Correlation between aggregated molecular cancer subtypes and selected clinical features

Lung Squamous Cell Carcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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/C11V5D19

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 495 patients, 34 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 'NEOPLASM_DISEASESTAGE' and 'PATHOLOGY_T_STAGE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'NEOPLASM_DISEASESTAGE' 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' and 'KARNOFSKY_PERFORMANCE_SCORE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'COMPLETENESS_OF_RESECTION'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death' and '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', 'NEOPLASM_DISEASESTAGE', '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', '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, 34 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	0.612 (0.824)	0.784 (0.917)	0.218 (0.517)	0.155 (0.429)	0.0137 (0.119)	0.0224 (0.182)	0.0472 (0.25)	0.119 (0.362)	0.38 (0.652)	0.618 (0.824)	0.0777 (0.303)	0.89 (0.958)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.924 (0.972)	0.645 (0.848)	0.0423 (0.235)	0.0103 (0.119)	0.0128 (0.119)	0.288 (0.599)	0.0119 (0.119)	0.00165 (0.0372)	0.0016 (0.0372)	0.00572 (0.0858)	0.005 (0.0845)	0.0378 (0.227)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.0112 (0.119)	0.0232 (0.182)	0.387 (0.652)	0.749 (0.899)	0.283 (0.598)	0.793 (0.921)	0.191 (0.49)	0.138 (0.406)	0.0002 (0.009)	0.69 (0.869)	0.101 (0.335)	0.244 (0.555)
PATHOLOGY T STAGE	Fisher's exact test	0.00044 (0.0158)	8e-05 (0.0072)	0.493 (0.727)	0.783 (0.917)	0.144 (0.412)	0.503 (0.736)	0.59 (0.811)	0.0883 (0.315)	0.0139 (0.119)	0.426 (0.692)	0.0342 (0.214)	0.0939 (0.325)
PATHOLOGY N STAGE	Fisher's exact test	0.905 (0.958)	0.359 (0.632)	0.0344 (0.214)	0.438 (0.696)	0.31 (0.609)	0.0659 (0.291)	0.36 (0.632)	0.93 (0.973)	0.427 (0.692)	0.892 (0.958)	0.361 (0.632)	0.251 (0.566)
PATHOLOGY M STAGE	Fisher's exact test	0.105 (0.335)	0.0721 (0.302)	0.457 (0.711)	0.46 (0.711)			0.0785 (0.303)	0.0848 (0.312)	0.316 (0.609)	0.356 (0.632)	0.493 (0.727)	0.733 (0.893)
GENDER	Fisher's exact test	0.331 (0.614)	0.0579 (0.29)	0.00227 (0.0454)	0.337 (0.614)	0.264 (0.579)	0.664 (0.857)	0.414 (0.684)	0.0345 (0.214)	0.507 (0.736)	0.738 (0.893)	0.267 (0.579)	0.722 (0.89)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)	0.318 (0.609)	0.0627 (0.291)	0.23 (0.53)	0.0283 (0.204)	0.441 (0.696)	0.948 (0.981)	0.778 (0.917)	0.217 (0.517)	1.68e-05 (0.00303)	0.0624 (0.291)	0.942 (0.98)	0.0548 (0.282)
HISTOLOGICAL TYPE	Fisher's exact test	0.295 (0.603)	0.324 (0.61)	0.0301 (0.209)	0.077 (0.303)	0.0831 (0.312)	0.0662 (0.291)	0.338 (0.614)	0.043 (0.235)	0.148 (0.415)	0.108 (0.335)	0.0677 (0.291)	0.00646 (0.0894)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.552 (0.777)	0.537 (0.761)	0.436 (0.696)	0.463 (0.711)	0.852 (0.941)	0.896 (0.958)	0.466 (0.711)	0.829 (0.933)	0.702 (0.878)	0.325 (0.61)	0.107 (0.335)	0.717 (0.89)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.485 (0.727)	0.999 (1.00)	0.135 (0.405)	0.202 (0.491)	0.0791 (0.303)	0.604 (0.824)	0.315 (0.609)	0.829 (0.933)	0.956 (0.983)	0.582 (0.806)	0.404 (0.674)	0.182 (0.474)
YEAR OF TOBACCO SMOKING ONSET	Kruskal-Wallis (anova)	0.899 (0.958)	0.516 (0.743)	0.165 (0.442)	0.308 (0.609)	0.104 (0.335)	0.885 (0.958)	0.0894 (0.315)	0.000893 (0.0268)	0.000146 (0.00875)	0.625 (0.827)	0.00516 (0.0845)	0.0116 (0.119)
COMPLETENESS OF RESECTION	Fisher's exact test	0.561 (0.782)	0.816 (0.933)	0.173 (0.458)	0.529 (0.755)	0.0407 (0.235)	0.0956 (0.325)	0.74 (0.893)	0.617 (0.824)	0.164 (0.442)	0.221 (0.517)	0.78 (0.917)	0.676 (0.857)
RACE	Fisher's exact test	0.984 (1.00)	0.976 (0.998)	0.29 (0.599)	0.261 (0.579)	0.198 (0.491)	0.836 (0.935)	0.823 (0.933)	0.652 (0.85)	0.3 (0.606)	0.0276 (0.204)	0.47 (0.711)	0.0135 (0.119)
ETHNICITY	Fisher's exact test	0.271 (0.581)	0.068 (0.291)	0.201 (0.491)	0.672 (0.857)	0.385 (0.652)	0.842 (0.935)	0.144 (0.412)	0.67 (0.857)	0.806 (0.93)	0.858 (0.942)	0.379 (0.652)	0.202 (0.491)

