Correlation between aggregated molecular cancer subtypes and selected clinical features

Lung Squamous Cell 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/C10001BZ

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 12 different clustering approaches and 15 clinical features across 502 patients, 33 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGIC_STAGE' and 'PATHOLOGY_T_STAGE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGIC_STAGE' and 'PATHOLOGY_T_STAGE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_N_STAGE', 'GENDER', and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH'.
CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', and 'GENDER'.
Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'GENDER', 'HISTOLOGICAL_TYPE', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', and 'YEAR_OF_TOBACCO_SMOKING_ONSET'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', 'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'RACE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 15 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 33 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	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	0.736 (0.872)	0.88 (0.946)	0.194 (0.501)	0.152 (0.442)	0.0681 (0.276)	0.697 (0.854)	0.0574 (0.262)	0.0817 (0.289)	0.258 (0.572)	0.751 (0.877)	0.0575 (0.262)	0.82 (0.911)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.924 (0.965)	0.645 (0.84)	0.0257 (0.178)	0.00629 (0.0666)	0.000222 (0.00998)	0.338 (0.628)	0.00966 (0.0828)	0.000733 (0.0188)	0.00136 (0.0263)	0.00748 (0.0708)	0.00399 (0.0552)	0.038 (0.214)
PATHOLOGIC STAGE	Fisher's exact test	0.00146 (0.0263)	0.00448 (0.0573)	0.417 (0.688)	0.663 (0.841)	0.00713 (0.0708)	0.563 (0.786)	0.322 (0.618)	0.0705 (0.276)	0.00209 (0.0342)	0.661 (0.841)	0.0979 (0.326)	0.366 (0.639)
PATHOLOGY T STAGE	Fisher's exact test	0.00045 (0.0162)	0.0001 (0.0099)	0.481 (0.73)	0.73 (0.871)	0.00509 (0.0573)	0.803 (0.911)	0.576 (0.791)	0.0946 (0.321)	0.0308 (0.191)	0.543 (0.786)	0.0337 (0.196)	0.155 (0.442)
PATHOLOGY N STAGE	Fisher's exact test	0.904 (0.957)	0.361 (0.639)	0.0214 (0.163)	0.578 (0.791)	0.256 (0.572)	0.883 (0.946)	0.364 (0.639)	0.913 (0.961)	0.413 (0.688)	0.866 (0.944)	0.283 (0.594)	0.213 (0.519)
PATHOLOGY M STAGE	Fisher's exact test	0.104 (0.341)	0.0697 (0.276)	0.458 (0.723)	0.458 (0.723)	0.275 (0.594)	0.462 (0.723)	0.0772 (0.278)	0.0734 (0.278)	0.3 (0.594)	0.354 (0.639)	0.489 (0.73)	0.731 (0.871)
GENDER	Fisher's exact test	0.334 (0.626)	0.0583 (0.262)	0.00383 (0.0552)	0.436 (0.702)	0.00011 (0.0099)	0.0772 (0.278)	0.491 (0.73)	0.0308 (0.191)	0.434 (0.702)	0.748 (0.877)	0.351 (0.639)	0.806 (0.911)
RADIATION THERAPY	Fisher's exact test	1 (1.00)	1 (1.00)	0.476 (0.73)	0.58 (0.791)	0.685 (0.849)	0.876 (0.946)	0.955 (0.988)	0.288 (0.594)	0.562 (0.786)	0.555 (0.786)	0.303 (0.594)	0.0491 (0.26)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)	0.927 (0.965)	0.26 (0.572)	0.688 (0.849)	0.0547 (0.262)	0.247 (0.571)	0.151 (0.442)	0.558 (0.786)	0.327 (0.619)	0.000562 (0.0169)	0.196 (0.501)	0.364 (0.639)	0.0243 (0.175)
HISTOLOGICAL TYPE	Fisher's exact test	0.295 (0.594)	0.323 (0.618)	0.0327 (0.196)	0.0651 (0.276)	0.0527 (0.262)	0.771 (0.89)	0.304 (0.594)	0.0403 (0.22)	0.132 (0.416)	0.066 (0.276)	0.0894 (0.31)	0.0218 (0.163)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.485 (0.73)	0.999 (1.00)	0.126 (0.406)	0.196 (0.501)	0.589 (0.797)	0.301 (0.594)	0.381 (0.653)	0.768 (0.89)	0.986 (1.00)	0.617 (0.823)	0.437 (0.702)	0.283 (0.594)
YEAR OF TOBACCO SMOKING ONSET	Kruskal-Wallis (anova)	0.899 (0.957)	0.516 (0.755)	0.139 (0.43)	0.401 (0.674)	0.246 (0.571)	0.389 (0.661)	0.0758 (0.278)	0.00108 (0.0242)	0.000199 (0.00998)	0.631 (0.829)	0.00495 (0.0573)	0.00938 (0.0828)
RESIDUAL TUMOR	Fisher's exact test	0.564 (0.786)	0.818 (0.911)	0.154 (0.442)	0.516 (0.755)	0.63 (0.829)	0.175 (0.475)	0.729 (0.871)	0.61 (0.819)	0.169 (0.475)	0.204 (0.504)	0.797 (0.911)	0.675 (0.844)
RACE	Fisher's exact test	0.994 (1.00)	0.975 (1.00)	0.29 (0.594)	0.245 (0.571)	0.843 (0.931)	0.175 (0.475)	0.819 (0.911)	0.648 (0.84)	0.251 (0.571)	0.0269 (0.18)	0.476 (0.73)	0.0135 (0.11)
ETHNICITY	Fisher's exact test	0.249 (0.571)	0.0689 (0.276)	0.2 (0.501)	0.668 (0.841)	0.0515 (0.262)	0.177 (0.475)	0.142 (0.434)	0.666 (0.841)	0.73 (0.871)	0.856 (0.94)	0.379 (0.653)	0.2 (0.501)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	42	52	32	28

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.736 (logrank test), Q value = 0.87

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	152	71	0.4 - 173.8 (23.0)
subtype1	41	17	0.4 - 122.4 (21.7)
subtype2	51	24	0.4 - 129.0 (32.0)
subtype3	32	17	0.4 - 173.8 (18.5)
subtype4	28	13	0.4 - 133.7 (13.7)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.924 (Kruskal-Wallis (anova)), Q value = 0.96

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

	nPatients	Mean (Std.Dev)
ALL	152	66.5 (8.6)
subtype1	41	66.4 (7.7)
subtype2	51	66.5 (8.2)
subtype3	32	67.2 (9.8)
subtype4	28	66.0 (9.4)

