Correlation between aggregated molecular cancer subtypes and selected clinical features

Stomach and Esophageal carcinoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C10V8C25

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 628 patients, 67 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'RADIATION_THERAPY', and 'RACE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', and 'RACE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', and 'PATHOLOGY_M_STAGE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY_T_STAGE' and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'RACE', and 'ETHNICITY'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'NUMBER_PACK_YEARS_SMOKED', 'RACE', and 'ETHNICITY'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_PACK_YEARS_SMOKED', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'NUMBER_PACK_YEARS_SMOKED', and 'RACE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.

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, 67 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	0.263 (0.354)	0.269 (0.359)	0.00583 (0.0152)	0.118 (0.183)	0.859 (0.881)	0.837 (0.881)	0.251 (0.343)	0.336 (0.444)	0.0277 (0.0573)	0.552 (0.649)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.000329 (0.00127)	1.22e-05 (6.96e-05)	0.115 (0.182)	0.594 (0.685)	5.44e-06 (6e-05)	6.96e-06 (6e-05)	8.41e-08 (2.52e-06)	1.55e-09 (1.87e-07)	2.25e-08 (1.13e-06)	2.82e-08 (1.13e-06)
PATHOLOGIC STAGE	Fisher's exact test	7e-05 (0.000336)	5e-05 (0.00025)	0.0087 (0.0205)	0.351 (0.451)	9e-05 (0.000415)	1e-05 (6e-05)	1e-05 (6e-05)	1e-05 (6e-05)	0.00041 (0.00154)	1e-05 (6e-05)
PATHOLOGY T STAGE	Fisher's exact test	0.00068 (0.0024)	0.00144 (0.00455)	0.00113 (0.00366)	0.0125 (0.029)	1e-05 (6e-05)	0.00194 (0.00582)	0.00015 (0.000643)	1e-05 (6e-05)	2e-05 (0.000104)	1e-05 (6e-05)
PATHOLOGY N STAGE	Fisher's exact test	0.0232 (0.0498)	0.0412 (0.0749)	0.412 (0.5)	0.401 (0.491)	0.00269 (0.00787)	0.00486 (0.013)	0.00075 (0.00257)	1e-05 (6e-05)	0.00847 (0.0203)	2e-05 (0.000104)
PATHOLOGY M STAGE	Fisher's exact test	0.852 (0.881)	0.036 (0.0698)	0.00477 (0.013)	0.25 (0.343)	0.776 (0.846)	0.343 (0.447)	0.904 (0.909)	0.357 (0.451)	0.354 (0.451)	0.784 (0.848)
GENDER	Fisher's exact test	1e-05 (6e-05)	0.838 (0.881)	0.436 (0.523)	0.538 (0.639)	0.64 (0.731)	0.909 (0.909)	0.00026 (0.00104)	0.0132 (0.0298)	0.0065 (0.0166)	0.00847 (0.0203)
RADIATION THERAPY	Fisher's exact test	0.00328 (0.00937)	0.214 (0.302)	0.205 (0.296)	0.103 (0.165)	0.00011 (0.000489)	0.00024 (0.000993)	0.557 (0.649)	0.00428 (0.0119)	0.677 (0.759)	0.0144 (0.032)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)	0.383 (0.478)	0.0939 (0.152)	0.391 (0.483)	0.737 (0.817)	0.029 (0.059)	0.0361 (0.0698)	0.208 (0.298)	0.0384 (0.072)	0.142 (0.216)	0.0469 (0.0839)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.0917 (0.151)	0.0695 (0.123)	0.178 (0.261)	0.873 (0.888)	0.0772 (0.134)	0.125 (0.192)	0.0371 (0.0707)	0.0406 (0.0749)	0.00839 (0.0203)	0.0853 (0.144)
RACE	Fisher's exact test	0.00161 (0.00495)	0.00083 (0.00277)	0.249 (0.343)	0.0311 (0.0622)	1e-05 (6e-05)	1e-05 (6e-05)	1e-05 (6e-05)	1e-05 (6e-05)	0.00053 (0.00193)	1e-05 (6e-05)
ETHNICITY	Fisher's exact test	0.859 (0.881)	0.799 (0.856)	0.0794 (0.136)	0.663 (0.751)	0.0205 (0.0447)	0.172 (0.254)	0.0247 (0.0519)	0.742 (0.817)	0.0901 (0.15)	0.16 (0.24)