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 = 0.612 (logrank test), Q value = 0.82

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	69	0.4 - 174.1 (22.7)
subtype1	41	17	0.4 - 122.4 (21.1)
subtype2	50	22	0.4 - 117.6 (29.9)
subtype3	30	17	0.4 - 174.1 (24.0)
subtype4	27	13	0.4 - 133.7 (15.6)

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 = 0.97

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 'NEOPLASM_DISEASESTAGE'

P value = 0.0112 (Fisher's exact test), Q value = 0.12

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

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	20	61	7	27	19	15	4
subtype1	2	21	0	9	6	2	2
subtype2	2	24	4	9	6	7	0
subtype3	8	11	2	4	4	2	0
subtype4	8	5	1	5	3	4	2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00044 (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 = 0.905 (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 = 0.105 (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 = 0.331 (Fisher's exact test), Q value = 0.61

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 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.318 (Kruskal-Wallis (anova)), Q value = 0.61

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

	nPatients	Mean (Std.Dev)
ALL	27	26.3 (39.2)
subtype1	5	16.0 (35.8)
subtype2	4	0.0 (0.0)
subtype3	9	31.1 (46.8)
subtype4	9	38.9 (39.5)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.295 (Fisher's exact test), Q value = 0.6

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: '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 S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.552 (Fisher's exact test), Q value = 0.78

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

nPatients	NO	YES
ALL	2	152
subtype1	1	41
subtype2	0	52
subtype3	1	31
subtype4	0	28

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

'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 'COMPLETENESS_OF_RESECTION'

P value = 0.561 (Fisher's exact test), Q value = 0.78

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

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: 'COMPLETENESS_OF_RESECTION'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.984 (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	91
subtype1	1	2	24
subtype2	1	3	27
subtype3	1	1	21
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 = 0.271 (Fisher's exact test), Q value = 0.58

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	88
subtype1	3	23
subtype2	1	26
subtype3	0	20
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 = 0.784 (logrank test), Q value = 0.92

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	69	0.4 - 174.1 (22.7)
subtype1	46	22	0.4 - 122.4 (20.8)
subtype2	54	24	0.4 - 117.6 (28.0)
subtype3	48	23	0.4 - 174.1 (21.5)

