Correlate_Clinical_vs_Molecular_Signatures

Head and Neck Squamous Cell Carcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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 (2013): Correlate_Clinical_vs_Molecular_Signatures. Broad Institute of MIT and Harvard. doi:10.7908/C1222RZS

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 8 different clustering approaches and 12 clinical features across 325 patients, 4 significant findings detected with P value < 0.05 and Q value < 0.25.

7 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death' and 'TOBACCOSMOKINGHISTORYINDICATOR'.
8 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE' and 'TOBACCOSMOKINGHISTORYINDICATOR'.
CNMF clustering analysis on RPPA data identified 3 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.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 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, 4 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
Time to Death	logrank test	0.000534 (0.047)	0.0554 (1.00)	0.109 (1.00)	0.265 (1.00)	0.448 (1.00)	0.205 (1.00)	0.703 (1.00)	0.707 (1.00)
AGE	ANOVA	0.167 (1.00)	0.000106 (0.00964)	0.434 (1.00)	0.303 (1.00)	0.925 (1.00)	0.383 (1.00)	0.838 (1.00)	0.35 (1.00)
GENDER	Fisher's exact test	0.661 (1.00)	0.0445 (1.00)	0.139 (1.00)	0.842 (1.00)	0.188 (1.00)	0.00985 (0.847)	0.2 (1.00)	0.522 (1.00)
PATHOLOGY T	Chi-square test	0.459 (1.00)	0.0887 (1.00)	0.216 (1.00)	0.194 (1.00)	0.225 (1.00)	0.298 (1.00)	0.289 (1.00)	0.0851 (1.00)
PATHOLOGY N	Chi-square test	0.0124 (1.00)	0.439 (1.00)	0.0063 (0.548)	0.0534 (1.00)	0.194 (1.00)	0.0772 (1.00)	0.172 (1.00)	0.535 (1.00)
PATHOLOGICSPREAD(M)	Fisher's exact test	0.397 (1.00)						0.422 (1.00)	0.733 (1.00)
TUMOR STAGE	Chi-square test	0.386 (1.00)	0.272 (1.00)	0.0532 (1.00)	0.103 (1.00)	0.0364 (1.00)	0.39 (1.00)	0.621 (1.00)	0.274 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.795 (1.00)	0.715 (1.00)	0.301 (1.00)	0.595 (1.00)	0.413 (1.00)	0.546 (1.00)	0.536 (1.00)	0.283 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.0178 (1.00)	0.0491 (1.00)	0.658 (1.00)	0.752 (1.00)	0.0646 (1.00)	0.0544 (1.00)	0.494 (1.00)	0.51 (1.00)
STOPPEDSMOKINGYEAR	ANOVA	0.321 (1.00)	0.19 (1.00)	0.79 (1.00)	0.37 (1.00)	0.587 (1.00)	0.724 (1.00)	0.306 (1.00)	0.236 (1.00)
TOBACCOSMOKINGHISTORYINDICATOR	Chi-square test	0.000245 (0.022)	0.00052 (0.0462)	0.167 (1.00)	0.433 (1.00)	0.42 (1.00)	0.129 (1.00)	0.278 (1.00)	0.238 (1.00)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.791 (1.00)	0.0163 (1.00)	0.539 (1.00)	0.746 (1.00)	0.133 (1.00)	0.125 (1.00)	0.766 (1.00)	0.464 (1.00)

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

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

Cluster Labels	1	2	3	4	5	6	7
Number of samples	66	41	46	84	21	10	51

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

P value = 0.000534 (logrank test), Q value = 0.047

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	315	121	0.1 - 210.9 (14.3)
subtype1	65	34	0.2 - 135.3 (12.7)
subtype2	41	18	1.0 - 126.1 (16.6)
subtype3	46	16	1.0 - 142.5 (16.9)
subtype4	84	18	0.8 - 210.9 (17.5)
subtype5	20	8	0.1 - 84.2 (12.7)
subtype6	10	3	1.5 - 58.4 (25.5)
subtype7	49	24	0.1 - 126.1 (12.5)