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00146 (Fisher's exact test), Q value = 0.026

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

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	23	60	7	28	19	13	4
subtype1	2	21	0	9	6	2	2
subtype2	2	24	4	9	6	7	0
subtype3	9	11	2	4	4	2	0
subtype4	10	4	1	6	3	2	2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00045 (Fisher's exact test), Q value = 0.016

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

nPatients	T1	T2	T3	T4
ALL	30	100	12	12
subtype1	5	30	6	1
subtype2	4	41	1	6
subtype3	11	18	2	1
subtype4	10	11	3	4

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.904 (Fisher's exact test), Q value = 0.96

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

nPatients	N0	N1	N2	N3
ALL	96	40	13	5
subtype1	26	11	3	2
subtype2	28	17	5	2
subtype3	22	6	3	1
subtype4	20	6	2	0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.104 (Fisher's exact test), Q value = 0.34

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

nPatients	0	1
ALL	146	4
subtype1	38	2
subtype2	50	0
subtype3	32	0
subtype4	26	2

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.334 (Fisher's exact test), Q value = 0.63

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

nPatients	FEMALE	MALE
ALL	44	110
subtype1	9	33
subtype2	13	39
subtype3	11	21
subtype4	11	17

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

nPatients	NO	YES
ALL	98	11
subtype1	28	3
subtype2	34	4
subtype3	18	2
subtype4	18	2

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

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.927 (Kruskal-Wallis (anova)), Q value = 0.96

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

	nPatients	Mean (Std.Dev)
ALL	45	40.9 (41.9)
subtype1	9	34.4 (42.2)
subtype2	12	40.0 (42.6)
subtype3	11	40.9 (47.4)
subtype4	13	46.2 (40.5)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.295 (Fisher's exact test), Q value = 0.59

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	5	1	148
subtype1	0	0	42
subtype2	3	0	49
subtype3	1	0	31
subtype4	1	1	26

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.485 (Kruskal-Wallis (anova)), Q value = 0.73

Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	133	54.8 (36.5)
subtype1	38	60.2 (41.4)
subtype2	47	52.0 (25.5)
subtype3	27	48.6 (36.2)
subtype4	21	59.6 (47.7)

Figure S11. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'mRNA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.899 (Kruskal-Wallis (anova)), Q value = 0.96

Table S13. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	97	1958.0 (10.6)
subtype1	28	1957.0 (8.8)
subtype2	26	1958.5 (10.8)
subtype3	23	1957.7 (10.9)
subtype4	20	1959.2 (12.7)

Figure S12. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.564 (Fisher's exact test), Q value = 0.79

Table S14. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	139	3	2	5
subtype1	38	0	1	1
subtype2	47	1	1	1
subtype3	27	1	0	3
subtype4	27	1	0	0

Figure S13. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.994 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	3	7	93
subtype1	1	2	24
subtype2	1	3	28
subtype3	1	1	22
subtype4	0	1	19

Figure S14. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.249 (Fisher's exact test), Q value = 0.57

Table S16. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	89
subtype1	3	23
subtype2	1	26
subtype3	0	21
subtype4	0	19

Figure S15. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S17. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	47	56	51

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.88 (logrank test), Q value = 0.95

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	152	71	0.4 - 173.8 (23.0)
subtype1	46	22	0.4 - 122.4 (21.4)
subtype2	55	26	0.4 - 129.0 (29.9)
subtype3	51	23	0.4 - 173.8 (21.1)

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.645 (Kruskal-Wallis (anova)), Q value = 0.84

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	152	66.5 (8.6)
subtype1	46	66.0 (8.7)
subtype2	55	66.2 (8.0)
subtype3	51	67.4 (9.1)

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00448 (Fisher's exact test), Q value = 0.057

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	23	60	7	28	19	13	4
subtype1	4	20	2	9	7	2	3
subtype2	2	25	4	10	7	8	0
subtype3	17	15	1	9	5	3	1

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-04 (Fisher's exact test), Q value = 0.0099

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	30	100	12	12
subtype1	6	32	7	2
subtype2	4	44	1	7
subtype3	20	24	4	3

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.361 (Fisher's exact test), Q value = 0.64

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	96	40	13	5
subtype1	28	14	4	1
subtype2	30	18	5	3
subtype3	38	8	4	1

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0697 (Fisher's exact test), Q value = 0.28

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	146	4
subtype1	42	3
subtype2	54	0
subtype3	50	1

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0583 (Fisher's exact test), Q value = 0.26

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	44	110
subtype1	10	37
subtype2	13	43
subtype3	21	30

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

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

Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	98	11
subtype1	34	4
subtype2	35	4
subtype3	29	3

Figure S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.26 (Kruskal-Wallis (anova)), Q value = 0.57

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	45	40.9 (41.9)
subtype1	15	30.7 (40.1)
subtype2	14	37.9 (40.8)
subtype3	16	53.1 (43.9)

Figure S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.323 (Fisher's exact test), Q value = 0.62

Table S27. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	5	1	148
subtype1	0	1	46
subtype2	3	0	53
subtype3	2	0	49

Figure S25. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.999 (Kruskal-Wallis (anova)), Q value = 1

Table S28. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	133	54.8 (36.5)
subtype1	41	57.5 (41.9)
subtype2	51	51.1 (24.6)
subtype3	41	56.8 (43.0)

Figure S26. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'mRNA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.516 (Kruskal-Wallis (anova)), Q value = 0.76

Table S29. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	97	1958.0 (10.6)
subtype1	32	1956.4 (8.9)
subtype2	29	1959.5 (10.7)
subtype3	36	1958.3 (11.9)

Figure S27. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.818 (Fisher's exact test), Q value = 0.91

Table S30. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	139	3	2	5
subtype1	42	0	1	2
subtype2	51	1	1	1
subtype3	46	2	0	2

Figure S28. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.975 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	3	7	93
subtype1	1	2	27
subtype2	1	3	31
subtype3	1	2	35

Figure S29. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0689 (Fisher's exact test), Q value = 0.28

Table S32. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	89
subtype1	3	25
subtype2	1	29
subtype3	0	35

Figure S30. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S33. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	182	164	153

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

P value = 0.194 (logrank test), Q value = 0.5

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	487	212	0.0 - 173.8 (21.0)
subtype1	178	85	0.1 - 173.8 (19.0)
subtype2	159	68	0.2 - 150.2 (27.2)
subtype3	150	59	0.0 - 122.4 (20.7)