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 = 0.85

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 'NEOPLASM_DISEASESTAGE'

P value = 0.0232 (Fisher's exact test), Q value = 0.18

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

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	20	61	7	27	19	15	4
subtype1	4	20	2	9	7	2	3
subtype2	2	25	4	10	6	9	0
subtype3	14	16	1	8	6	4	1

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 8e-05 (Fisher's exact test), Q value = 0.0072

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 = 0.359 (Fisher's exact test), Q value = 0.63

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 = 0.0721 (Fisher's exact test), Q value = 0.3

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 = 0.0579 (Fisher's exact test), Q value = 0.29

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 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0627 (Kruskal-Wallis (anova)), Q value = 0.29

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

	nPatients	Mean (Std.Dev)
ALL	27	26.3 (39.2)
subtype1	9	8.9 (26.7)
subtype2	5	10.0 (22.4)
subtype3	13	44.6 (44.6)

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.324 (Fisher's exact test), Q value = 0.61

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: '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 S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.537 (Fisher's exact test), Q value = 0.76

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

nPatients	NO	YES
ALL	2	152
subtype1	1	46
subtype2	0	56
subtype3	1	50

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

'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 = 0.74

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 'COMPLETENESS_OF_RESECTION'

P value = 0.816 (Fisher's exact test), Q value = 0.93

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

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: 'COMPLETENESS_OF_RESECTION'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.976 (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	91
subtype1	1	2	27
subtype2	1	3	30
subtype3	1	2	34

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

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.068 (Fisher's exact test), Q value = 0.29

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	88
subtype1	3	25
subtype2	1	29
subtype3	0	34

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	181	162	150

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

P value = 0.218 (logrank test), Q value = 0.52

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	477	196	0.0 - 174.1 (19.1)
subtype1	175	80	0.1 - 174.1 (16.9)
subtype2	157	61	0.1 - 133.7 (23.0)
subtype3	145	55	0.0 - 122.4 (19.6)

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 = 0.0423 (Kruskal-Wallis (anova)), Q value = 0.23

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

	nPatients	Mean (Std.Dev)
ALL	484	67.3 (8.6)
subtype1	178	67.7 (9.3)
subtype2	159	66.1 (8.1)
subtype3	147	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 'NEOPLASM_DISEASESTAGE'

P value = 0.387 (Fisher's exact test), Q value = 0.65

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	2	87	151	2	63	92	3	62	21	7
subtype1	0	34	63	1	21	36	0	17	6	1
subtype2	0	22	48	1	26	29	2	22	9	3
subtype3	2	31	40	0	16	27	1	23	6	3

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.493 (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	111	290	69	23
subtype1	41	104	30	6
subtype2	31	100	23	8
subtype3	39	86	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 = 0.0344 (Fisher's exact test), Q value = 0.21

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

nPatients	N0	N1	N2	N3
ALL	312	131	39	5
subtype1	128	37	10	2
subtype2	88	56	15	2
subtype3	96	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 = 0.457 (Fisher's exact test), Q value = 0.71

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

nPatients	0	1
ALL	404	7
subtype1	147	1
subtype2	132	3
subtype3	125	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 = 0.00227 (Fisher's exact test), Q value = 0.045

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

nPatients	FEMALE	MALE
ALL	127	366
subtype1	63	118
subtype2	33	129
subtype3	31	119

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 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.23 (Kruskal-Wallis (anova)), Q value = 0.53

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

	nPatients	Mean (Std.Dev)
ALL	104	56.3 (41.1)
subtype1	33	60.6 (40.5)
subtype2	35	58.3 (42.0)
subtype3	36	50.6 (41.4)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0301 (Fisher's exact test), Q value = 0.21

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

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG CLEAR CELL SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	15	1	6	1	470
subtype1	1	0	4	1	175
subtype2	9	0	1	0	152
subtype3	5	1	1	0	143