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 = 0.167 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	318	61.2 (12.1)
subtype1	65	60.9 (11.4)
subtype2	41	56.8 (15.7)
subtype3	46	62.9 (9.8)
subtype4	84	62.2 (13.0)
subtype5	21	61.2 (8.6)
subtype6	10	56.8 (8.8)
subtype7	51	62.5 (11.1)

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 'GENDER'

P value = 0.661 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	90	229
subtype1	17	49
subtype2	9	32
subtype3	11	35
subtype4	30	54
subtype5	6	15
subtype6	2	8
subtype7	15	36

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

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

P value = 0.459 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	28	79	65	104
subtype1	6	11	11	26
subtype2	4	10	6	16
subtype3	2	10	14	14
subtype4	11	25	12	22
subtype5	1	7	6	4
subtype6	0	3	3	3
subtype7	4	13	13	19

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

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

P value = 0.0124 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	105	36	99	5
subtype1	14	5	29	2
subtype2	14	5	9	1
subtype3	15	3	17	1
subtype4	38	8	14	0
subtype5	5	3	9	0
subtype6	1	3	4	1
subtype7	18	9	17	0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.397 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients	M0	MX
ALL	57	6
subtype1	9	2
subtype2	5	0
subtype3	7	1
subtype4	22	1
subtype5	1	1
subtype6	1	0
subtype7	12	1

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.386 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	20	49	45	157
subtype1	5	5	5	39
subtype2	4	7	6	19
subtype3	2	6	7	24
subtype4	5	16	15	30
subtype5	0	2	5	11
subtype6	0	1	2	6
subtype7	4	12	5	28

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

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

P value = 0.795 (Chi-square test), Q value = 1

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

nPatients	NO	YES
ALL	76	243
subtype1	19	47
subtype2	9	32
subtype3	12	34
subtype4	21	63
subtype5	5	16
subtype6	2	8
subtype7	8	43

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0178 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	173	50.9 (41.4)
subtype1	42	64.0 (52.0)
subtype2	17	49.1 (25.1)
subtype3	31	63.6 (36.5)
subtype4	45	35.7 (23.3)
subtype5	11	37.6 (16.9)
subtype6	4	34.8 (33.7)
subtype7	23	50.0 (59.2)

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

'Copy Number Ratio CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.321 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	123	1994.4 (13.9)
subtype1	22	1996.4 (13.5)
subtype2	10	2000.2 (5.6)
subtype3	22	1996.7 (11.9)
subtype4	37	1990.0 (15.4)
subtype5	10	1996.8 (14.9)
subtype6	3	1997.0 (5.6)
subtype7	19	1993.2 (15.7)

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000245 (Chi-square test), Q value = 0.022

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	83	57	102	66
subtype1	16	10	33	5
subtype2	13	1	11	14
subtype3	16	8	18	2
subtype4	18	21	20	24
subtype5	7	4	4	6
subtype6	3	0	3	4
subtype7	10	13	13	11

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

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.791 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	180	1965.0 (12.4)
subtype1	45	1965.8 (11.6)
subtype2	18	1966.3 (13.5)
subtype3	31	1963.3 (11.0)
subtype4	46	1963.5 (14.2)
subtype5	9	1968.8 (7.4)
subtype6	5	1969.4 (6.8)
subtype7	26	1965.1 (13.7)

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3	4	5	6	7	8
Number of samples	43	47	40	44	58	53	5	19

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0554 (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	306	121	0.1 - 210.9 (14.6)
subtype1	43	15	0.1 - 142.5 (13.2)
subtype2	47	19	0.2 - 126.1 (13.6)
subtype3	39	20	0.1 - 156.5 (12.7)
subtype4	44	8	0.8 - 95.0 (22.3)
subtype5	58	30	0.5 - 135.3 (14.4)
subtype6	51	19	1.5 - 210.9 (14.3)
subtype7	5	1	5.0 - 28.6 (10.7)
subtype8	19	9	1.0 - 84.5 (18.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.000106 (ANOVA), Q value = 0.0096