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0257 (Kruskal-Wallis (anova)), Q value = 0.18

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	490	67.3 (8.6)
subtype1	179	67.8 (9.3)
subtype2	161	66.0 (8.1)
subtype3	150	68.2 (8.0)

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.417 (Fisher's exact test), Q value = 0.69

Table S36. Clustering Approach #3: '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 IV
ALL	2	89	152	2	66	94	3	63	19	7
subtype1	0	36	62	1	21	37	0	18	5	1
subtype2	0	22	48	1	27	29	2	23	9	3
subtype3	2	31	42	0	18	28	1	22	5	3

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.481 (Fisher's exact test), Q value = 0.73

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	112	293	70	24
subtype1	41	104	31	6
subtype2	32	100	23	9
subtype3	39	89	16	9

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0214 (Fisher's exact test), Q value = 0.16

Table S38. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	316	132	40	5
subtype1	129	37	10	2
subtype2	88	57	16	2
subtype3	99	38	14	1

Figure S35. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.458 (Fisher's exact test), Q value = 0.72

Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	407	7
subtype1	147	1
subtype2	134	3
subtype3	126	3

Figure S36. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00383 (Fisher's exact test), Q value = 0.055

Table S40. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	129	370
subtype1	63	119
subtype2	34	130
subtype3	32	121

Figure S37. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.476 (Fisher's exact test), Q value = 0.73

Table S41. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	370	49
subtype1	137	18
subtype2	123	20
subtype3	110	11

Figure S38. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.688 (Kruskal-Wallis (anova)), Q value = 0.85

Table S42. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	162	60.6 (40.8)
subtype1	53	63.4 (40.3)
subtype2	57	60.4 (40.7)
subtype3	52	58.1 (42.1)

Figure S39. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0327 (Fisher's exact test), Q value = 0.2

Table S43. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	15	6	1	477
subtype1	1	4	1	176
subtype2	9	1	0	154
subtype3	5	1	0	147

Figure S40. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.126 (Kruskal-Wallis (anova)), Q value = 0.41

Table S44. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	424	53.0 (31.2)
subtype1	152	50.7 (32.1)
subtype2	140	53.8 (31.2)
subtype3	132	54.7 (30.3)

Figure S41. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.139 (Kruskal-Wallis (anova)), Q value = 0.43

Table S45. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	320	1960.4 (11.6)
subtype1	125	1959.5 (12.0)
subtype2	94	1962.2 (11.3)
subtype3	101	1960.0 (11.2)

Figure S42. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.154 (Fisher's exact test), Q value = 0.44

Table S46. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	398	12	4	22
subtype1	141	5	2	11
subtype2	130	5	2	9
subtype3	127	2	0	2

Figure S43. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.29 (Fisher's exact test), Q value = 0.59

Table S47. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	9	31	348
subtype1	6	8	128
subtype2	2	11	116
subtype3	1	12	104

Figure S44. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.2 (Fisher's exact test), Q value = 0.5

Table S48. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	316
subtype1	6	122
subtype2	1	98
subtype3	1	96

Figure S45. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S49. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	153	123	92

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.152 (logrank test), Q value = 0.44

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	356	154	0.0 - 173.8 (21.0)
subtype1	149	69	0.0 - 173.8 (18.4)
subtype2	119	51	0.1 - 154.3 (28.3)
subtype3	88	34	0.4 - 140.1 (22.2)

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00629 (Kruskal-Wallis (anova)), Q value = 0.067

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	359	67.6 (8.7)
subtype1	150	68.8 (9.4)
subtype2	121	65.8 (8.4)
subtype3	88	68.0 (7.5)

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.663 (Fisher's exact test), Q value = 0.84

Table S52. Clustering Approach #4: '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 IV
ALL	3	71	98	3	61	70	3	47	6	4
subtype1	1	31	37	1	22	37	1	17	3	3
subtype2	1	22	40	2	23	18	1	13	2	1
subtype3	1	18	21	0	16	15	1	17	1	0

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.73 (Fisher's exact test), Q value = 0.87

Table S53. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	90	206	59	13
subtype1	36	81	30	6
subtype2	31	69	19	4
subtype3	23	56	10	3

Figure S49. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.578 (Fisher's exact test), Q value = 0.79

Table S54. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	235	98	29
subtype1	98	39	11
subtype2	81	34	7
subtype3	56	25	11

Figure S50. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.458 (Fisher's exact test), Q value = 0.72

Table S55. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	283	4
subtype1	110	3
subtype2	99	1
subtype3	74	0

Figure S51. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.436 (Fisher's exact test), Q value = 0.7

Table S56. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	95	273
subtype1	45	108
subtype2	29	94
subtype3	21	71

Figure S52. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.58 (Fisher's exact test), Q value = 0.79

Table S57. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	291	39
subtype1	126	14
subtype2	96	16
subtype3	69	9

Figure S53. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0547 (Kruskal-Wallis (anova)), Q value = 0.26

Table S58. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	123	66.0 (38.9)
subtype1	43	74.7 (34.5)
subtype2	50	64.2 (40.1)
subtype3	30	56.7 (41.6)

Figure S54. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0651 (Fisher's exact test), Q value = 0.28

Table S59. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	12	5	1	350
subtype1	1	3	0	149
subtype2	7	1	0	115
subtype3	4	1	1	86

Figure S55. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.196 (Kruskal-Wallis (anova)), Q value = 0.5

Table S60. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	312	52.3 (29.4)
subtype1	129	49.7 (29.5)
subtype2	103	52.2 (26.4)
subtype3	80	56.5 (32.6)

Figure S56. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.401 (Kruskal-Wallis (anova)), Q value = 0.67

Table S61. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	237	1961.2 (11.7)
subtype1	97	1960.8 (12.3)
subtype2	77	1962.6 (12.1)
subtype3	63	1960.0 (10.1)

Figure S57. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.516 (Fisher's exact test), Q value = 0.76

Table S62. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	278	9	2	17
subtype1	110	3	0	9
subtype2	98	5	1	6
subtype3	70	1	1	2

Figure S58. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'RACE'

P value = 0.245 (Fisher's exact test), Q value = 0.57

Table S63. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	7	24	273
subtype1	6	11	110
subtype2	1	7	99
subtype3	0	6	64

Figure S59. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.668 (Fisher's exact test), Q value = 0.84