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

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

nPatients	NO	YES
ALL	13	480
subtype1	5	176
subtype2	6	156
subtype3	2	148

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.135 (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	421	52.8 (31.2)
subtype1	152	50.7 (32.1)
subtype2	139	53.6 (31.2)
subtype3	130	54.6 (30.2)

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 = 0.165 (Kruskal-Wallis (anova)), Q value = 0.44

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

	nPatients	Mean (Std.Dev)
ALL	318	1960.5 (11.6)
subtype1	125	1959.5 (12.0)
subtype2	93	1962.2 (11.4)
subtype3	100	1960.1 (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 'COMPLETENESS_OF_RESECTION'

P value = 0.173 (Fisher's exact test), Q value = 0.46

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

nPatients	R0	R1	R2	RX
ALL	394	12	4	22
subtype1	140	5	2	11
subtype2	130	5	2	9
subtype3	124	2	0	2

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.29 (Fisher's exact test), Q value = 0.6

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	340
subtype1	6	8	126
subtype2	2	11	113
subtype3	1	12	101

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 = 0.201 (Fisher's exact test), Q value = 0.49

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	309
subtype1	6	120
subtype2	1	96
subtype3	1	93

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	152	120	89

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.155 (logrank test), Q value = 0.43

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	349	140	0.0 - 174.1 (18.7)
subtype1	148	64	0.0 - 174.1 (16.5)
subtype2	116	45	0.1 - 154.3 (24.7)
subtype3	85	31	0.1 - 107.0 (19.2)

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 = 0.0103 (Kruskal-Wallis (anova)), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	352	67.7 (8.7)
subtype1	149	68.8 (9.4)
subtype2	118	65.9 (8.3)
subtype3	85	68.1 (7.3)

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

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.749 (Fisher's exact test), Q value = 0.9

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	72	96	2	58	69	3	46	6	4
subtype1	1	31	37	1	22	36	1	17	3	3
subtype2	1	22	39	1	23	18	1	13	1	1
subtype3	1	19	20	0	13	15	1	16	2	0

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.783 (Fisher's exact test), Q value = 0.92

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

nPatients	T1	T2	T3	T4
ALL	89	202	58	12
subtype1	36	81	29	6
subtype2	31	67	19	3
subtype3	22	54	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 = 0.438 (Fisher's exact test), Q value = 0.7

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

nPatients	N0	N1	N2
ALL	230	97	28
subtype1	97	39	11
subtype2	79	34	6
subtype3	54	24	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 = 0.46 (Fisher's exact test), Q value = 0.71

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

nPatients	0	1
ALL	279	4
subtype1	110	3
subtype2	96	1
subtype3	73	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 = 0.337 (Fisher's exact test), Q value = 0.61

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

nPatients	FEMALE	MALE
ALL	93	268
subtype1	45	107
subtype2	29	91
subtype3	19	70

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0283 (Kruskal-Wallis (anova)), Q value = 0.2

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

	nPatients	Mean (Std.Dev)
ALL	81	64.6 (37.9)
subtype1	24	77.9 (28.7)
subtype2	37	60.3 (41.3)
subtype3	20	56.5 (38.4)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.077 (Fisher's exact test), Q value = 0.3

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

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG CLEAR CELL SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	12	1	5	1	342
subtype1	1	1	3	0	147
subtype2	7	0	1	0	112
subtype3	4	0	1	1	83

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

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.463 (Fisher's exact test), Q value = 0.71

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

nPatients	NO	YES
ALL	11	350
subtype1	5	147
subtype2	2	118
subtype3	4	85

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

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.202 (Kruskal-Wallis (anova)), Q value = 0.49

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

	nPatients	Mean (Std.Dev)
ALL	309	52.1 (29.3)
subtype1	129	49.7 (29.5)
subtype2	102	51.8 (26.1)
subtype3	78	56.5 (32.8)

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 = 0.308 (Kruskal-Wallis (anova)), Q value = 0.61