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

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

Cluster Labels	1	2	3
Number of samples	166	210	249

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

P value = 0.263 (logrank test), Q value = 0.35

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	590	235	0.0 - 122.3 (13.5)
subtype1	157	62	0.1 - 122.3 (13.2)
subtype2	203	83	0.1 - 63.6 (12.9)
subtype3	230	90	0.0 - 116.4 (15.5)

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 'YEARS_TO_BIRTH'

P value = 0.000329 (Kruskal-Wallis (anova)), Q value = 0.0013

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

	nPatients	Mean (Std.Dev)
ALL	616	64.8 (11.2)
subtype1	162	67.1 (9.9)
subtype2	208	62.6 (11.5)
subtype3	246	65.1 (11.5)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 7e-05 (Fisher's exact test), Q value = 0.00034

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	20	47	33	87	88	29	97	73	46	51	4
subtype1	5	6	12	10	10	26	9	29	19	9	15	1
subtype2	2	3	12	10	47	28	18	30	20	13	12	3
subtype3	4	11	23	13	30	34	2	38	34	24	24	0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00068 (Fisher's exact test), Q value = 0.0024

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

nPatients	T0+T1	T2	T3	T4
ALL	55	136	283	124
subtype1	20	33	77	28
subtype2	16	53	105	25
subtype3	19	50	101	71

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 = 0.0232 (Fisher's exact test), Q value = 0.05

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

nPatients	N0	N1	N2	N3
ALL	207	188	98	96
subtype1	43	53	33	23
subtype2	75	73	25	26
subtype3	89	62	40	47

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 = 0.852 (Fisher's exact test), Q value = 0.88

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

nPatients	0	1
ALL	524	39
subtype1	132	9
subtype2	178	12
subtype3	214	18

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 = 1e-05 (Fisher's exact test), Q value = 6e-05

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

nPatients	FEMALE	MALE
ALL	185	440
subtype1	45	121
subtype2	39	171
subtype3	101	148

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.00328 (Fisher's exact test), Q value = 0.0094

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

nPatients	NO	YES
ALL	438	116
subtype1	122	22
subtype2	136	56
subtype3	180	38

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.383 (Kruskal-Wallis (anova)), Q value = 0.48

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

	nPatients	Mean (Std.Dev)
ALL	68	73.8 (16.2)
subtype1	3	80.0 (17.3)
subtype2	61	73.0 (16.5)
subtype3	4	82.5 (9.6)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0917 (Kruskal-Wallis (anova)), Q value = 0.15

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

	nPatients	Mean (Std.Dev)
ALL	98	34.5 (21.5)
subtype1	23	39.3 (24.5)
subtype2	62	30.9 (19.5)
subtype3	13	43.0 (22.6)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00161 (Fisher's exact test), Q value = 0.005

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	134	17	1	392
subtype1	22	5	1	110
subtype2	64	6	0	116
subtype3	48	6	0	166

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.859 (Fisher's exact test), Q value = 0.88

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	404
subtype1	3	100
subtype2	4	127
subtype3	4	177

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	249	158	173

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.269 (logrank test), Q value = 0.36

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	566	223	0.1 - 122.3 (13.5)
subtype1	243	90	0.1 - 122.1 (14.3)
subtype2	155	59	0.1 - 68.0 (12.8)
subtype3	168	74	0.1 - 122.3 (13.3)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 1.22e-05 (Kruskal-Wallis (anova)), Q value = 7e-05

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

	nPatients	Mean (Std.Dev)
ALL	571	64.3 (11.2)
subtype1	244	66.6 (10.9)
subtype2	156	62.1 (10.8)
subtype3	171	63.1 (11.3)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00025

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	19	41	30	88	87	29	91	72	45	40	4
subtype1	9	10	18	12	24	45	12	35	37	20	13	0
subtype2	2	4	10	8	39	23	15	21	13	8	8	2
subtype3	0	5	13	10	25	19	2	35	22	17	19	2