Table S64. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	6	238
subtype1	4	105
subtype2	1	81
subtype3	1	52

Figure S60. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S65. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	105	110	85	27

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0681 (logrank test), Q value = 0.28

Table S66. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	316	134	0.0 - 173.8 (22.5)
subtype1	102	42	0.1 - 154.3 (24.6)
subtype2	106	45	0.1 - 133.2 (19.8)
subtype3	83	33	0.5 - 173.8 (26.4)
subtype4	25	14	0.0 - 75.1 (18.2)

Figure S61. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000222 (Kruskal-Wallis (anova)), Q value = 0.01

Table S67. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	319	67.1 (8.9)
subtype1	103	68.4 (8.7)
subtype2	107	66.5 (8.2)
subtype3	84	64.6 (10.0)
subtype4	25	72.6 (6.0)

Figure S62. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00713 (Fisher's exact test), Q value = 0.071

Table S68. Clustering Approach #5: '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 IV
ALL	2	52	100	3	46	61	2	42	14	3
subtype1	0	27	33	1	12	19	1	8	3	1
subtype2	0	11	29	1	18	26	0	18	5	1
subtype3	1	7	29	1	13	16	0	12	5	0
subtype4	1	7	9	0	3	0	1	4	1	1

Figure S63. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00509 (Fisher's exact test), Q value = 0.057

Table S69. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	70	193	48	16
subtype1	33	56	12	4
subtype2	15	68	22	5
subtype3	12	59	9	5
subtype4	10	10	5	2

Figure S64. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.256 (Fisher's exact test), Q value = 0.57

Table S70. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	204	89	26	4
subtype1	72	27	4	0
subtype2	63	33	11	2
subtype3	49	24	10	2
subtype4	20	5	1	0

Figure S65. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.275 (Fisher's exact test), Q value = 0.59

Table S71. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	278	3
subtype1	89	1
subtype2	98	1
subtype3	74	0
subtype4	17	1

Figure S66. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.00011 (Fisher's exact test), Q value = 0.0099

Table S72. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	247
subtype1	39	66
subtype2	20	90
subtype3	11	74
subtype4	10	17

Figure S67. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.685 (Fisher's exact test), Q value = 0.85

Table S73. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	253	34
subtype1	87	10
subtype2	86	10
subtype3	57	11
subtype4	23	3

Figure S68. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.247 (Kruskal-Wallis (anova)), Q value = 0.57

Table S74. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	108	64.4 (39.8)
subtype1	38	67.9 (40.1)
subtype2	33	56.4 (43.4)
subtype3	31	74.5 (30.4)
subtype4	6	35.0 (47.2)

Figure S69. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0527 (Fisher's exact test), Q value = 0.26

Table S75. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	8	4	315
subtype1	1	0	104
subtype2	1	2	107
subtype3	6	2	77
subtype4	0	0	27

Figure S70. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.589 (Kruskal-Wallis (anova)), Q value = 0.8

Table S76. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	278	50.5 (29.9)
subtype1	89	48.6 (28.5)
subtype2	94	49.7 (28.3)
subtype3	73	53.1 (35.3)
subtype4	22	52.9 (23.6)

Figure S71. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.246 (Kruskal-Wallis (anova)), Q value = 0.57

Table S77. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	210	1960.9 (11.7)
subtype1	70	1960.9 (12.7)
subtype2	71	1960.9 (10.7)
subtype3	54	1962.4 (11.9)
subtype4	15	1955.3 (11.0)

Figure S72. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.63 (Fisher's exact test), Q value = 0.83

Table S78. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	256	7	4	12
subtype1	82	3	1	2
subtype2	82	4	1	6
subtype3	69	0	2	3
subtype4	23	0	0	1

Figure S73. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.843 (Fisher's exact test), Q value = 0.93

Table S79. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	19	241
subtype1	3	7	80
subtype2	2	8	78
subtype3	1	4	61
subtype4	0	0	22

Figure S74. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.0515 (Fisher's exact test), Q value = 0.26

Table S80. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	212
subtype1	0	77
subtype2	4	65
subtype3	0	55
subtype4	0	15

Figure S75. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S81. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	79	67	62	60	31	28

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.697 (logrank test), Q value = 0.85

Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	316	134	0.0 - 173.8 (22.5)
subtype1	76	34	0.4 - 154.3 (27.5)
subtype2	67	30	0.0 - 173.8 (22.1)
subtype3	59	21	0.1 - 126.2 (19.3)
subtype4	57	24	0.3 - 129.0 (24.9)
subtype5	30	14	0.1 - 132.4 (16.6)
subtype6	27	11	0.8 - 92.7 (14.1)

Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.338 (Kruskal-Wallis (anova)), Q value = 0.63

Table S83. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	319	67.1 (8.9)
subtype1	77	65.1 (10.3)
subtype2	67	67.8 (8.1)
subtype3	59	67.3 (7.4)
subtype4	58	67.8 (8.3)
subtype5	30	67.3 (9.7)
subtype6	28	69.0 (9.8)

Figure S77. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.563 (Fisher's exact test), Q value = 0.79

Table S84. Clustering Approach #6: '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 IV
ALL	2	52	100	3	46	61	2	42	14	3
subtype1	0	15	21	2	12	16	1	11	1	0
subtype2	2	12	21	0	12	6	1	7	4	2
subtype3	0	5	17	0	9	15	0	9	5	1
subtype4	0	9	22	0	6	10	0	8	4	0
subtype5	0	5	11	1	5	5	0	4	0	0
subtype6	0	6	8	0	2	9	0	3	0	0

Figure S78. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.803 (Fisher's exact test), Q value = 0.91

Table S85. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	70	193	48	16
subtype1	19	46	10	4
subtype2	19	36	8	4
subtype3	7	38	13	4
subtype4	11	38	8	3
subtype5	7	18	5	1
subtype6	7	17	4	0

Figure S79. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.883 (Fisher's exact test), Q value = 0.95

Table S86. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	204	89	26	4
subtype1	45	28	6	0
subtype2	45	14	5	1
subtype3	37	18	4	2
subtype4	38	14	7	1
subtype5	21	7	2	0
subtype6	18	8	2	0

Figure S80. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.462 (Fisher's exact test), Q value = 0.72

Table S87. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	278	3
subtype1	66	0
subtype2	53	2
subtype3	56	1
subtype4	53	0
subtype5	24	0
subtype6	26	0

Figure S81. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0772 (Fisher's exact test), Q value = 0.28