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

	nPatients	Mean (Std.Dev)
ALL	309	61.1 (12.1)
subtype1	43	58.0 (10.6)
subtype2	47	55.5 (13.8)
subtype3	40	62.4 (11.1)
subtype4	44	58.1 (10.9)
subtype5	58	64.4 (10.1)
subtype6	53	63.9 (13.2)
subtype7	5	63.8 (11.7)
subtype8	19	67.4 (10.3)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0445 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	85	224
subtype1	7	36
subtype2	17	30
subtype3	15	25
subtype4	5	39
subtype5	15	43
subtype6	18	35
subtype7	1	4
subtype8	7	12

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0887 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	24	78	63	102
subtype1	2	10	11	17
subtype2	7	14	12	10
subtype3	1	10	5	19
subtype4	4	9	5	8
subtype5	1	13	13	23
subtype6	8	18	8	17
subtype7	1	0	2	1
subtype8	0	4	7	7

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.439 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	101	32	100	5
subtype1	21	4	11	1
subtype2	19	4	14	0
subtype3	15	4	9	2
subtype4	9	5	9	0
subtype5	12	5	24	2
subtype6	18	8	22	0
subtype7	1	0	3	0
subtype8	6	2	8	0

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

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

P value = 0.715 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	78	231
subtype1	7	36
subtype2	10	37
subtype3	11	29
subtype4	10	34
subtype5	16	42
subtype6	17	36
subtype7	2	3
subtype8	5	14

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0491 (ANOVA), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	168	49.5 (37.2)
subtype1	29	56.3 (30.5)
subtype2	15	33.5 (20.9)
subtype3	18	55.2 (64.5)
subtype4	22	41.8 (44.6)
subtype5	36	64.1 (33.8)
subtype6	31	42.9 (25.7)
subtype7	4	47.2 (23.6)
subtype8	13	34.2 (21.4)

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.19 (ANOVA), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	121	1994.5 (13.8)
subtype1	13	1999.8 (12.8)
subtype2	12	1995.8 (11.7)
subtype3	14	1995.7 (15.2)
subtype4	19	1994.3 (12.2)
subtype5	28	1996.3 (12.1)
subtype6	22	1988.0 (15.9)
subtype7	3	2005.3 (8.1)
subtype8	10	1990.7 (16.7)

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.00052 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	82	55	99	62
subtype1	12	7	21	2
subtype2	7	10	9	18
subtype3	9	6	11	11
subtype4	14	6	11	13
subtype5	23	8	21	3
subtype6	8	14	17	13
subtype7	3	0	2	0
subtype8	6	4	7	2

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0163 (ANOVA), Q value = 0.046

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	175	1964.6 (12.1)
subtype1	25	1965.2 (9.2)
subtype2	18	1971.8 (12.9)
subtype3	19	1967.8 (11.9)
subtype4	26	1965.3 (11.8)
subtype5	36	1962.4 (11.1)
subtype6	34	1964.2 (12.4)
subtype7	5	1963.4 (11.3)
subtype8	12	1954.9 (14.8)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S25. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	82	85	45

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.109 (logrank test), Q value = 1

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	212	107	0.1 - 210.9 (13.2)
subtype1	82	42	1.5 - 129.2 (12.1)
subtype2	85	47	0.1 - 210.9 (13.1)
subtype3	45	18	2.1 - 156.5 (20.5)

Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.434 (ANOVA), Q value = 1

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	212	62.1 (12.2)
subtype1	82	61.1 (12.1)
subtype2	85	63.4 (12.1)
subtype3	45	61.4 (12.7)

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.139 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	62	150
subtype1	19	63
subtype2	25	60
subtype3	18	27

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.216 (Chi-square test), Q value = 1

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	13	59	53	79
subtype1	3	24	16	34
subtype2	6	18	27	32
subtype3	4	17	10	13

Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0063 (Chi-square test), Q value = 0.55

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	73	20	79	4
subtype1	27	9	30	1
subtype2	19	8	40	2
subtype3	27	3	9	1

Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

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

P value = 0.301 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	56	156
subtype1	25	57
subtype2	23	62
subtype3	8	37

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.658 (ANOVA), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	105	48.9 (38.6)
subtype1	39	48.3 (34.1)
subtype2	44	46.2 (29.7)
subtype3	22	55.4 (58.5)

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.79 (ANOVA), Q value = 1

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	85	1994.1 (14.2)
subtype1	25	1995.7 (13.1)
subtype2	41	1993.2 (13.8)
subtype3	19	1993.8 (16.8)

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.167 (Chi-square test), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	60	38	64	40
subtype1	20	10	28	19
subtype2	27	21	24	10
subtype3	13	7	12	11

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.539 (ANOVA), Q value = 1

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	115	1964.2 (12.4)
subtype1	43	1965.9 (14.1)
subtype2	49	1963.0 (11.7)
subtype3	23	1963.7 (10.9)

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #4: 'RPPA cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	115	81	16

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.265 (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	107	0.1 - 210.9 (13.2)
subtype1	115	56	0.1 - 210.9 (13.2)
subtype2	81	42	1.5 - 156.5 (13.6)
subtype3	16	9	3.3 - 52.3 (11.6)

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 = 0.303 (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	115	63.3 (11.2)
subtype2	81	61.1 (13.1)
subtype3	16	59.4 (14.3)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.842 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	62	150
subtype1	35	80
subtype2	22	59
subtype3	5	11

Figure S35. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.194 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	13	59	53	79
subtype1	7	25	36	42
subtype2	5	30	13	30
subtype3	1	4	4	7

Figure S36. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0534 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	73	20	79	4
subtype1	39	8	46	3
subtype2	32	11	22	1
subtype3	2	1	11	0

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

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

P value = 0.595 (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	30	85
subtype2	20	61
subtype3	6	10

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

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.752 (ANOVA), Q value = 1

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	105	48.9 (38.6)
subtype1	60	47.2 (31.5)
subtype2	37	49.6 (50.0)
subtype3	8	58.1 (28.6)

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.37 (ANOVA), Q value = 1

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	85	1994.1 (14.2)
subtype1	53	1994.4 (13.7)
subtype2	27	1992.0 (16.0)
subtype3	5	2001.6 (5.9)

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.433 (Chi-square test), Q value = 1

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	60	38	64	40
subtype1	34	26	33	18
subtype2	22	11	24	18
subtype3	4	1	7	4

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.746 (ANOVA), Q value = 1

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	115	1964.2 (12.4)
subtype1	66	1963.7 (10.8)
subtype2	41	1964.5 (15.4)
subtype3	8	1967.2 (7.9)

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S47. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	118	93	91

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.448 (logrank test), Q value = 1

Table S48. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	299	120	0.1 - 210.9 (14.8)
subtype1	118	42	0.1 - 135.3 (14.2)
subtype2	91	40	0.2 - 142.5 (15.7)
subtype3	90	38	0.1 - 210.9 (14.0)

Figure S43. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.925 (ANOVA), Q value = 1

Table S49. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	302	61.0 (12.2)
subtype1	118	61.3 (11.1)
subtype2	93	60.6 (12.8)
subtype3	91	61.2 (12.9)

Figure S44. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.188 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	81	221
subtype1	25	93
subtype2	27	66
subtype3	29	62

Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.225 (Chi-square test), Q value = 1

Table S51. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	23	78	60	101
subtype1	6	25	24	36
subtype2	13	25	19	29
subtype3	4	28	17	36

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.194 (Chi-square test), Q value = 1

Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	100	32	97	5
subtype1	34	10	32	3
subtype2	28	10	42	1
subtype3	38	12	23	1

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

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

P value = 0.413 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	75	227
subtype1	25	93
subtype2	27	66
subtype3	23	68

Figure S48. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0646 (ANOVA), Q value = 1

Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	165	50.0 (37.4)
subtype1	75	56.7 (36.5)
subtype2	41	48.5 (50.4)
subtype3	49	40.9 (21.1)