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00144 (Fisher's exact test), Q value = 0.0045

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	53	121	274	115
subtype1	33	41	117	48
subtype2	13	44	75	22
subtype3	7	36	82	45

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 = 0.0412 (Fisher's exact test), Q value = 0.075

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

nPatients	N0	N1	N2	N3
ALL	201	171	92	91
subtype1	84	71	42	40
subtype2	64	53	21	14
subtype3	53	47	29	37

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 = 0.036 (Fisher's exact test), Q value = 0.07

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	489	32
subtype1	211	8
subtype2	137	8
subtype3	141	16

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.838 (Fisher's exact test), Q value = 0.88

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

nPatients	FEMALE	MALE
ALL	163	417
subtype1	70	179
subtype2	42	116
subtype3	51	122

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.214 (Fisher's exact test), Q value = 0.3

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	425	115
subtype1	186	44
subtype2	109	39
subtype3	130	32

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0939 (Kruskal-Wallis (anova)), Q value = 0.15

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	68	73.8 (16.2)
subtype1	6	85.0 (12.2)
subtype2	54	72.0 (16.6)
subtype3	8	77.5 (12.8)

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

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0695 (Kruskal-Wallis (anova)), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	98	34.5 (21.5)
subtype1	32	41.2 (24.8)
subtype2	54	29.8 (18.1)
subtype3	12	37.7 (23.1)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.00083 (Fisher's exact test), Q value = 0.0028

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	135	18	1	367
subtype1	44	7	1	167
subtype2	56	7	0	82
subtype3	35	4	0	118

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.799 (Fisher's exact test), Q value = 0.86

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	381
subtype1	4	152
subtype2	4	103
subtype3	3	126

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

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	145	137	137	64

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.00583 (logrank test), Q value = 0.015

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	448	173	0.1 - 122.3 (13.4)
subtype1	128	51	0.1 - 72.2 (14.4)
subtype2	130	41	0.1 - 122.3 (14.4)
subtype3	130	59	0.1 - 52.6 (12.6)
subtype4	60	22	0.3 - 79.1 (15.0)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.115 (Kruskal-Wallis (anova)), Q value = 0.18

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

	nPatients	Mean (Std.Dev)
ALL	475	64.9 (11.2)
subtype1	139	65.9 (12.0)
subtype2	137	65.3 (10.6)
subtype3	137	62.9 (11.1)
subtype4	62	65.9 (10.2)

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

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0087 (Fisher's exact test), Q value = 0.02

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	9	13	36	30	74	67	20	81	59	34	38	1
subtype1	4	5	13	8	19	20	6	25	19	12	6	0
subtype2	3	2	15	8	27	19	4	21	18	8	7	0
subtype3	0	1	3	11	20	23	10	19	18	11	15	1
subtype4	2	5	5	3	8	5	0	16	4	3	10	0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00113 (Fisher's exact test), Q value = 0.0037

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	32	111	225	103
subtype1	13	40	60	29
subtype2	6	40	66	23
subtype3	5	16	75	37
subtype4	8	15	24	14

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 = 0.412 (Fisher's exact test), Q value = 0.5

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	171	146	74	74
subtype1	46	44	28	20
subtype2	58	36	20	19
subtype3	43	46	16	27
subtype4	24	20	10	8

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00477 (Fisher's exact test), Q value = 0.013

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	427	25
subtype1	130	4
subtype2	128	3
subtype3	121	10
subtype4	48	8

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.436 (Fisher's exact test), Q value = 0.52

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	139	344
subtype1	36	109
subtype2	46	91
subtype3	38	99
subtype4	19	45

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.205 (Fisher's exact test), Q value = 0.3

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	342	86
subtype1	97	27
subtype2	96	27
subtype3	109	18
subtype4	40	14

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.391 (Kruskal-Wallis (anova)), Q value = 0.48

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	48	69.2 (16.2)
subtype1	14	65.7 (19.5)
subtype2	12	69.2 (17.8)
subtype3	15	68.0 (13.2)
subtype4	7	78.6 (10.7)

