Correlation between aggregated molecular cancer subtypes and selected clinical features

Bladder Urothelial Carcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1PV6JQ6

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 13 clinical features across 412 patients, 70 significant findings detected with P value < 0.05 and Q value < 0.25.

4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_N_STAGE', and 'RACE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'PATHOLOGIC_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_OF_LYMPH_NODES', 'RACE', and 'ETHNICITY'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', and 'NUMBER_OF_LYMPH_NODES'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'NUMBER_OF_LYMPH_NODES', 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 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 70 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	0.906 (0.939)	0.219 (0.296)	0.0474 (0.0906)	0.0186 (0.0439)	0.071 (0.119)	0.00283 (0.00967)	0.00145 (0.00538)	0.984 (0.992)	0.114 (0.169)	0.0401 (0.0814)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.0052 (0.0154)	0.0577 (0.104)	0.0703 (0.119)	0.0499 (0.0927)	0.485 (0.573)	0.000113 (0.000668)	0.000845 (0.00333)	0.00516 (0.0154)	0.000666 (0.00294)	0.00111 (0.00425)
PATHOLOGIC STAGE	Fisher's exact test	0.0864 (0.14)	0.0108 (0.0285)	0.00037 (0.00178)	7e-05 (0.000433)	0.00039 (0.00181)	1e-05 (9.29e-05)	1e-05 (9.29e-05)	0.0007 (0.00294)	1e-05 (9.29e-05)	4e-05 (0.000289)
PATHOLOGY T STAGE	Fisher's exact test	0.524 (0.598)	0.0683 (0.118)	1e-05 (9.29e-05)	3e-05 (0.000244)	7e-05 (0.000433)	1e-05 (9.29e-05)	1e-05 (9.29e-05)	0.112 (0.169)	2e-05 (0.000173)	1e-05 (9.29e-05)
PATHOLOGY N STAGE	Fisher's exact test	0.00336 (0.0107)	0.283 (0.364)	0.0285 (0.0617)	0.0121 (0.0303)	0.0361 (0.0756)	1e-05 (9.29e-05)	0.00022 (0.00114)	0.00069 (0.00294)	1e-05 (9.29e-05)	0.00032 (0.0016)
PATHOLOGY M STAGE	Fisher's exact test	0.492 (0.576)	0.948 (0.963)	0.0923 (0.146)	0.0321 (0.0683)	0.0858 (0.14)	0.307 (0.384)	0.00628 (0.0181)	0.447 (0.538)	0.644 (0.716)	0.297 (0.374)
GENDER	Fisher's exact test	0.545 (0.616)	0.266 (0.349)	0.114 (0.169)	0.00194 (0.00701)	0.918 (0.939)	0.0118 (0.0301)	0.166 (0.229)	0.0412 (0.0822)	0.352 (0.432)	0.29 (0.37)
RADIATION THERAPY	Fisher's exact test	0.0597 (0.106)	0.102 (0.157)	0.0272 (0.06)	0.0488 (0.0919)	0.163 (0.228)	0.0146 (0.0358)	0.0885 (0.142)	0.0574 (0.104)	0.0169 (0.0406)	0.0221 (0.0495)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)	0.462 (0.551)	0.00512 (0.0154)	0.0202 (0.0469)	0.0368 (0.0759)	0.00874 (0.0242)	0.00707 (0.02)	0.00308 (0.01)	0.0648 (0.114)	0.00306 (0.01)	0.113 (0.169)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.998 (0.998)	0.916 (0.939)	0.161 (0.228)	0.523 (0.598)	0.68 (0.749)	0.714 (0.773)	0.118 (0.172)	0.582 (0.652)	0.762 (0.812)	0.755 (0.811)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.0716 (0.119)	0.0417 (0.0822)	0.274 (0.357)	0.146 (0.209)	0.101 (0.157)	2.26e-06 (9.29e-05)	0.00897 (0.0243)	0.00219 (0.00771)	4.91e-05 (0.000336)	0.0112 (0.029)
RACE	Fisher's exact test	0.00076 (0.00309)	0.00019 (0.00107)	1e-05 (9.29e-05)	1e-05 (9.29e-05)	1e-05 (9.29e-05)	1e-05 (9.29e-05)	4e-05 (0.000289)	0.198 (0.272)	0.00022 (0.00114)	0.0219 (0.0495)
ETHNICITY	Fisher's exact test	0.521 (0.598)	0.0469 (0.0906)	0.444 (0.538)	0.351 (0.432)	0.227 (0.305)	0.266 (0.349)	0.139 (0.2)	0.883 (0.926)	0.704 (0.769)	0.779 (0.823)