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.587 (ANOVA), Q value = 1

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	119	1994.6 (13.9)
subtype1	49	1995.7 (13.1)
subtype2	34	1995.0 (13.4)
subtype3	36	1992.6 (15.6)

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.42 (Chi-square test), Q value = 1

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	82	53	98	58
subtype1	37	17	42	17
subtype2	24	16	29	21
subtype3	21	20	27	20

Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.133 (ANOVA), Q value = 1

Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	173	1964.5 (12.1)
subtype1	73	1963.0 (11.0)
subtype2	49	1967.4 (11.5)
subtype3	51	1964.1 (13.8)

Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	97	85	120

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.205 (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	299	120	0.1 - 210.9 (14.8)
subtype1	95	43	1.5 - 142.5 (15.1)
subtype2	84	36	0.1 - 210.9 (14.0)
subtype3	120	41	0.1 - 135.3 (15.1)

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 = 0.383 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	302	61.0 (12.2)
subtype1	97	60.0 (13.3)
subtype2	85	62.5 (12.9)
subtype3	120	60.9 (10.5)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00985 (Fisher's exact test), Q value = 0.85

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

nPatients	FEMALE	MALE
ALL	81	221
subtype1	31	66
subtype2	29	56
subtype3	21	99

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.298 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	23	78	60	101
subtype1	13	27	22	30
subtype2	4	26	15	32
subtype3	6	25	23	39

Figure S56. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0772 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	100	32	97	5
subtype1	27	12	45	1
subtype2	37	10	20	1
subtype3	36	10	32	3

Figure S57. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

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

P value = 0.546 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	75	227
subtype1	28	69
subtype2	20	65
subtype3	27	93

Figure S58. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0544 (ANOVA), Q value = 1

Table S65. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	165	50.0 (37.4)
subtype1	44	46.6 (49.1)
subtype2	42	40.5 (20.5)
subtype3	79	56.9 (35.8)

Figure S59. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.724 (ANOVA), Q value = 1

Table S66. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	119	1994.6 (13.9)
subtype1	37	1993.6 (14.0)
subtype2	34	1993.9 (15.5)
subtype3	48	1995.8 (12.9)

Figure S60. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'STOPPEDSMOKINGYEAR'

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.129 (Chi-square test), Q value = 1

Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	82	53	98	58
subtype1	24	19	29	23
subtype2	21	17	22	20
subtype3	37	17	47	15

Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.125 (ANOVA), Q value = 1

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	173	1964.5 (12.1)
subtype1	51	1967.4 (11.8)
subtype2	44	1963.9 (14.2)
subtype3	78	1963.0 (10.8)

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S69. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	102	127	94

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.703 (logrank test), Q value = 1

Table S70. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	319	123	0.1 - 210.9 (14.8)
subtype1	100	36	0.1 - 210.9 (12.3)
subtype2	127	48	0.1 - 142.5 (14.8)
subtype3	92	39	1.0 - 108.3 (17.1)

Figure S63. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.838 (ANOVA), Q value = 1

Table S71. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	322	61.2 (12.1)
subtype1	101	60.6 (12.6)
subtype2	127	61.5 (12.1)
subtype3	94	61.4 (11.5)

Figure S64. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.2 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	92	231
subtype1	24	78
subtype2	35	92
subtype3	33	61

Figure S65. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.289 (Chi-square test), Q value = 1

Table S73. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	28	81	65	106
subtype1	7	29	16	35
subtype2	8	26	28	44
subtype3	13	26	21	27

Figure S66. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.172 (Chi-square test), Q value = 1

Table S74. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	104	36	103	5
subtype1	36	10	25	2
subtype2	43	11	40	3
subtype3	25	15	38	0

Figure S67. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.422 (Fisher's exact test), Q value = 1

Table S75. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients	M0	MX
ALL	57	6
subtype1	15	3
subtype2	22	1
subtype3	20	2

Figure S68. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.621 (Chi-square test), Q value = 1

Table S76. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	20	49	44	162
subtype1	6	19	12	47
subtype2	5	16	18	66
subtype3	9	14	14	49