Table S88. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	247
subtype1	22	57
subtype2	13	54
subtype3	11	51
subtype4	15	45
subtype5	6	25
subtype6	13	15

Figure S82. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.876 (Fisher's exact test), Q value = 0.95

Table S89. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	253	34
subtype1	60	8
subtype2	53	7
subtype3	48	4
subtype4	45	8
subtype5	25	3
subtype6	22	4

Figure S83. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.151 (Kruskal-Wallis (anova)), Q value = 0.44

Table S90. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	108	64.4 (39.8)
subtype1	26	70.8 (37.5)
subtype2	24	72.5 (34.8)
subtype3	17	52.9 (42.1)
subtype4	19	51.1 (40.7)
subtype5	9	64.4 (48.5)
subtype6	13	71.5 (41.8)

Figure S84. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.771 (Fisher's exact test), Q value = 0.89

Table S91. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	8	4	315
subtype1	2	1	76
subtype2	4	0	63
subtype3	1	2	59
subtype4	1	1	58
subtype5	0	0	31
subtype6	0	0	28

Figure S85. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.301 (Kruskal-Wallis (anova)), Q value = 0.59

Table S92. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	278	50.5 (29.9)
subtype1	64	56.0 (40.9)
subtype2	55	45.6 (24.7)
subtype3	54	52.9 (27.8)
subtype4	53	52.6 (23.3)
subtype5	26	42.9 (19.6)
subtype6	26	45.6 (32.1)

Figure S86. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.389 (Kruskal-Wallis (anova)), Q value = 0.66

Table S93. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	210	1960.9 (11.7)
subtype1	56	1963.2 (12.1)
subtype2	35	1959.9 (11.6)
subtype3	38	1961.7 (10.1)
subtype4	39	1957.9 (11.3)
subtype5	20	1959.8 (10.6)
subtype6	22	1961.1 (14.8)

Figure S87. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.175 (Fisher's exact test), Q value = 0.48

Table S94. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	256	7	4	12
subtype1	63	0	0	4
subtype2	53	1	0	1
subtype3	47	2	1	3
subtype4	52	0	2	2
subtype5	20	2	1	1
subtype6	21	2	0	1

Figure S88. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.175 (Fisher's exact test), Q value = 0.48

Table S95. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	19	241
subtype1	2	7	59
subtype2	1	3	54
subtype3	1	6	41
subtype4	1	0	41
subtype5	0	0	26
subtype6	1	3	20

Figure S89. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.177 (Fisher's exact test), Q value = 0.48

Table S96. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	212
subtype1	3	54
subtype2	0	48
subtype3	0	38
subtype4	0	32
subtype5	1	17
subtype6	0	23

Figure S90. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S97. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	113	152	154	80

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0574 (logrank test), Q value = 0.26

Table S98. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	487	210	0.0 - 173.8 (21.0)
subtype1	111	44	0.1 - 122.4 (19.0)
subtype2	148	61	0.2 - 154.3 (30.1)
subtype3	150	66	0.1 - 173.8 (17.6)
subtype4	78	39	0.0 - 133.2 (20.4)

Figure S91. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00966 (Kruskal-Wallis (anova)), Q value = 0.083

Table S99. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	490	67.2 (8.6)
subtype1	111	67.4 (9.1)
subtype2	150	65.5 (8.2)
subtype3	150	68.0 (8.4)
subtype4	79	68.7 (8.6)

Figure S92. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.322 (Fisher's exact test), Q value = 0.62

Table S100. Clustering Approach #7: '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 IV
ALL	3	90	151	3	66	93	3	63	18	7
subtype1	1	20	38	1	10	22	0	16	3	1
subtype2	1	19	55	1	24	29	1	15	7	0
subtype3	1	29	37	1	24	31	2	21	5	3
subtype4	0	22	21	0	8	11	0	11	3	3

Figure S93. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.576 (Fisher's exact test), Q value = 0.79

Table S101. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	114	292	70	23
subtype1	24	68	17	4
subtype2	29	97	17	9
subtype3	36	87	24	7
subtype4	25	40	12	3

Figure S94. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.364 (Fisher's exact test), Q value = 0.64

Table S102. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	318	130	40	5
subtype1	74	26	10	1
subtype2	93	46	11	1
subtype3	98	45	10	1
subtype4	53	13	9	2

Figure S95. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0772 (Fisher's exact test), Q value = 0.28

Table S103. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	407	7
subtype1	90	1
subtype2	128	0
subtype3	123	3
subtype4	66	3

Figure S96. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.491 (Fisher's exact test), Q value = 0.73

Table S104. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	129	370
subtype1	28	85
subtype2	34	118
subtype3	42	112
subtype4	25	55

Figure S97. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.955 (Fisher's exact test), Q value = 0.99

Table S105. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	370	49
subtype1	80	12
subtype2	118	15
subtype3	111	15
subtype4	61	7

Figure S98. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.558 (Kruskal-Wallis (anova)), Q value = 0.79

Table S106. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	162	60.6 (40.8)
subtype1	29	60.3 (42.6)
subtype2	55	65.8 (38.9)
subtype3	47	59.1 (40.5)
subtype4	31	53.9 (43.6)

Figure S99. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.304 (Fisher's exact test), Q value = 0.59

Table S107. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	15	6	1	477
subtype1	2	2	0	109
subtype2	6	1	0	145
subtype3	7	1	1	145
subtype4	0	2	0	78

Figure S100. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.381 (Kruskal-Wallis (anova)), Q value = 0.65

Table S108. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	423	52.8 (31.0)
subtype1	96	49.7 (32.7)
subtype2	129	52.9 (27.4)
subtype3	130	53.1 (31.8)
subtype4	68	56.0 (33.7)

Figure S101. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0758 (Kruskal-Wallis (anova)), Q value = 0.28

Table S109. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	318	1960.6 (11.4)
subtype1	75	1960.0 (12.5)
subtype2	91	1962.7 (11.9)
subtype3	101	1960.7 (10.4)
subtype4	51	1957.7 (10.4)

Figure S102. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.729 (Fisher's exact test), Q value = 0.87

Table S110. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	397	12	4	22
subtype1	86	2	2	3
subtype2	128	6	0	7
subtype3	120	3	1	7
subtype4	63	1	1	5

Figure S103. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.819 (Fisher's exact test), Q value = 0.91

Table S111. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	9	30	348
subtype1	2	7	73
subtype2	1	9	107
subtype3	3	10	111
subtype4	3	4	57