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

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

Cluster Labels	1	2	3	4	5
Number of samples	78	146	121	61	2

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

P value = 0.906 (logrank test), Q value = 0.94

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	403	178	0.5 - 166.0 (17.6)
subtype1	78	38	1.1 - 165.7 (19.2)
subtype2	146	65	0.5 - 166.0 (18.2)
subtype3	118	50	0.7 - 163.3 (16.0)
subtype4	61	25	2.7 - 142.8 (17.6)

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 = 0.0052 (Kruskal-Wallis (anova)), Q value = 0.015

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

	nPatients	Mean (Std.Dev)
ALL	405	68.1 (10.6)
subtype1	77	69.4 (10.7)
subtype2	146	69.8 (9.7)
subtype3	121	65.1 (11.5)
subtype4	61	68.3 (9.7)

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 = 0.0864 (Fisher's exact test), Q value = 0.14

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	129	139	134
subtype1	0	22	25	30
subtype2	1	40	46	59
subtype3	1	47	46	26
subtype4	0	20	22	19

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 = 0.524 (Fisher's exact test), Q value = 0.6

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

nPatients	T0+T1+T2	T3	T4
ALL	123	193	58
subtype1	23	39	13
subtype2	38	72	22
subtype3	42	50	18
subtype4	20	32	5

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 = 0.00336 (Fisher's exact test), Q value = 0.011

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

nPatients	N0	N1	N2	N3
ALL	235	46	75	8
subtype1	34	14	14	3
subtype2	76	17	36	3
subtype3	88	9	12	2
subtype4	37	6	13	0

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 = 0.492 (Fisher's exact test), Q value = 0.58

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

nPatients	0	1
ALL	191	11
subtype1	33	0
subtype2	66	5
subtype3	73	5
subtype4	19	1

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.545 (Fisher's exact test), Q value = 0.62

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

nPatients	FEMALE	MALE
ALL	106	300
subtype1	20	58
subtype2	43	103
subtype3	31	90
subtype4	12	49

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 = 0.0597 (Fisher's exact test), Q value = 0.11

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

nPatients	NO	YES
ALL	362	20
subtype1	71	2
subtype2	128	5
subtype3	106	12
subtype4	57	1

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 = 0.462 (Kruskal-Wallis (anova)), Q value = 0.55

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

	nPatients	Mean (Std.Dev)
ALL	134	83.0 (13.8)
subtype1	28	83.6 (15.9)
subtype2	52	84.2 (12.4)
subtype3	37	79.7 (15.5)
subtype4	17	85.3 (8.7)

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 = 0.998 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	221	39.0 (53.2)
subtype1	47	34.6 (21.5)
subtype2	80	44.4 (82.0)
subtype3	59	37.9 (30.3)
subtype4	35	34.5 (19.6)

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 'NUMBER_OF_LYMPH_NODES'

P value = 0.0716 (Kruskal-Wallis (anova)), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	295	2.1 (7.0)
subtype1	60	1.8 (3.8)
subtype2	111	2.2 (4.7)
subtype3	74	1.4 (4.1)
subtype4	50	3.4 (14.2)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00076 (Fisher's exact test), Q value = 0.0031

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	43	22	325
subtype1	5	1	65
subtype2	9	11	120
subtype3	26	6	87
subtype4	3	4	53

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.521 (Fisher's exact test), Q value = 0.6

Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	367
subtype1	3	68
subtype2	2	132
subtype3	2	113
subtype4	2	54

Figure S13. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S15. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	139	128	65	80

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.219 (logrank test), Q value = 0.3

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	409	180	0.4 - 166.0 (17.6)
subtype1	138	65	0.7 - 166.0 (16.8)
subtype2	127	48	0.4 - 130.9 (19.1)
subtype3	64	29	0.7 - 104.7 (15.5)
subtype4	80	38	0.5 - 86.3 (16.4)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0577 (Kruskal-Wallis (anova)), Q value = 0.1

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

	nPatients	Mean (Std.Dev)
ALL	411	68.1 (10.6)
subtype1	139	69.3 (9.7)
subtype2	127	69.3 (9.8)
subtype3	65	66.0 (11.6)
subtype4	80	65.8 (12.0)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.0108 (Fisher's exact test), Q value = 0.029

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	131	141	136
subtype1	0	31	64	44
subtype2	1	43	37	45
subtype3	1	24	16	24
subtype4	0	33	24	23

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0683 (Fisher's exact test), Q value = 0.12

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	124	196	59
subtype1	29	81	20
subtype2	41	54	18
subtype3	23	27	10
subtype4	31	34	11

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.283 (Fisher's exact test), Q value = 0.36

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	239	47	76	8
subtype1	84	17	23	3
subtype2	67	17	24	3
subtype3	37	3	19	1
subtype4	51	10	10	1