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.178 (Kruskal-Wallis (anova)), Q value = 0.26

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	66	36.4 (20.6)
subtype1	19	33.3 (16.1)
subtype2	17	30.6 (18.3)
subtype3	22	46.4 (25.6)
subtype4	8	28.4 (7.7)

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.249 (Fisher's exact test), Q value = 0.34

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	109	7	1	305
subtype1	35	3	0	82
subtype2	29	2	1	89
subtype3	36	0	0	93
subtype4	9	2	0	41

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.0794 (Fisher's exact test), Q value = 0.14

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	323
subtype1	1	92
subtype2	1	91
subtype3	0	105
subtype4	2	35

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S40. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	273	111	99

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.118 (logrank test), Q value = 0.18

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	448	173	0.1 - 122.3 (13.4)
subtype1	248	98	0.1 - 73.4 (13.4)
subtype2	106	36	0.5 - 122.3 (15.0)
subtype3	94	39	0.1 - 52.6 (12.8)

Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.594 (Kruskal-Wallis (anova)), Q value = 0.69

Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	475	64.9 (11.2)
subtype1	265	65.2 (11.4)
subtype2	111	65.0 (11.2)
subtype3	99	63.9 (10.6)

Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.351 (Fisher's exact test), Q value = 0.45

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	9	13	36	30	74	67	20	81	59	34	38	1
subtype1	6	9	20	15	42	33	14	50	32	20	20	1
subtype2	3	2	14	9	17	18	2	16	13	6	5	0
subtype3	0	2	2	6	15	16	4	15	14	8	13	0

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0125 (Fisher's exact test), Q value = 0.029

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

nPatients	T0+T1	T2	T3	T4
ALL	32	111	225	103
subtype1	22	63	128	55
subtype2	6	36	45	20
subtype3	4	12	52	28

Figure S40. 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 = 0.401 (Fisher's exact test), Q value = 0.49

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	171	146	74	74
subtype1	96	90	42	37
subtype2	44	25	18	17
subtype3	31	31	14	20

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.25 (Fisher's exact test), Q value = 0.34

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	427	25
subtype1	237	14
subtype2	102	3
subtype3	88	8

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.538 (Fisher's exact test), Q value = 0.64

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	139	344
subtype1	75	198
subtype2	31	80
subtype3	33	66

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.103 (Fisher's exact test), Q value = 0.17

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	342	86
subtype1	180	55
subtype2	82	19
subtype3	80	12

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.737 (Kruskal-Wallis (anova)), Q value = 0.82

Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	48	69.2 (16.2)
subtype1	33	68.8 (16.0)
subtype2	7	72.9 (19.8)
subtype3	8	67.5 (15.8)

Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.873 (Kruskal-Wallis (anova)), Q value = 0.89

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	66	36.4 (20.6)
subtype1	39	35.2 (19.6)
subtype2	15	36.1 (22.1)
subtype3	12	40.5 (23.4)

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.0311 (Fisher's exact test), Q value = 0.062

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	109	7	1	305
subtype1	71	4	0	157
subtype2	17	3	1	76
subtype3	21	0	0	72

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.663 (Fisher's exact test), Q value = 0.75

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	323
subtype1	3	173
subtype2	1	71
subtype3	0	79

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S53. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	82	69	22	11

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.859 (logrank test), Q value = 0.88

Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	183	77	0.1 - 122.1 (13.2)
subtype1	82	42	0.4 - 122.1 (13.4)
subtype2	68	21	0.1 - 68.0 (12.6)
subtype3	22	10	1.3 - 54.0 (17.2)
subtype4	11	4	2.6 - 31.6 (14.9)

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 5.44e-06 (Kruskal-Wallis (anova)), Q value = 6e-05

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	184	62.5 (11.9)
subtype1	82	67.1 (11.8)
subtype2	69	57.9 (10.6)
subtype3	22	61.1 (11.9)
subtype4	11	59.0 (8.0)

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 9e-05 (Fisher's exact test), Q value = 0.00042