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

	nPatients	Mean (Std.Dev)
ALL	235	1961.2 (11.7)
subtype1	97	1960.8 (12.3)
subtype2	76	1962.8 (12.0)
subtype3	62	1959.9 (10.2)

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

'METHLYATION CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.529 (Fisher's exact test), Q value = 0.76

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

nPatients	R0	R1	R2	RX
ALL	274	9	2	17
subtype1	109	3	0	9
subtype2	97	5	1	6
subtype3	68	1	1	2

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.261 (Fisher's exact test), Q value = 0.58

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	7	24	266
subtype1	6	11	109
subtype2	1	7	96
subtype3	0	6	61

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.672 (Fisher's exact test), Q value = 0.86

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	6	231
subtype1	4	104
subtype2	1	78
subtype3	1	49

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
Number of samples	60	73	62

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0137 (logrank test), Q value = 0.12

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	185	82	0.1 - 174.1 (22.5)
subtype1	57	19	0.4 - 174.1 (27.4)
subtype2	69	33	0.2 - 119.5 (21.5)
subtype3	59	30	0.1 - 119.8 (18.4)

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 = 0.0128 (Kruskal-Wallis (anova)), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	187	67.4 (9.5)
subtype1	59	65.5 (10.5)
subtype2	69	66.6 (8.5)
subtype3	59	70.3 (9.1)

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

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.283 (Fisher's exact test), Q value = 0.6

Table S68. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB
ALL	1	30	69	1	22	34	23	13
subtype1	0	11	16	0	7	11	7	8
subtype2	0	7	28	0	10	15	9	3
subtype3	1	12	25	1	5	8	7	2

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.144 (Fisher's exact test), Q value = 0.41

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

nPatients	T1	T2	T3	T4
ALL	45	119	20	11
subtype1	15	35	4	6
subtype2	11	51	9	2
subtype3	19	33	7	3

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 = 0.31 (Fisher's exact test), Q value = 0.61

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

nPatients	N0	N1	N2	N3
ALL	124	49	16	4
subtype1	34	17	7	2
subtype2	44	21	5	2
subtype3	46	11	4	0

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.264 (Fisher's exact test), Q value = 0.58

Table S71. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	146
subtype1	12	48
subtype2	17	56
subtype3	20	42

Figure S66. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.441 (Kruskal-Wallis (anova)), Q value = 0.7

Table S72. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	35	31.7 (39.7)
subtype1	8	43.8 (35.0)
subtype2	15	32.0 (40.7)
subtype3	12	23.3 (42.3)

Figure S67. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0831 (Fisher's exact test), Q value = 0.31

Table S73. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	3	2	190
subtype1	2	2	56
subtype2	0	0	73
subtype3	1	0	61

Figure S68. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.852 (Fisher's exact test), Q value = 0.94

Table S74. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	10	185
subtype1	4	56
subtype2	3	70
subtype3	3	59

Figure S69. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0791 (Kruskal-Wallis (anova)), Q value = 0.3

Table S75. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	165	51.7 (32.3)
subtype1	51	60.6 (39.7)
subtype2	62	49.3 (30.0)
subtype3	52	45.9 (25.1)

Figure S70. 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 = 0.104 (Kruskal-Wallis (anova)), Q value = 0.34

Table S76. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	131	1958.5 (12.0)
subtype1	39	1961.3 (11.6)
subtype2	50	1958.2 (11.0)
subtype3	42	1956.1 (13.3)

Figure S71. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0407 (Fisher's exact test), Q value = 0.23

Table S77. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	149	5	4	9
subtype1	43	0	2	5
subtype2	55	4	2	4
subtype3	51	1	0	0

Figure S72. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.198 (Fisher's exact test), Q value = 0.49

Table S78. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	3	9	147
subtype1	2	1	46
subtype2	0	6	53
subtype3	1	2	48

Figure S73. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.385 (Fisher's exact test), Q value = 0.65

Table S79. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	120
subtype1	1	37
subtype2	3	45
subtype3	0	38

Figure S74. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S80. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	54	41	52	48