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.948 (Fisher's exact test), Q value = 0.96

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	196	11
subtype1	52	4
subtype2	68	3
subtype3	40	2
subtype4	36	2

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.266 (Fisher's exact test), Q value = 0.35

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	108	304
subtype1	42	97
subtype2	36	92
subtype3	12	53
subtype4	18	62

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.102 (Fisher's exact test), Q value = 0.16

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	366	20
subtype1	126	5
subtype2	115	3
subtype3	55	6
subtype4	70	6

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00512 (Kruskal-Wallis (anova)), Q value = 0.015

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	136	83.1 (13.7)
subtype1	40	87.8 (10.0)
subtype2	43	84.7 (11.2)
subtype3	25	80.4 (11.7)
subtype4	28	76.4 (19.7)

Figure S22. 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 = 0.916 (Kruskal-Wallis (anova)), Q value = 0.94

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	224	39.0 (52.9)
subtype1	80	37.7 (28.1)
subtype2	70	34.4 (24.4)
subtype3	31	34.1 (21.9)
subtype4	43	52.6 (108.7)

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0417 (Kruskal-Wallis (anova)), Q value = 0.082

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	297	2.1 (7.0)
subtype1	113	1.2 (2.5)
subtype2	84	2.1 (4.8)
subtype3	41	5.7 (16.2)
subtype4	59	1.2 (3.4)

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00019 (Fisher's exact test), Q value = 0.0011

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	327
subtype1	5	8	121
subtype2	14	7	100
subtype3	18	2	43
subtype4	7	6	63

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.0469 (Fisher's exact test), Q value = 0.091

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	371
subtype1	3	126
subtype2	1	115
subtype3	0	61
subtype4	5	69

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S29. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	123	116	105

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0474 (logrank test), Q value = 0.091

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	341	155	0.4 - 166.0 (17.6)
subtype1	123	68	0.7 - 166.0 (16.8)
subtype2	114	36	0.4 - 97.1 (16.7)
subtype3	104	51	0.9 - 165.7 (19.2)

Figure S27. 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 = 0.0703 (Kruskal-Wallis (anova)), Q value = 0.12

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	343	67.9 (10.5)
subtype1	123	69.0 (9.0)
subtype2	116	65.8 (11.6)
subtype3	104	68.8 (10.7)

Figure S28. 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 = 0.00037 (Fisher's exact test), Q value = 0.0018

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	105	119	116
subtype1	0	30	48	45
subtype2	2	53	31	29
subtype3	0	22	40	42

Figure S29. 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 = 1e-05 (Fisher's exact test), Q value = 9.3e-05

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	100	169	48
subtype1	20	73	17
subtype2	52	39	11
subtype3	28	57	20

Figure S30. 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 = 0.0285 (Fisher's exact test), Q value = 0.062

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	201	39	66	7
subtype1	66	13	30	1
subtype2	79	11	14	1
subtype3	56	15	22	5

Figure S31. 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 = 0.0923 (Fisher's exact test), Q value = 0.15

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	163	10
subtype1	41	6
subtype2	72	2
subtype3	50	2

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.114 (Fisher's exact test), Q value = 0.17

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	85	259
subtype1	38	85
subtype2	27	89
subtype3	20	85

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0272 (Fisher's exact test), Q value = 0.06

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	306	14
subtype1	109	4
subtype2	98	9
subtype3	99	1

Figure S34. 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 = 0.0202 (Kruskal-Wallis (anova)), Q value = 0.047

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

	nPatients	Mean (Std.Dev)
ALL	117	83.7 (12.8)
subtype1	47	87.2 (6.5)
subtype2	39	78.7 (16.3)
subtype3	31	84.5 (13.4)

Figure S35. 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 = 0.161 (Kruskal-Wallis (anova)), Q value = 0.23

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

	nPatients	Mean (Std.Dev)
ALL	187	39.8 (56.9)
subtype1	74	49.0 (85.1)
subtype2	60	33.1 (27.4)
subtype3	53	34.7 (18.7)

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.274 (Kruskal-Wallis (anova)), Q value = 0.36

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

	nPatients	Mean (Std.Dev)
ALL	253	2.2 (7.5)
subtype1	96	2.2 (4.7)
subtype2	69	2.6 (12.2)
subtype3	88	1.9 (4.5)

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 9.3e-05

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	40	20	268
subtype1	5	6	106
subtype2	32	8	71
subtype3	3	6	91

Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.444 (Fisher's exact test), Q value = 0.54

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	305
subtype1	4	112
subtype2	1	103
subtype3	3	90

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

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S43. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	90	122	100	32

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0186 (logrank test), Q value = 0.044