Figure S104. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.142 (Fisher's exact test), Q value = 0.43

Table S112. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	315
subtype1	4	66
subtype2	1	94
subtype3	1	105
subtype4	2	50

Figure S105. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S113. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	179	189	131

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0817 (logrank test), Q value = 0.29

Table S114. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	487	210	0.0 - 173.8 (21.0)
subtype1	175	69	0.0 - 132.4 (19.3)
subtype2	183	76	0.2 - 154.3 (26.8)
subtype3	129	65	0.2 - 173.8 (18.9)

Figure S106. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000733 (Kruskal-Wallis (anova)), Q value = 0.019

Table S115. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	490	67.2 (8.6)
subtype1	176	69.0 (8.1)
subtype2	185	65.6 (8.7)
subtype3	129	67.1 (8.6)

Figure S107. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0705 (Fisher's exact test), Q value = 0.28

Table S116. Clustering Approach #8: '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 IV
ALL	3	90	151	3	66	93	3	63	18	7
subtype1	1	30	59	1	20	31	0	28	4	4
subtype2	1	26	63	2	28	37	1	20	10	0
subtype3	1	34	29	0	18	25	2	15	4	3

Figure S108. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0946 (Fisher's exact test), Q value = 0.32

Table S117. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	114	292	70	23
subtype1	35	105	32	7
subtype2	38	120	21	10
subtype3	41	67	17	6

Figure S109. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.913 (Fisher's exact test), Q value = 0.96

Table S118. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	318	130	40	5
subtype1	118	43	14	1
subtype2	115	54	16	3
subtype3	85	33	10	1

Figure S110. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0734 (Fisher's exact test), Q value = 0.28

Table S119. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	407	7
subtype1	144	4
subtype2	157	0
subtype3	106	3

Figure S111. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0308 (Fisher's exact test), Q value = 0.19

Table S120. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	129	370
subtype1	56	123
subtype2	37	152
subtype3	36	95

Figure S112. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.288 (Fisher's exact test), Q value = 0.59

Table S121. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	370	49
subtype1	134	16
subtype2	139	24
subtype3	97	9

Figure S113. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.327 (Kruskal-Wallis (anova)), Q value = 0.62

Table S122. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	162	60.6 (40.8)
subtype1	44	54.5 (42.4)
subtype2	71	65.8 (38.8)
subtype3	47	58.5 (42.1)

Figure S114. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0403 (Fisher's exact test), Q value = 0.22

Table S123. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	15	6	1	477
subtype1	1	3	0	175
subtype2	7	1	0	181
subtype3	7	2	1	121

Figure S115. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.768 (Kruskal-Wallis (anova)), Q value = 0.89

Table S124. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	423	52.8 (31.0)
subtype1	152	54.1 (33.3)
subtype2	159	52.3 (27.4)
subtype3	112	51.5 (32.9)

Figure S116. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.00108 (Kruskal-Wallis (anova)), Q value = 0.024

Table S125. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	318	1960.6 (11.4)
subtype1	118	1957.6 (10.4)
subtype2	113	1963.3 (12.2)
subtype3	87	1961.2 (11.0)

Figure S117. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.61 (Fisher's exact test), Q value = 0.82

Table S126. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	397	12	4	22
subtype1	142	2	1	9
subtype2	152	7	3	8
subtype3	103	3	0	5

Figure S118. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.648 (Fisher's exact test), Q value = 0.84

Table S127. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	9	30	348
subtype1	3	11	116
subtype2	2	13	132
subtype3	4	6	100

Figure S119. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.666 (Fisher's exact test), Q value = 0.84

Table S128. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	315
subtype1	4	104
subtype2	2	118
subtype3	2	93

Figure S120. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S129. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	121	135	122	61	37

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.258 (logrank test), Q value = 0.57

Table S130. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	465	198	0.0 - 173.8 (21.1)
subtype1	120	57	0.1 - 173.8 (21.5)
subtype2	131	44	0.1 - 150.2 (19.4)
subtype3	117	54	0.0 - 132.4 (21.0)
subtype4	60	25	0.2 - 154.3 (27.4)
subtype5	37	18	0.2 - 104.1 (18.3)

Figure S121. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00136 (Kruskal-Wallis (anova)), Q value = 0.026

Table S131. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	468	67.4 (8.6)
subtype1	120	67.2 (8.2)
subtype2	133	65.7 (9.0)
subtype3	118	68.8 (8.8)
subtype4	60	66.5 (8.2)
subtype5	37	71.4 (6.9)

Figure S122. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00209 (Fisher's exact test), Q value = 0.034

Table S132. Clustering Approach #9: '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 IV
ALL	3	83	144	3	63	92	3	61	16	6
subtype1	0	17	48	0	6	20	0	21	6	3
subtype2	1	15	38	2	28	28	3	17	2	0
subtype3	1	25	31	1	19	25	0	15	3	2
subtype4	0	16	20	0	8	10	0	3	3	0
subtype5	1	10	7	0	2	9	0	5	2	1

Figure S123. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0308 (Fisher's exact test), Q value = 0.19

Table S133. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	106	280	68	22
subtype1	22	80	11	8
subtype2	23	86	23	3
subtype3	30	62	24	6
subtype4	20	34	4	3
subtype5	11	18	6	2

Figure S124. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.413 (Fisher's exact test), Q value = 0.69

Table S134. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	300	126	40	4
subtype1	76	28	13	4
subtype2	78	42	13	0
subtype3	79	31	8	0
subtype4	42	16	3	0
subtype5	25	9	3	0

Figure S125. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.3 (Fisher's exact test), Q value = 0.59

Table S135. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	386	6
subtype1	113	3
subtype2	103	0
subtype3	94	2
subtype4	48	0
subtype5	28	1

Figure S126. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.434 (Fisher's exact test), Q value = 0.7

Table S136. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	123	353
subtype1	31	90
subtype2	30	105
subtype3	33	89
subtype4	15	46
subtype5	14	23

Figure S127. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.562 (Fisher's exact test), Q value = 0.79

Table S137. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	358	44
subtype1	74	11
subtype2	99	14
subtype3	101	12
subtype4	50	6
subtype5	34	1

Figure S128. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000562 (Kruskal-Wallis (anova)), Q value = 0.017

Table S138. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	153	62.2 (40.4)
subtype1	41	38.3 (42.9)
subtype2	44	72.0 (32.5)
subtype3	38	67.6 (38.9)
subtype4	21	76.7 (36.5)
subtype5	9	66.7 (40.0)