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

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

P value = 0.536 (Fisher's exact test), Q value = 1

Table S77. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	77	246
subtype1	24	78
subtype2	27	100
subtype3	26	68

Figure S70. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.494 (ANOVA), Q value = 1

Table S78. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	177	50.6 (41.1)
subtype1	63	48.4 (40.7)
subtype2	71	55.1 (36.1)
subtype3	43	46.6 (49.2)

Figure S71. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.306 (ANOVA), Q value = 1

Table S79. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	125	1994.3 (13.8)
subtype1	38	1992.6 (15.6)
subtype2	50	1996.6 (12.3)
subtype3	37	1992.8 (13.8)

Figure S72. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'STOPPEDSMOKINGYEAR'

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.278 (Chi-square test), Q value = 1

Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	84	58	104	66
subtype1	25	19	35	20
subtype2	37	17	45	22
subtype3	22	22	24	24

Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.766 (ANOVA), Q value = 1

Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	184	1964.9 (12.3)
subtype1	62	1965.5 (14.2)
subtype2	72	1964.1 (11.7)
subtype3	50	1965.3 (10.6)

Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S82. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	182	131	10

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.707 (logrank test), Q value = 1

Table S83. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	319	123	0.1 - 210.9 (14.8)
subtype1	180	68	0.1 - 210.9 (14.2)
subtype2	129	52	0.2 - 156.5 (16.2)
subtype3	10	3	3.5 - 56.7 (10.8)

Figure S75. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.35 (ANOVA), Q value = 1

Table S84. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	322	61.2 (12.1)
subtype1	181	62.0 (11.6)
subtype2	131	60.3 (12.8)
subtype3	10	58.2 (11.1)

Figure S76. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.522 (Fisher's exact test), Q value = 1

Table S85. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	92	231
subtype1	48	134
subtype2	42	89
subtype3	2	8

Figure S77. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.0851 (Chi-square test), Q value = 1

Table S86. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	28	81	65	106
subtype1	9	43	34	63
subtype2	19	34	30	38
subtype3	0	4	1	5

Figure S78. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.535 (Chi-square test), Q value = 1

Table S87. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	104	36	103	5
subtype1	58	18	49	4
subtype2	40	17	51	1
subtype3	6	1	3	0

Figure S79. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.733 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients	M0	MX
ALL	57	6
subtype1	27	2
subtype2	28	4
subtype3	2	0

Figure S80. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.274 (Chi-square test), Q value = 1

Table S89. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	20	49	44	162
subtype1	6	26	25	88
subtype2	14	20	17	69
subtype3	0	3	2	5

Figure S81. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'TUMOR.STAGE'

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

P value = 0.283 (Fisher's exact test), Q value = 1

Table S90. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	77	246
subtype1	39	143
subtype2	34	97
subtype3	4	6

Figure S82. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.51 (ANOVA), Q value = 1

Table S91. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	177	50.6 (41.1)
subtype1	109	50.3 (33.3)
subtype2	62	49.4 (53.4)
subtype3	6	69.8 (18.2)

Figure S83. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'STOPPEDSMOKINGYEAR'

P value = 0.236 (ANOVA), Q value = 1

Table S92. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	125	1994.3 (13.8)
subtype1	74	1995.6 (13.5)
subtype2	49	1992.5 (14.5)
subtype3	2	1988.5 (3.5)

Figure S84. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'STOPPEDSMOKINGYEAR'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.238 (Chi-square test), Q value = 1

Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	84	58	104	66
subtype1	54	28	60	32
subtype2	29	29	39	32
subtype3	1	1	5	2

Figure S85. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.464 (ANOVA), Q value = 1

Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	184	1964.9 (12.3)
subtype1	108	1963.9 (11.4)
subtype2	70	1966.2 (13.8)
subtype3	6	1966.2 (8.7)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

Methods & Data

Input

Cluster data file = HNSC-TP.mergedcluster.txt
Clinical data file = HNSC-TP.clin.merged.picked.txt
Number of patients = 325
Number of clustering approaches = 8
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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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)

Made with Nozzle