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	341	155	0.4 - 166.0 (17.6)
subtype1	89	52	1.1 - 166.0 (16.8)
subtype2	121	38	0.4 - 97.1 (17.8)
subtype3	99	52	1.2 - 165.7 (17.2)
subtype4	32	13	0.7 - 125.5 (19.8)

Figure S40. 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 = 0.0499 (Kruskal-Wallis (anova)), Q value = 0.093

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	343	67.9 (10.5)
subtype1	90	69.4 (8.6)
subtype2	121	66.1 (11.5)
subtype3	100	69.3 (10.9)
subtype4	32	65.7 (9.1)

Figure S41. 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 = 7e-05 (Fisher's exact test), Q value = 0.00043

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	105	119	116
subtype1	0	14	36	40
subtype2	2	55	34	29
subtype3	0	22	39	39
subtype4	0	14	10	8

Figure S42. 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 = 3e-05 (Fisher's exact test), Q value = 0.00024

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

nPatients	T0+T1+T2	T3	T4
ALL	100	169	48
subtype1	15	56	18
subtype2	58	40	14
subtype3	22	59	15
subtype4	5	14	1

Figure S43. 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 = 0.0121 (Fisher's exact test), Q value = 0.03

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	201	39	66	7
subtype1	44	10	28	2
subtype2	82	10	17	1
subtype3	56	18	17	3
subtype4	19	1	4	1

Figure S44. 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 = 0.0321 (Fisher's exact test), Q value = 0.068

Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	163	10
subtype1	34	3
subtype2	77	2
subtype3	42	2
subtype4	10	3

Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.00194 (Fisher's exact test), Q value = 0.007

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	85	259
subtype1	33	57
subtype2	22	100
subtype3	18	82
subtype4	12	20

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0488 (Fisher's exact test), Q value = 0.092

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	306	14
subtype1	79	2
subtype2	108	8
subtype3	92	1
subtype4	27	3

Figure S47. 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 = 0.0368 (Kruskal-Wallis (anova)), Q value = 0.076

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	117	83.7 (12.8)
subtype1	24	87.1 (6.9)
subtype2	41	78.8 (16.0)
subtype3	34	85.3 (12.6)
subtype4	18	87.2 (6.7)

Figure S48. 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 = 0.523 (Kruskal-Wallis (anova)), Q value = 0.6

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	187	39.8 (56.9)
subtype1	55	40.5 (30.5)
subtype2	61	34.3 (26.1)
subtype3	52	48.3 (98.4)
subtype4	19	32.2 (23.2)

Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.146 (Kruskal-Wallis (anova)), Q value = 0.21

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	253	2.2 (7.5)
subtype1	77	2.5 (5.0)
subtype2	75	2.7 (11.8)
subtype3	82	1.8 (4.5)
subtype4	19	1.1 (2.8)

Figure S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 9.3e-05

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	40	20	268
subtype1	3	3	79
subtype2	32	7	77
subtype3	5	7	84
subtype4	0	3	28

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.351 (Fisher's exact test), Q value = 0.43

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	305
subtype1	3	80
subtype2	1	106
subtype3	4	87
subtype4	0	32

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S57. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	72	137	38	104	57

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.071 (logrank test), Q value = 0.12

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	405	178	0.4 - 166.0 (17.6)
subtype1	71	39	1.1 - 166.0 (18.1)
subtype2	135	43	0.4 - 112.4 (17.4)
subtype3	38	15	2.8 - 104.7 (16.8)
subtype4	104	51	1.2 - 165.7 (16.1)
subtype5	57	30	0.5 - 130.9 (17.9)

Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.485 (Kruskal-Wallis (anova)), Q value = 0.57

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

	nPatients	Mean (Std.Dev)
ALL	407	68.1 (10.6)
subtype1	72	70.1 (8.6)
subtype2	136	68.2 (11.6)
subtype3	38	66.2 (11.0)
subtype4	104	67.5 (11.0)
subtype5	57	67.5 (9.4)

Figure S54. 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 = 0.00039 (Fisher's exact test), Q value = 0.0018

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	130	140	134
subtype1	0	9	30	33
subtype2	2	60	34	39
subtype3	0	16	11	11
subtype4	0	29	41	34
subtype5	0	16	24	17

Figure S55. 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 = 7e-05 (Fisher's exact test), Q value = 0.00043

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

nPatients	T0+T1+T2	T3	T4
ALL	123	194	58
subtype1	9	50	12
subtype2	60	50	16
subtype3	15	14	6
subtype4	28	52	17
subtype5	11	28	7

Figure S56. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0361 (Fisher's exact test), Q value = 0.076

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

nPatients	N0	N1	N2	N3
ALL	237	46	75	8
subtype1	36	9	23	1
subtype2	82	9	29	2
subtype3	23	4	6	0
subtype4	64	17	12	3
subtype5	32	7	5	2