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	8	5	6	1	46	31	26	14	9	7	5	4
subtype1	8	2	1	0	5	16	14	4	4	4	2	3
subtype2	0	1	4	1	32	8	10	5	2	2	3	0
subtype3	0	2	1	0	5	5	2	2	2	0	0	1
subtype4	0	0	0	0	4	2	0	3	1	1	0	0

Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	32	42	88	5
subtype1	24	8	36	0
subtype2	3	24	38	3
subtype3	5	7	8	0
subtype4	0	3	6	2

Figure S52. 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 = 0.00269 (Fisher's exact test), Q value = 0.0079

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	76	69	12	8
subtype1	21	37	6	4
subtype2	42	19	3	3
subtype3	6	12	2	0
subtype4	7	1	1	1

Figure S53. 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 = 0.776 (Fisher's exact test), Q value = 0.85

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	135	9
subtype1	47	5
subtype2	59	3
subtype3	19	1
subtype4	10	0

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.64 (Fisher's exact test), Q value = 0.73

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	26	158
subtype1	11	71
subtype2	9	60
subtype3	3	19
subtype4	3	8

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.00011 (Fisher's exact test), Q value = 0.00049

Table S61. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	124	42
subtype1	63	7
subtype2	41	24
subtype3	16	5
subtype4	4	6

Figure S56. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.029 (Kruskal-Wallis (anova)), Q value = 0.059

Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	67	73.6 (16.2)
subtype1	6	85.0 (12.2)
subtype2	45	70.4 (15.9)
subtype3	10	82.0 (14.0)
subtype4	6	71.7 (18.3)

Figure S57. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0772 (Kruskal-Wallis (anova)), Q value = 0.13

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	97	34.7 (21.5)
subtype1	38	41.3 (24.5)
subtype2	39	30.4 (19.3)
subtype3	16	28.0 (16.6)
subtype4	4	40.9 (14.1)

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	46	5	113
subtype1	1	0	65
subtype2	37	2	27
subtype3	4	3	14
subtype4	4	0	7

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0205 (Fisher's exact test), Q value = 0.045

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	88
subtype1	3	21
subtype2	0	49
subtype3	2	9
subtype4	0	9

Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S66. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	68	20	96

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.837 (logrank test), Q value = 0.88

Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	183	77	0.1 - 122.1 (13.2)
subtype1	68	34	1.4 - 122.1 (15.3)
subtype2	20	10	0.4 - 54.0 (10.9)
subtype3	95	33	0.1 - 68.0 (12.7)

Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 6.96e-06 (Kruskal-Wallis (anova)), Q value = 6e-05

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	184	62.5 (11.9)
subtype1	68	67.1 (11.7)
subtype2	20	65.5 (13.3)
subtype3	96	58.6 (10.5)

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	8	5	6	1	46	31	26	14	9	7	5	4
subtype1	6	2	1	0	3	16	13	4	1	5	2	3
subtype2	2	0	1	0	2	2	1	1	3	0	0	0
subtype3	0	3	4	1	41	13	12	9	5	2	3	1

Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00194 (Fisher's exact test), Q value = 0.0058

Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	32	42	88	5
subtype1	18	9	30	1
subtype2	6	2	7	0
subtype3	8	31	51	4

Figure S64. 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 = 0.00486 (Fisher's exact test), Q value = 0.013

Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	76	69	12	8
subtype1	17	32	3	5
subtype2	5	7	3	0
subtype3	54	30	6	3

Figure S65. 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 = 0.343 (Fisher's exact test), Q value = 0.45

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	135	9
subtype1	42	5
subtype2	9	0
subtype3	84	4

Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.909 (Fisher's exact test), Q value = 0.91

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	26	158
subtype1	10	58
subtype2	2	18
subtype3	14	82

Figure S67. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00024 (Fisher's exact test), Q value = 0.00099

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	124	42
subtype1	48	9
subtype2	19	0
subtype3	57	33

Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0361 (Kruskal-Wallis (anova)), Q value = 0.07

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	67	73.6 (16.2)
subtype1	6	85.0 (12.2)
subtype2	1	90.0 (NA)
subtype3	60	72.2 (16.2)

Figure S69. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.125 (Kruskal-Wallis (anova)), Q value = 0.19

Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	97	34.7 (21.5)
subtype1	27	37.6 (25.6)
subtype2	14	43.8 (21.9)
subtype3	56	31.0 (18.6)

Figure S70. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	46	5	113
subtype1	1	0	54
subtype2	0	0	17
subtype3	45	5	42

Figure S71. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.172 (Fisher's exact test), Q value = 0.25

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	88
subtype1	2	14
subtype2	1	9
subtype3	2	65

Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S79. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	163	331	126

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.251 (logrank test), Q value = 0.34

Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	585	234	0.0 - 122.3 (13.4)
subtype1	160	67	0.1 - 68.0 (12.8)
subtype2	303	120	0.0 - 122.3 (15.3)
subtype3	122	47	0.1 - 116.4 (12.5)

Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 8.41e-08 (Kruskal-Wallis (anova)), Q value = 2.5e-06

Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	611	64.8 (11.2)
subtype1	163	61.0 (10.8)
subtype2	322	67.0 (10.6)
subtype3	126	64.1 (11.9)

Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	20	46	33	87	87	30	96	71	46	50	4
subtype1	0	6	11	3	45	21	16	19	13	4	10	2
subtype2	11	12	25	21	28	48	13	51	41	29	28	2
subtype3	0	2	10	9	14	18	1	26	17	13	12	0

Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00015 (Fisher's exact test), Q value = 0.00064

Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	55	135	280	123
subtype1	14	46	78	16
subtype2	37	66	140	73
subtype3	4	23	62	34

Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00075 (Fisher's exact test), Q value = 0.0026

Table S84. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	205	187	97	95
subtype1	67	55	17	11
subtype2	100	95	61	56
subtype3	38	37	19	28

Figure S77. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.904 (Fisher's exact test), Q value = 0.91

Table S85. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	519	39
subtype1	133	10
subtype2	279	20
subtype3	107	9

Figure S78. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00026 (Fisher's exact test), Q value = 0.001

Table S86. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	182	438
subtype1	29	134
subtype2	106	225
subtype3	47	79

Figure S79. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.557 (Fisher's exact test), Q value = 0.65

Table S87. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	436	113
subtype1	116	36
subtype2	227	55
subtype3	93	22

Figure S80. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.208 (Kruskal-Wallis (anova)), Q value = 0.3

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	67	74.0 (16.2)
subtype1	59	72.9 (16.5)
subtype2	5	84.0 (13.4)
subtype3	3	80.0 (10.0)

Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0371 (Kruskal-Wallis (anova)), Q value = 0.071

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	97	34.7 (21.5)
subtype1	65	30.4 (18.4)
subtype2	24	43.6 (24.6)
subtype3	8	42.6 (28.0)

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	133	18	387
subtype1	59	7	81
subtype2	46	10	217
subtype3	28	1	89

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0247 (Fisher's exact test), Q value = 0.052

Table S91. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	404
subtype1	7	102
subtype2	3	198
subtype3	1	104

Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S92. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5
Number of samples	128	144	165	94	89

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.336 (logrank test), Q value = 0.44

Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	585	234	0.0 - 122.3 (13.4)
subtype1	120	54	0.1 - 79.1 (12.9)
subtype2	135	56	0.4 - 122.1 (13.9)
subtype3	152	56	0.0 - 122.3 (15.7)
subtype4	93	29	0.1 - 68.0 (12.8)
subtype5	85	39	0.1 - 116.4 (14.0)

Figure S85. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 1.55e-09 (Kruskal-Wallis (anova)), Q value = 1.9e-07

Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	611	64.8 (11.2)
subtype1	125	66.6 (10.0)
subtype2	141	67.5 (10.8)
subtype3	162	65.6 (11.2)
subtype4	94	58.7 (10.4)
subtype5	89	62.9 (12.0)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	20	46	33	87	87	30	96	71	46	50	4
subtype1	3	1	9	11	7	19	5	25	11	12	14	3
subtype2	7	8	17	6	10	19	11	15	16	9	9	0
subtype3	1	8	9	10	21	25	4	29	23	12	14	0
subtype4	0	3	4	1	39	15	10	9	5	2	3	1
subtype5	0	0	7	5	10	9	0	18	16	11	10	0

Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	55	135	280	123
subtype1	7	30	57	29
subtype2	26	26	55	24
subtype3	14	36	78	33
subtype4	8	31	48	5
subtype5	0	12	42	32

Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S97. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	205	187	97	95
subtype1	29	43	25	22
subtype2	50	40	21	17
subtype3	47	52	35	27
subtype4	53	29	6	3
subtype5	26	23	10	26

Figure S89. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.357 (Fisher's exact test), Q value = 0.45

Table S98. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	519	39
subtype1	101	12
subtype2	114	5
subtype3	145	12
subtype4	82	4
subtype5	77	6

Figure S90. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0132 (Fisher's exact test), Q value = 0.03

Table S99. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	182	438
subtype1	37	91
subtype2	40	104
subtype3	54	111
subtype4	16	78
subtype5	35	54

Figure S91. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.00428 (Fisher's exact test), Q value = 0.012

Table S100. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	436	113
subtype1	93	21
subtype2	99	27
subtype3	115	24
subtype4	57	31
subtype5	72	10

Figure S92. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0384 (Kruskal-Wallis (anova)), Q value = 0.072

Table S101. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	67	74.0 (16.2)
subtype2	6	85.0 (12.2)
subtype3	1	90.0 (NA)
subtype4	59	72.4 (16.2)
subtype5	1	90.0 (NA)

Figure S93. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0406 (Kruskal-Wallis (anova)), Q value = 0.075

Table S102. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	97	34.7 (21.5)
subtype1	9	46.3 (17.3)
subtype2	21	32.1 (21.9)
subtype3	12	45.6 (27.9)
subtype4	54	30.5 (18.6)
subtype5	1	80.0 (NA)

Figure S94. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	133	18	387
subtype1	22	4	77
subtype2	25	5	91
subtype3	19	2	116
subtype4	45	5	41
subtype5	22	2	62

Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.742 (Fisher's exact test), Q value = 0.82

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	404
subtype1	3	77
subtype2	2	78
subtype3	2	106
subtype4	3	65
subtype5	1	78

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S105. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	145	207	170

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

P value = 0.0277 (logrank test), Q value = 0.057

Table S106. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	509	208	0.1 - 122.3 (13.4)
subtype1	143	67	0.1 - 79.1 (12.8)
subtype2	199	70	0.3 - 122.3 (14.1)
subtype3	167	71	0.1 - 83.2 (13.7)

Figure S97. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 2.25e-08 (Kruskal-Wallis (anova)), Q value = 1.1e-06

Table S107. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	514	64.3 (11.3)
subtype1	143	60.3 (10.8)
subtype2	202	67.4 (11.0)
subtype3	169	63.9 (11.0)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00041 (Fisher's exact test), Q value = 0.0015

Table S108. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	10	13	35	30	81	78	28	83	62	39	37	4
subtype1	1	2	9	3	36	21	13	17	12	6	10	3
subtype2	9	8	15	14	23	32	8	29	31	16	11	1
subtype3	0	3	11	13	22	25	7	37	19	17	16	0

Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 1e-04

Table S109. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	46	114	248	98
subtype1	10	39	72	13
subtype2	30	40	97	35
subtype3	6	35	79	50

Figure S100. 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 = 0.00847 (Fisher's exact test), Q value = 0.02

Table S110. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	176	157	86	79
subtype1	51	52	18	11
subtype2	70	54	44	32
subtype3	55	51	24	36

Figure S101. 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 = 0.354 (Fisher's exact test), Q value = 0.45

Table S111. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	438	31
subtype1	114	11
subtype2	177	9
subtype3	147	11

Figure S102. 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 = 0.0065 (Fisher's exact test), Q value = 0.017

Table S112. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	143	379
subtype1	26	119
subtype2	61	146
subtype3	56	114

Figure S103. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.677 (Fisher's exact test), Q value = 0.76

Table S113. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	384	102
subtype1	104	32
subtype2	152	37
subtype3	128	33

Figure S104. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.142 (Kruskal-Wallis (anova)), Q value = 0.22