Figure S129. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.132 (Fisher's exact test), Q value = 0.42

Table S139. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	13	6	1	456
subtype1	1	1	0	119
subtype2	7	2	1	125
subtype3	1	3	0	118
subtype4	4	0	0	57
subtype5	0	0	0	37

Figure S130. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.986 (Kruskal-Wallis (anova)), Q value = 1

Table S140. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	405	53.4 (31.6)
subtype1	109	56.0 (37.8)
subtype2	112	51.5 (28.2)
subtype3	102	53.3 (29.2)
subtype4	53	51.8 (28.2)
subtype5	29	54.2 (33.9)

Figure S131. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.000199 (Kruskal-Wallis (anova)), Q value = 0.01

Table S141. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	305	1960.5 (11.6)
subtype1	81	1957.9 (9.7)
subtype2	87	1964.5 (10.5)
subtype3	75	1958.1 (11.8)
subtype4	39	1963.6 (13.8)
subtype5	23	1957.5 (12.4)

Figure S132. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.169 (Fisher's exact test), Q value = 0.48

Table S142. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	378	11	3	20
subtype1	109	2	2	2
subtype2	101	3	0	8
subtype3	91	2	0	7
subtype4	48	4	0	2
subtype5	29	0	1	1

Figure S133. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.251 (Fisher's exact test), Q value = 0.57

Table S143. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	9	30	332
subtype1	3	4	74
subtype2	2	5	102
subtype3	4	13	86
subtype4	0	6	43
subtype5	0	2	27

Figure S134. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.73 (Fisher's exact test), Q value = 0.87

Table S144. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	301
subtype1	3	67
subtype2	1	88
subtype3	2	83
subtype4	1	41
subtype5	0	22

Figure S135. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S145. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	88	117	201	70

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.751 (logrank test), Q value = 0.88

Table S146. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	465	198	0.0 - 173.8 (21.1)
subtype1	85	33	0.2 - 154.3 (25.3)
subtype2	114	51	0.1 - 173.8 (18.3)
subtype3	199	83	0.1 - 140.1 (20.8)
subtype4	67	31	0.0 - 126.2 (21.4)

Figure S136. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00748 (Kruskal-Wallis (anova)), Q value = 0.071

Table S147. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	468	67.4 (8.6)
subtype1	86	65.6 (8.3)
subtype2	115	66.2 (9.4)
subtype3	199	68.3 (8.0)
subtype4	68	69.2 (9.1)

Figure S137. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.661 (Fisher's exact test), Q value = 0.84

Table S148. Clustering Approach #10: '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 IV
ALL	3	83	144	3	63	92	3	61	16	6
subtype1	0	14	30	0	12	18	1	7	6	0
subtype2	0	17	41	1	17	24	1	12	2	2
subtype3	3	40	54	2	22	35	1	34	6	2
subtype4	0	12	19	0	12	15	0	8	2	2

Figure S138. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.543 (Fisher's exact test), Q value = 0.79

Table S149. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	106	280	68	22
subtype1	19	55	8	6
subtype2	22	74	18	3
subtype3	49	111	33	8
subtype4	16	40	9	5

Figure S139. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.866 (Fisher's exact test), Q value = 0.94

Table S150. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	300	126	40	4
subtype1	52	29	6	1
subtype2	78	29	9	1
subtype3	130	50	20	1
subtype4	40	18	5	1

Figure S140. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.354 (Fisher's exact test), Q value = 0.64

Table S151. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	386	6
subtype1	75	0
subtype2	100	2
subtype3	157	2
subtype4	54	2

Figure S141. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.748 (Fisher's exact test), Q value = 0.88

Table S152. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	123	353
subtype1	19	69
subtype2	30	87
subtype3	54	147
subtype4	20	50

Figure S142. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.555 (Fisher's exact test), Q value = 0.79

Table S153. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	358	44
subtype1	67	11
subtype2	78	7
subtype3	154	21
subtype4	59	5

Figure S143. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.196 (Kruskal-Wallis (anova)), Q value = 0.5

Table S154. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	153	62.2 (40.4)
subtype1	30	74.7 (35.4)
subtype2	37	54.3 (44.8)
subtype3	61	60.2 (39.1)
subtype4	25	64.0 (41.0)

Figure S144. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.066 (Fisher's exact test), Q value = 0.28

Table S155. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	13	6	1	456
subtype1	4	0	0	84
subtype2	6	3	0	108
subtype3	3	1	1	196
subtype4	0	2	0	68

Figure S145. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.617 (Kruskal-Wallis (anova)), Q value = 0.82

Table S156. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	405	53.4 (31.6)
subtype1	79	56.7 (31.6)
subtype2	94	53.5 (36.1)
subtype3	176	52.1 (28.4)
subtype4	56	52.9 (33.6)

Figure S146. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.631 (Kruskal-Wallis (anova)), Q value = 0.83

Table S157. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	305	1960.5 (11.6)
subtype1	60	1962.8 (13.7)
subtype2	76	1960.0 (11.6)
subtype3	132	1959.8 (10.4)
subtype4	37	1960.8 (12.0)

Figure S147. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.204 (Fisher's exact test), Q value = 0.5

Table S158. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	378	11	3	20
subtype1	72	4	1	3
subtype2	97	2	0	5
subtype3	158	4	1	5
subtype4	51	1	1	7

Figure S148. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0269 (Fisher's exact test), Q value = 0.18

Table S159. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	9	30	332
subtype1	0	7	60
subtype2	3	2	82
subtype3	2	17	141
subtype4	4	4	49

Figure S149. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.856 (Fisher's exact test), Q value = 0.94

Table S160. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	301
subtype1	1	58
subtype2	1	66
subtype3	3	129
subtype4	2	48

Figure S150. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S161. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	89	131	115

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

P value = 0.0575 (logrank test), Q value = 0.26

Table S162. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	327	133	0.0 - 150.2 (20.2)
subtype1	86	36	0.1 - 111.3 (19.4)
subtype2	128	56	0.0 - 111.0 (18.6)
subtype3	113	41	0.1 - 150.2 (22.6)

Figure S151. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00399 (Kruskal-Wallis (anova)), Q value = 0.055

Table S163. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	330	67.5 (8.6)
subtype1	87	69.3 (9.2)
subtype2	128	68.0 (8.0)
subtype3	115	65.7 (8.6)

Figure S152. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0979 (Fisher's exact test), Q value = 0.33