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0224 (logrank test), Q value = 0.18

Table S81. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	185	82	0.1 - 174.1 (22.5)
subtype1	51	20	0.4 - 174.1 (32.9)
subtype2	39	22	0.6 - 92.7 (19.8)
subtype3	49	22	0.1 - 119.5 (14.6)
subtype4	46	18	0.2 - 99.2 (24.0)

Figure S75. 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 = 0.288 (Kruskal-Wallis (anova)), Q value = 0.6

Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	187	67.4 (9.5)
subtype1	52	65.8 (10.9)
subtype2	40	68.2 (9.5)
subtype3	49	66.8 (8.3)
subtype4	46	69.2 (9.0)

Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.793 (Fisher's exact test), Q value = 0.92

Table S83. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB
ALL	1	30	69	1	22	34	23	13
subtype1	0	9	16	0	7	9	8	5
subtype2	0	7	17	1	5	6	3	1
subtype3	0	4	19	0	7	12	6	3
subtype4	1	10	17	0	3	7	6	4

Figure S77. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.503 (Fisher's exact test), Q value = 0.74

Table S84. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	45	119	20	11
subtype1	13	32	4	5
subtype2	9	26	5	1
subtype3	7	36	6	3
subtype4	16	25	5	2

Figure S78. 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 = 0.0659 (Fisher's exact test), Q value = 0.29

Table S85. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	124	49	16	4
subtype1	32	14	8	0
subtype2	31	7	3	0
subtype3	29	19	1	2
subtype4	32	9	4	2

Figure S79. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.664 (Fisher's exact test), Q value = 0.86

Table S86. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	146
subtype1	14	40
subtype2	9	32
subtype3	11	41
subtype4	15	33

Figure S80. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.948 (Kruskal-Wallis (anova)), Q value = 0.98

Table S87. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	35	31.7 (39.7)
subtype1	9	25.6 (35.4)
subtype2	13	30.8 (42.7)
subtype3	9	34.4 (41.0)
subtype4	4	42.5 (49.2)

Figure S81. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0662 (Fisher's exact test), Q value = 0.29

Table S88. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	3	2	190
subtype1	3	1	50
subtype2	0	1	40
subtype3	0	0	52
subtype4	0	0	48

Figure S82. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.896 (Fisher's exact test), Q value = 0.96

Table S89. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	10	185
subtype1	3	51
subtype2	1	40
subtype3	3	49
subtype4	3	45

Figure S83. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.604 (Kruskal-Wallis (anova)), Q value = 0.82

Table S90. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	165	51.7 (32.3)
subtype1	47	61.0 (46.0)
subtype2	34	48.5 (24.3)
subtype3	44	47.2 (23.8)
subtype4	40	48.4 (25.1)

Figure S84. 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 = 0.885 (Kruskal-Wallis (anova)), Q value = 0.96

Table S91. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	131	1958.5 (12.0)
subtype1	37	1958.9 (11.0)
subtype2	25	1956.4 (11.6)
subtype3	36	1958.6 (11.7)
subtype4	33	1959.5 (14.1)

Figure S85. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0956 (Fisher's exact test), Q value = 0.32

Table S92. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	149	5	4	9
subtype1	46	0	0	4
subtype2	29	0	0	3
subtype3	37	4	2	1
subtype4	37	1	2	1

Figure S86. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.836 (Fisher's exact test), Q value = 0.93

Table S93. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	3	9	147
subtype1	2	2	43
subtype2	1	2	32
subtype3	0	2	39
subtype4	0	3	33

Figure S87. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.842 (Fisher's exact test), Q value = 0.94

Table S94. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	120
subtype1	2	34
subtype2	1	26
subtype3	1	30
subtype4	0	30

Figure S88. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S95. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	111	150	151	80

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0472 (logrank test), Q value = 0.25