Table S114. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	65	74.2 (16.4)
subtype1	47	73.4 (16.2)
subtype2	6	85.0 (12.2)
subtype3	12	71.7 (18.0)

Figure S105. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.00839 (Kruskal-Wallis (anova)), Q value = 0.02

Table S115. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	94	35.1 (21.6)
subtype1	59	29.7 (18.9)
subtype2	19	41.6 (18.5)
subtype3	16	47.2 (28.1)

Figure S106. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.00053 (Fisher's exact test), Q value = 0.0019

Table S116. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	111	16	338
subtype1	46	6	74
subtype2	31	8	136
subtype3	34	2	128

Figure S107. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.0901 (Fisher's exact test), Q value = 0.15

Table S117. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	334
subtype1	6	81
subtype2	3	120
subtype3	2	133

Figure S108. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S118. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	200	131	99	92

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

P value = 0.552 (logrank test), Q value = 0.65

Table S119. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	509	208	0.1 - 122.3 (13.4)
subtype1	195	88	0.4 - 122.1 (14.3)
subtype2	127	47	0.3 - 122.3 (13.6)
subtype3	96	43	0.1 - 83.2 (13.6)
subtype4	91	30	0.1 - 68.0 (12.8)

Figure S109. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 2.82e-08 (Kruskal-Wallis (anova)), Q value = 1.1e-06

Table S120. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	514	64.3 (11.3)
subtype1	196	65.7 (11.1)
subtype2	128	67.0 (10.6)
subtype3	98	63.3 (11.4)
subtype4	92	58.7 (10.4)

Figure S110. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S121. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	10	13	35	30	81	78	28	83	62	39	37	4
subtype1	9	5	19	10	17	28	14	29	26	13	10	3
subtype2	1	5	5	11	12	23	3	25	17	13	13	0
subtype3	0	0	8	8	12	13	2	20	14	11	11	0
subtype4	0	3	3	1	40	14	9	9	5	2	3	1

Figure S111. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S122. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	46	114	248	98
subtype1	27	37	89	34
subtype2	10	27	62	31
subtype3	1	19	51	28
subtype4	8	31	46	5

Figure S112. 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 = 2e-05 (Fisher's exact test), Q value = 1e-04

Table S123. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	176	157	86	79
subtype1	58	64	36	26
subtype2	38	35	26	29
subtype3	28	30	18	21
subtype4	52	28	6	3

Figure S113. 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 = 0.784 (Fisher's exact test), Q value = 0.85

Table S124. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	438	31
subtype1	156	10
subtype2	115	10
subtype3	86	7
subtype4	81	4

Figure S114. 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 = 0.00847 (Fisher's exact test), Q value = 0.02

Table S125. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	143	379
subtype1	50	150
subtype2	43	88
subtype3	35	64
subtype4	15	77

Figure S115. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0144 (Fisher's exact test), Q value = 0.032

Table S126. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	384	102
subtype1	149	38
subtype2	100	21
subtype3	78	14
subtype4	57	29

Figure S116. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0469 (Kruskal-Wallis (anova)), Q value = 0.084

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

	nPatients	Mean (Std.Dev)
ALL	65	74.2 (16.4)
subtype1	6	85.0 (12.2)
subtype2	1	90.0 (NA)
subtype4	58	72.8 (16.4)

Figure S117. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0853 (Kruskal-Wallis (anova)), Q value = 0.14

Table S128. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	94	35.1 (21.6)
subtype1	28	38.3 (20.7)
subtype2	13	46.3 (29.8)
subtype3	1	40.0 (NA)
subtype4	52	30.4 (18.9)

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 6e-05

Table S129. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	111	16	338
subtype1	30	7	130
subtype2	11	4	97
subtype3	26	1	70
subtype4	44	4	41

Figure S119. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.16 (Fisher's exact test), Q value = 0.24

Table S130. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	334
subtype1	6	95
subtype2	1	87
subtype3	1	89
subtype4	3	63

Figure S120. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/STES-TP/20144037/STES-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/STES-TP/19775559/STES-TP.merged_data.txt
Number of patients = 628
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

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

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

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