	37	54
subtype3	2	10	9	26

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 = 0.942 (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	120	50	115	5
subtype1	49	19	49	1
subtype2	55	25	49	3
subtype3	16	6	17	1

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 = 0.00711 (Fisher's exact test), Q value = 0.75

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

nPatients	M0	MX
ALL	90	43
subtype1	43	12
subtype2	37	17
subtype3	10	14

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 = 0.286 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	109	262
subtype1	46	94
subtype2	46	134
subtype3	17	34

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 = 0.591 (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	364	1	6
subtype1	138	1	1
subtype2	176	0	4
subtype3	50	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 = 0.0638 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	62	309
subtype1	25	115
subtype2	34	146
subtype3	3	48

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 = 0.684 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	203	47.3 (39.2)
subtype1	66	44.2 (42.1)
subtype2	115	49.4 (39.9)
subtype3	22	45.6 (25.3)

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 = 0.48 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	207	1966.4 (12.7)
subtype1	74	1967.5 (13.0)
subtype2	116	1965.4 (12.6)
subtype3	17	1967.9 (13.0)

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 = 0.789 (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	284	2.3 (4.9)
subtype1	119	2.3 (4.6)
subtype2	122	2.2 (5.2)
subtype3	43	2.8 (4.4)

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 = 410
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[8] 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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