Table S96. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	476	194	0.0 - 174.1 (19.1)
subtype1	109	38	0.1 - 122.4 (16.8)
subtype2	145	56	0.2 - 154.3 (27.0)
subtype3	145	62	0.1 - 174.1 (16.8)
subtype4	77	38	0.0 - 126.2 (19.0)

Figure S89. 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 = 0.0119 (Kruskal-Wallis (anova)), Q value = 0.12

Table S97. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	483	67.3 (8.5)
subtype1	109	67.3 (9.1)
subtype2	148	65.6 (8.2)
subtype3	147	68.2 (8.3)
subtype4	79	68.7 (8.6)

Figure S90. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.191 (Fisher's exact test), Q value = 0.49

Table S98. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	88	150	2	63	91	3	62	20	7
subtype1	1	21	37	1	8	22	0	16	3	1
subtype2	1	19	55	1	23	29	1	14	7	0
subtype3	1	26	37	0	24	29	2	21	7	3
subtype4	0	22	21	0	8	11	0	11	3	3

Figure S91. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.59 (Fisher's exact test), Q value = 0.81

Table S99. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	113	288	69	22
subtype1	24	66	17	4
subtype2	28	97	17	8
subtype3	36	85	23	7
subtype4	25	40	12	3

Figure S92. 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 = 0.36 (Fisher's exact test), Q value = 0.63

Table S100. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	313	129	39	5
subtype1	72	26	10	1
subtype2	93	45	10	1
subtype3	95	45	10	1
subtype4	53	13	9	2

Figure S93. 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 = 0.0785 (Fisher's exact test), Q value = 0.3

Table S101. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	403	7
subtype1	89	1
subtype2	126	0
subtype3	122	3
subtype4	66	3

Figure S94. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.414 (Fisher's exact test), Q value = 0.68

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

nPatients	FEMALE	MALE
ALL	127	365
subtype1	27	84
subtype2	33	117
subtype3	42	109
subtype4	25	55

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

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.778 (Kruskal-Wallis (anova)), Q value = 0.92

Table S103. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	104	56.3 (41.1)
subtype1	17	59.4 (39.9)
subtype2	34	61.8 (39.7)
subtype3	32	55.0 (41.5)
subtype4	21	47.1 (45.0)

Figure S96. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.338 (Fisher's exact test), Q value = 0.61

Table S104. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG CLEAR CELL SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	15	1	6	1	469
subtype1	2	0	2	0	107
subtype2	6	0	1	0	143
subtype3	7	1	1	1	141
subtype4	0	0	2	0	78

Figure S97. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.466 (Fisher's exact test), Q value = 0.71

Table S105. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	13	479
subtype1	2	109
subtype2	2	148
subtype3	6	145
subtype4	3	77

Figure S98. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.315 (Kruskal-Wallis (anova)), Q value = 0.61

Table S106. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	420	52.6 (31.0)
subtype1	95	49.2 (32.5)
subtype2	128	52.7 (27.4)
subtype3	129	53.3 (31.9)
subtype4	68	56.0 (33.7)

Figure S99. 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 = 0.0894 (Kruskal-Wallis (anova)), Q value = 0.32

Table S107. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	316	1960.6 (11.4)
subtype1	74	1960.2 (12.5)
subtype2	90	1962.6 (12.0)
subtype3	101	1960.7 (10.4)
subtype4	51	1957.7 (10.4)

Figure S100. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.74 (Fisher's exact test), Q value = 0.89

Table S108. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	393	12	4	22
subtype1	84	2	2	3
subtype2	128	6	0	7
subtype3	118	3	1	7
subtype4	63	1	1	5

Figure S101. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.823 (Fisher's exact test), Q value = 0.93

Table S109. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	9	30	339
subtype1	2	7	71
subtype2	1	9	104
subtype3	3	10	107
subtype4	3	4	57

Figure S102. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.144 (Fisher's exact test), Q value = 0.41

Table S110. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	307
subtype1	4	64
subtype2	1	92
subtype3	1	101
subtype4	2	50

Figure S103. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S111. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	178	185	129