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0858 (Fisher's exact test), Q value = 0.14

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	196	11
subtype1	24	3
subtype2	82	2
subtype3	21	0
subtype4	41	2
subtype5	28	4

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.918 (Fisher's exact test), Q value = 0.94

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

nPatients	FEMALE	MALE
ALL	107	301
subtype1	21	51
subtype2	34	103
subtype3	10	28
subtype4	29	75
subtype5	13	44

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.163 (Fisher's exact test), Q value = 0.23

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	362	20
subtype1	62	4
subtype2	118	9
subtype3	35	3
subtype4	97	1
subtype5	50	3

Figure S60. 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 = 0.00874 (Kruskal-Wallis (anova)), Q value = 0.024

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

	nPatients	Mean (Std.Dev)
ALL	134	83.0 (13.8)
subtype1	22	87.3 (9.4)
subtype2	48	79.0 (14.5)
subtype3	9	74.4 (23.5)
subtype4	32	85.3 (13.4)
subtype5	23	87.4 (6.9)

Figure S61. 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 = 0.68 (Kruskal-Wallis (anova)), Q value = 0.75

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

	nPatients	Mean (Std.Dev)
ALL	222	39.2 (53.1)
subtype1	46	41.8 (31.6)
subtype2	66	37.1 (29.4)
subtype3	18	29.8 (17.9)
subtype4	56	46.5 (95.1)
subtype5	36	32.9 (19.4)

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.101 (Kruskal-Wallis (anova)), Q value = 0.16

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

	nPatients	Mean (Std.Dev)
ALL	293	2.1 (7.0)
subtype1	63	2.6 (4.9)
subtype2	86	3.1 (11.3)
subtype3	27	2.1 (6.3)
subtype4	82	1.3 (3.3)
subtype5	35	0.6 (1.0)

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 9.3e-05

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	324
subtype1	3	1	68
subtype2	32	6	94
subtype3	3	2	29
subtype4	3	10	87
subtype5	3	4	46

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.227 (Fisher's exact test), Q value = 0.3

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	368
subtype1	1	69
subtype2	1	127
subtype3	2	31
subtype4	2	92
subtype5	2	49

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S71. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	119	113	176

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00283 (logrank test), Q value = 0.0097

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	405	178	0.4 - 166.0 (17.6)
subtype1	118	60	1.1 - 130.9 (18.7)
subtype2	111	27	0.4 - 94.9 (16.7)
subtype3	176	91	0.5 - 166.0 (16.2)

Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000113 (Kruskal-Wallis (anova)), Q value = 0.00067

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	407	68.1 (10.6)
subtype1	119	71.3 (9.5)
subtype2	112	65.1 (10.8)
subtype3	176	67.8 (10.7)

Figure S67. 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 = 9.3e-05

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	130	140	134
subtype1	0	17	40	62
subtype2	2	65	27	17
subtype3	0	48	73	55

Figure S68. 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 = 1e-05 (Fisher's exact test), Q value = 9.3e-05

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	123	194	58
subtype1	21	68	25
subtype2	59	29	10
subtype3	43	97	23

Figure S69. 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 = 1e-05 (Fisher's exact test), Q value = 9.3e-05

Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	237	46	75	8
subtype1	52	17	40	4
subtype2	80	5	10	1
subtype3	105	24	25	3

Figure S70. 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 = 0.307 (Fisher's exact test), Q value = 0.38

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

nPatients	0	1
ALL	196	11
subtype1	49	4
subtype2	78	2
subtype3	69	5

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0118 (Fisher's exact test), Q value = 0.03

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	301
subtype1	31	88
subtype2	19	94
subtype3	57	119

Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0146 (Fisher's exact test), Q value = 0.036

Table S79. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	362	20
subtype1	104	3
subtype2	100	12
subtype3	158	5

Figure S73. 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 = 0.00707 (Kruskal-Wallis (anova)), Q value = 0.02

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	134	83.0 (13.8)
subtype1	32	85.0 (12.2)
subtype2	43	78.4 (15.6)
subtype3	59	85.3 (12.5)

Figure S74. 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 = 0.714 (Kruskal-Wallis (anova)), Q value = 0.77

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	222	39.2 (53.1)
subtype1	58	39.0 (31.3)
subtype2	65	34.3 (25.0)
subtype3	99	42.4 (73.3)

Figure S75. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 2.26e-06 (Kruskal-Wallis (anova)), Q value = 9.3e-05

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	293	2.1 (7.0)
subtype1	97	2.7 (4.7)
subtype2	60	2.8 (13.2)
subtype3	136	1.4 (3.9)

Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 9.3e-05

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	324
subtype1	6	8	103
subtype2	32	4	72
subtype3	6	11	149