Table S164. Clustering Approach #11: '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 IV
ALL	3	64	90	3	56	65	3	40	6	3
subtype1	1	13	27	1	11	22	0	10	3	1
subtype2	1	34	37	1	21	20	0	13	0	2
subtype3	1	17	26	1	24	23	3	17	3	0

Figure S153. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0337 (Fisher's exact test), Q value = 0.2

Table S165. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	80	188	56	11
subtype1	15	50	20	4
subtype2	43	71	14	3
subtype3	22	67	22	4

Figure S154. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.283 (Fisher's exact test), Q value = 0.59

Table S166. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	215	88	26
subtype1	60	21	6
subtype2	89	30	8
subtype3	66	37	12

Figure S155. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.489 (Fisher's exact test), Q value = 0.73

Table S167. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	252	3
subtype1	67	1
subtype2	97	2
subtype3	88	0

Figure S156. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.351 (Fisher's exact test), Q value = 0.64

Table S168. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	84	251
subtype1	27	62
subtype2	32	99
subtype3	25	90

Figure S157. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.303 (Fisher's exact test), Q value = 0.59

Table S169. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	267	34
subtype1	74	6
subtype2	102	12
subtype3	91	16

Figure S158. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.364 (Kruskal-Wallis (anova)), Q value = 0.64

Table S170. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	112	69.0 (37.6)
subtype1	21	66.2 (38.5)
subtype2	52	75.2 (34.2)
subtype3	39	62.3 (40.9)

Figure S159. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0894 (Fisher's exact test), Q value = 0.31

Table S171. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	10	5	1	319
subtype1	0	2	0	87
subtype2	3	1	1	126
subtype3	7	2	0	106

Figure S160. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.437 (Kruskal-Wallis (anova)), Q value = 0.7

Table S172. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	282	52.0 (28.6)
subtype1	71	54.0 (33.5)
subtype2	113	49.4 (24.3)
subtype3	98	53.5 (29.3)

Figure S161. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.00495 (Kruskal-Wallis (anova)), Q value = 0.057

Table S173. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	215	1961.9 (11.8)
subtype1	52	1957.7 (12.9)
subtype2	87	1962.3 (11.1)
subtype3	76	1964.3 (11.1)

Figure S162. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.797 (Fisher's exact test), Q value = 0.91

Table S174. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	249	9	2	17
subtype1	62	3	0	7
subtype2	97	3	1	5
subtype3	90	3	1	5

Figure S163. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.476 (Fisher's exact test), Q value = 0.73

Table S175. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	23	250
subtype1	2	9	58
subtype2	2	9	109
subtype3	2	5	83

Figure S164. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.379 (Fisher's exact test), Q value = 0.65

Table S176. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	223
subtype1	1	59
subtype2	3	96
subtype3	0	68

Figure S165. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S177. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	39	151	145

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

P value = 0.82 (logrank test), Q value = 0.91

Table S178. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	327	133	0.0 - 150.2 (20.2)
subtype1	39	13	0.1 - 104.1 (17.7)
subtype2	149	62	0.1 - 150.2 (22.6)
subtype3	139	58	0.0 - 132.4 (19.2)

Figure S166. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.038 (Kruskal-Wallis (anova)), Q value = 0.21

Table S179. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	330	67.5 (8.6)
subtype1	39	69.3 (7.3)
subtype2	151	66.4 (8.4)
subtype3	140	68.3 (9.0)

Figure S167. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.366 (Fisher's exact test), Q value = 0.64

Table S180. Clustering Approach #12: '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 IV
ALL	3	64	90	3	56	65	3	40	6	3
subtype1	0	7	9	1	4	7	0	9	2	0
subtype2	2	24	36	1	31	32	2	19	2	1
subtype3	1	33	45	1	21	26	1	12	2	2

Figure S168. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.155 (Fisher's exact test), Q value = 0.44

Table S181. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	80	188	56	11
subtype1	7	18	12	2
subtype2	32	92	23	4
subtype3	41	78	21	5

Figure S169. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.213 (Fisher's exact test), Q value = 0.52

Table S182. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	215	88	26
subtype1	26	8	5
subtype2	90	47	13
subtype3	99	33	8

Figure S170. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.731 (Fisher's exact test), Q value = 0.87

Table S183. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	252	3
subtype1	30	0
subtype2	117	1
subtype3	105	2

Figure S171. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.806 (Fisher's exact test), Q value = 0.91

Table S184. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	84	251
subtype1	9	30
subtype2	36	115
subtype3	39	106

Figure S172. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0491 (Fisher's exact test), Q value = 0.26

Table S185. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	267	34
subtype1	31	5
subtype2	115	21
subtype3	121	8

Figure S173. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0243 (Kruskal-Wallis (anova)), Q value = 0.18

Table S186. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	112	69.0 (37.6)
subtype1	8	51.2 (44.2)
subtype2	57	63.2 (39.7)
subtype3	47	79.1 (31.4)

Figure S174. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0218 (Fisher's exact test), Q value = 0.16

Table S187. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	10	5	1	319
subtype1	2	1	0	36
subtype2	8	2	1	140
subtype3	0	2	0	143

Figure S175. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.283 (Kruskal-Wallis (anova)), Q value = 0.59

Table S188. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	282	52.0 (28.6)
subtype1	32	56.2 (27.0)
subtype2	131	50.9 (25.8)
subtype3	119	52.0 (31.8)

Figure S176. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.00938 (Kruskal-Wallis (anova)), Q value = 0.083

Table S189. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	215	1961.9 (11.8)
subtype1	20	1955.9 (8.6)
subtype2	101	1963.9 (11.0)
subtype3	94	1961.1 (12.7)

Figure S177. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.675 (Fisher's exact test), Q value = 0.84

Table S190. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	249	9	2	17
subtype1	28	1	0	1
subtype2	118	5	2	6
subtype3	103	3	0	10

Figure S178. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0135 (Fisher's exact test), Q value = 0.11

Table S191. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	23	250
subtype1	0	6	21
subtype2	1	6	123
subtype3	5	11	106

Figure S179. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.2 (Fisher's exact test), Q value = 0.5

Table S192. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	223
subtype1	0	22
subtype2	0	98
subtype3	4	103

Figure S180. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LUSC-TP/20140646/LUSC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LUSC-TP/19775388/LUSC-TP.merged_data.txt
Number of patients = 502
Number of clustering approaches = 12
Number of selected clinical features = 15
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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