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.266 (Fisher's exact test), Q value = 0.35

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	368
subtype1	1	107
subtype2	1	105
subtype3	6	156

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S85. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	105	75	115	25	89

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00145 (logrank test), Q value = 0.0054

Table S86. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	406	178	0.4 - 166.0 (17.7)
subtype1	105	50	0.5 - 165.7 (18.0)
subtype2	74	35	1.2 - 130.9 (15.7)
subtype3	114	32	0.4 - 112.4 (19.0)
subtype4	25	15	0.6 - 110.6 (17.2)
subtype5	88	46	1.1 - 166.0 (16.5)

Figure S79. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000845 (Kruskal-Wallis (anova)), Q value = 0.0033

Table S87. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	408	68.1 (10.6)
subtype1	105	68.0 (11.3)
subtype2	74	70.3 (10.0)
subtype3	115	65.4 (10.9)
subtype4	25	64.7 (9.8)
subtype5	89	70.6 (9.2)

Figure S80. 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 = 9.3e-05

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	131	139	135
subtype1	0	32	44	29
subtype2	1	16	21	36
subtype3	1	62	29	22
subtype4	0	5	10	10
subtype5	0	16	35	38

Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.3e-05

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	124	194	58
subtype1	26	56	13
subtype2	20	38	14
subtype3	59	31	11
subtype4	4	13	5
subtype5	15	56	15

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00022 (Fisher's exact test), Q value = 0.0011

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	237	46	76	8
subtype1	66	15	11	1
subtype2	34	13	18	4
subtype3	78	6	14	2
subtype4	13	2	5	1
subtype5	46	10	28	0

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.00628 (Fisher's exact test), Q value = 0.018

Table S91. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	195	11
subtype1	47	0
subtype2	29	2
subtype3	77	3
subtype4	11	4
subtype5	31	2

Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.166 (Fisher's exact test), Q value = 0.23

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

nPatients	FEMALE	MALE
ALL	107	302
subtype1	30	75
subtype2	16	59
subtype3	26	89
subtype4	4	21
subtype5	31	58

Figure S85. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.0885 (Fisher's exact test), Q value = 0.14

Table S93. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	363	20
subtype1	94	3
subtype2	71	2
subtype3	99	11
subtype4	22	2
subtype5	77	2

Figure S86. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00308 (Kruskal-Wallis (anova)), Q value = 0.01

Table S94. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	135	83.0 (13.7)
subtype1	34	84.1 (13.1)
subtype2	22	86.4 (10.0)
subtype3	45	76.9 (16.8)
subtype4	9	88.9 (6.0)
subtype5	25	87.6 (9.3)

Figure S87. 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 = 0.118 (Kruskal-Wallis (anova)), Q value = 0.17

Table S95. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	221	39.1 (53.2)
subtype1	56	32.8 (18.4)
subtype2	43	37.0 (28.0)
subtype3	64	31.2 (21.4)
subtype4	12	48.5 (27.8)
subtype5	46	57.5 (106.6)

Figure S88. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00897 (Kruskal-Wallis (anova)), Q value = 0.024

Table S96. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	295	2.1 (7.0)
subtype1	78	0.8 (1.9)
subtype2	62	4.1 (12.9)
subtype3	62	1.3 (4.3)
subtype4	18	3.2 (8.1)
subtype5	75	2.2 (4.5)

Figure S89. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00029

Table S97. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	324
subtype1	3	7	90
subtype2	6	4	62
subtype3	29	4	73
subtype4	2	2	21
subtype5	4	6	78

Figure S90. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.139 (Fisher's exact test), Q value = 0.2

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	368
subtype1	1	96
subtype2	3	67
subtype3	2	101
subtype4	2	19
subtype5	1	85

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S99. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	269	84	56

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.984 (logrank test), Q value = 0.99

Table S100. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	406	178	0.4 - 166.0 (17.7)
subtype1	268	116	0.4 - 166.0 (17.9)
subtype2	83	37	1.2 - 130.9 (17.4)
subtype3	55	25	2.1 - 163.3 (17.6)

Figure S92. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00516 (Kruskal-Wallis (anova)), Q value = 0.015

Table S101. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	408	68.1 (10.6)
subtype1	269	66.8 (10.9)
subtype2	83	70.4 (9.6)
subtype3	56	70.5 (9.8)

Figure S93. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 7e-04 (Fisher's exact test), Q value = 0.0029

Table S102. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	131	139	135
subtype1	1	101	96	70
subtype2	1	19	24	39
subtype3	0	11	19	26

Figure S94. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.112 (Fisher's exact test), Q value = 0.17

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	124	194	58
subtype1	90	118	34
subtype2	22	41	16
subtype3	12	35	8

Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.00069 (Fisher's exact test), Q value = 0.0029

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	237	46	76	8
subtype1	168	26	35	4
subtype2	40	14	21	4
subtype3	29	6	20	0

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.447 (Fisher's exact test), Q value = 0.54

Table S105. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	195	11
subtype1	142	8
subtype2	35	1
subtype3	18	2

Figure S97. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0412 (Fisher's exact test), Q value = 0.082

Table S106. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	302
subtype1	68	201
subtype2	17	67
subtype3	22	34

Figure S98. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.0574 (Fisher's exact test), Q value = 0.1

Table S107. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	363	20
subtype1	235	18
subtype2	80	2
subtype3	48	0

Figure S99. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0648 (Kruskal-Wallis (anova)), Q value = 0.11

Table S108. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	135	83.0 (13.7)
subtype1	94	81.4 (14.8)
subtype2	24	87.1 (10.0)
subtype3	17	86.5 (10.6)

Figure S100. 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 = 0.582 (Kruskal-Wallis (anova)), Q value = 0.65

Table S109. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	221	39.1 (53.2)
subtype1	146	39.2 (62.1)
subtype2	44	36.3 (24.7)
subtype3	31	43.0 (35.2)

Figure S101. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00219 (Kruskal-Wallis (anova)), Q value = 0.0077

Table S110. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	295	2.1 (7.0)
subtype1	175	1.4 (4.4)
subtype2	70	3.8 (12.1)
subtype3	50	2.1 (3.6)

Figure S102. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.198 (Fisher's exact test), Q value = 0.27

Table S111. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	324
subtype1	35	14	204
subtype2	7	5	70
subtype3	2	4	50

Figure S103. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.883 (Fisher's exact test), Q value = 0.93

Table S112. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	368
subtype1	7	237
subtype2	1	77
subtype3	1	54

Figure S104. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S113. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	125	52	129	92

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

P value = 0.114 (logrank test), Q value = 0.17

Table S114. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	395	176	0.4 - 166.0 (17.6)
subtype1	124	65	1.1 - 166.0 (17.9)
subtype2	51	27	1.5 - 130.9 (16.0)
subtype3	128	42	0.4 - 110.6 (17.9)
subtype4	92	42	1.2 - 165.7 (16.5)

Figure S105. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000666 (Kruskal-Wallis (anova)), Q value = 0.0029

Table S115. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	397	68.2 (10.6)
subtype1	125	70.3 (9.8)
subtype2	51	70.9 (9.0)
subtype3	129	65.4 (10.8)
subtype4	92	67.6 (11.1)

Figure S106. 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 = 1e-05 (Fisher's exact test), Q value = 9.3e-05

Table S116. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	124	135	135
subtype1	0	22	45	58
subtype2	1	9	15	26
subtype3	1	62	39	26
subtype4	0	31	36	25

Figure S107. 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 = 0.00017

Table S117. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	120	190	58
subtype1	23	78	19
subtype2	13	26	11
subtype3	57	39	16
subtype4	27	47	12

Figure S108. 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 = 1e-05 (Fisher's exact test), Q value = 9.3e-05

Table S118. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	230	46	76	8
subtype1	61	17	40	1
subtype2	22	9	14	3
subtype3	87	8	12	2
subtype4	60	12	10	2

Figure S109. 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 = 0.644 (Fisher's exact test), Q value = 0.72

Table S119. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	188	11
subtype1	49	5
subtype2	23	1
subtype3	82	4
subtype4	34	1

Figure S110. 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 = 0.352 (Fisher's exact test), Q value = 0.43

Table S120. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	291
subtype1	38	87
subtype2	13	39
subtype3	28	101
subtype4	28	64

Figure S111. 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 = 0.0169 (Fisher's exact test), Q value = 0.041

Table S121. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	354	19
subtype1	112	3
subtype2	50	1
subtype3	109	13
subtype4	83	2

Figure S112. 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 = 0.00306 (Kruskal-Wallis (anova)), Q value = 0.01

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

	nPatients	Mean (Std.Dev)
ALL	130	83.1 (13.9)
subtype1	41	87.3 (9.0)
subtype2	14	86.4 (11.5)
subtype3	47	77.4 (16.5)
subtype4	28	84.6 (13.7)

Figure S113. 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 = 0.762 (Kruskal-Wallis (anova)), Q value = 0.81

Table S123. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	216	39.6 (53.7)
subtype1	62	48.7 (92.9)
subtype2	33	39.2 (29.1)
subtype3	70	33.2 (21.6)
subtype4	51	37.4 (22.4)

Figure S114. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 4.91e-05 (Kruskal-Wallis (anova)), Q value = 0.00034

Table S124. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	291	2.1 (7.1)
subtype1	102	2.5 (5.1)
subtype2	43	3.1 (5.3)
subtype3	74	2.4 (11.8)
subtype4	72	0.8 (1.8)

Figure S115. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.00022 (Fisher's exact test), Q value = 0.0011

Table S125. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	41	22	317
subtype1	7	5	113
subtype2	3	3	44
subtype3	27	6	85
subtype4	4	8	75

Figure S116. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.704 (Fisher's exact test), Q value = 0.77

Table S126. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	357
subtype1	3	118
subtype2	0	48
subtype3	3	110
subtype4	3	81

Figure S117. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S127. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	88	61	42	133	74

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

P value = 0.0401 (logrank test), Q value = 0.081

Table S128. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	395	176	0.4 - 166.0 (17.6)
subtype1	87	45	1.1 - 163.3 (16.8)
subtype2	60	32	1.5 - 130.9 (16.7)
subtype3	42	25	2.0 - 166.0 (19.2)
subtype4	132	39	0.4 - 110.6 (17.9)
subtype5	74	35	1.2 - 165.7 (16.3)

Figure S118. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00111 (Kruskal-Wallis (anova)), Q value = 0.0042

Table S129. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	397	68.2 (10.6)
subtype1	88	69.3 (9.8)
subtype2	60	71.2 (9.2)
subtype3	42	70.8 (9.4)
subtype4	133	65.2 (10.9)
subtype5	74	68.1 (11.3)

Figure S119. 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 = 4e-05 (Fisher's exact test), Q value = 0.00029

Table S130. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	124	135	135
subtype1	0	18	33	37
subtype2	1	12	18	29
subtype3	0	9	17	16
subtype4	1	65	36	30
subtype5	0	20	31	23

Figure S120. 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 = 9.3e-05

Table S131. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	120	190	58
subtype1	18	52	14
subtype2	15	30	14
subtype3	9	25	6
subtype4	62	38	15
subtype5	16	45	9

Figure S121. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00032 (Fisher's exact test), Q value = 0.0016

Table S132. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	230	46	76	8
subtype1	45	8	29	0
subtype2	28	10	17	3
subtype3	24	7	8	1
subtype4	89	9	13	2
subtype5	44	12	9	2

Figure S122. 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 = 0.297 (Fisher's exact test), Q value = 0.37

Table S133. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	188	11
subtype1	39	2
subtype2	24	1
subtype3	18	3
subtype4	77	5
subtype5	30	0

Figure S123. 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 = 0.29 (Fisher's exact test), Q value = 0.37

Table S134. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	291
subtype1	30	58
subtype2	14	47
subtype3	12	30
subtype4	29	104
subtype5	22	52

Figure S124. 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 = 0.0221 (Fisher's exact test), Q value = 0.049

Table S135. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	354	19
subtype1	78	1
subtype2	58	2
subtype3	37	2
subtype4	112	13
subtype5	69	1

Figure S125. 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 = 0.113 (Kruskal-Wallis (anova)), Q value = 0.17

Table S136. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	130	83.1 (13.9)
subtype1	25	86.0 (10.4)
subtype2	19	87.9 (10.3)
subtype3	12	85.0 (8.0)
subtype4	50	79.2 (16.6)
subtype5	24	83.3 (14.3)

Figure S126. 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 = 0.755 (Kruskal-Wallis (anova)), Q value = 0.81

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

	nPatients	Mean (Std.Dev)
ALL	216	39.6 (53.7)
subtype1	46	37.8 (31.5)
subtype2	35	37.1 (26.2)
subtype3	21	72.8 (152.3)
subtype4	72	34.7 (23.8)
subtype5	42	35.4 (22.5)

Figure S127. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0112 (Kruskal-Wallis (anova)), Q value = 0.029

Table S138. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	291	2.1 (7.1)
subtype1	69	2.1 (3.3)
subtype2	53	2.8 (4.9)
subtype3	32	2.0 (6.0)
subtype4	81	2.2 (11.3)
subtype5	56	1.5 (4.3)

Figure S128. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0219 (Fisher's exact test), Q value = 0.049

Table S139. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	41	22	317
subtype1	5	5	78
subtype2	3	3	53
subtype3	4	4	31
subtype4	25	6	94
subtype5	4	4	61

Figure S129. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.779 (Fisher's exact test), Q value = 0.82

Table S140. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	357
subtype1	1	83
subtype2	1	55
subtype3	1	37
subtype4	3	117
subtype5	3	65

Figure S130. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/BLCA-TP/22541056/BLCA-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BLCA-TP/22506467/BLCA-TP.merged_data.txt
Number of patients = 412
Number of clustering approaches = 10
Number of selected clinical features = 13
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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