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.119 (logrank test), Q value = 0.36

Table S112. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	476	194	0.0 - 174.1 (19.1)
subtype1	173	65	0.0 - 132.4 (18.2)
subtype2	178	69	0.2 - 154.3 (23.1)
subtype3	125	60	0.1 - 174.1 (17.7)

Figure S104. 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 = 0.00165 (Kruskal-Wallis (anova)), Q value = 0.037

Table S113. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	483	67.3 (8.5)
subtype1	175	69.0 (8.1)
subtype2	181	65.7 (8.6)
subtype3	127	67.1 (8.6)

Figure S105. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.138 (Fisher's exact test), Q value = 0.41

Table S114. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	88	150	2	63	91	3	62	20	7
subtype1	1	30	59	1	19	31	0	28	4	4
subtype2	1	27	62	1	26	37	1	19	10	0
subtype3	1	31	29	0	18	23	2	15	6	3

Figure S106. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0883 (Fisher's exact test), Q value = 0.32

Table S115. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	113	288	69	22
subtype1	35	104	32	7
subtype2	37	118	21	9
subtype3	41	66	16	6

Figure S107. 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 = 0.93 (Fisher's exact test), Q value = 0.97

Table S116. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	313	129	39	5
subtype1	117	43	14	1
subtype2	113	53	15	3
subtype3	83	33	10	1

Figure S108. 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 = 0.0848 (Fisher's exact test), Q value = 0.31

Table S117. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	403	7
subtype1	144	4
subtype2	153	0
subtype3	106	3

Figure S109. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0345 (Fisher's exact test), Q value = 0.21

Table S118. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	127	365
subtype1	55	123
subtype2	36	149
subtype3	36	93

Figure S110. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.217 (Kruskal-Wallis (anova)), Q value = 0.52

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

	nPatients	Mean (Std.Dev)
ALL	104	56.3 (41.1)
subtype1	32	48.4 (42.7)
subtype2	42	64.5 (38.4)
subtype3	30	53.3 (42.5)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.043 (Fisher's exact test), Q value = 0.23

Table S120. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG CLEAR CELL SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	15	1	6	1	469
subtype1	1	1	3	0	173
subtype2	7	0	1	0	177
subtype3	7	0	2	1	119

Figure S112. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.829 (Fisher's exact test), Q value = 0.93

Table S121. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	13	479
subtype1	4	174
subtype2	6	179
subtype3	3	126

Figure S113. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.829 (Kruskal-Wallis (anova)), Q value = 0.93

Table S122. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	420	52.6 (31.0)
subtype1	152	54.1 (33.3)
subtype2	157	51.9 (27.2)
subtype3	111	51.7 (33.0)

Figure S114. 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 = 0.000893 (Kruskal-Wallis (anova)), Q value = 0.027

Table S123. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	316	1960.6 (11.4)
subtype1	118	1957.6 (10.4)
subtype2	111	1963.4 (12.1)
subtype3	87	1961.2 (11.0)

Figure S115. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.617 (Fisher's exact test), Q value = 0.82

Table S124. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	393	12	4	22
subtype1	141	2	1	9
subtype2	151	7	3	8
subtype3	101	3	0	5

Figure S116. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.652 (Fisher's exact test), Q value = 0.85

Table S125. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	9	30	339
subtype1	3	11	115
subtype2	2	13	127
subtype3	4	6	97

Figure S117. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.67 (Fisher's exact test), Q value = 0.86

Table S126. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	307
subtype1	4	103
subtype2	2	114
subtype3	2	90

Figure S118. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S127. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	121	130	122	61	35

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.38 (logrank test), Q value = 0.65

Table S128. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	454	182	0.0 - 174.1 (19.3)
subtype1	116	55	0.1 - 174.1 (21.1)
subtype2	126	40	0.1 - 118.4 (17.8)
subtype3	117	49	0.0 - 132.4 (18.2)
subtype4	60	23	0.2 - 154.3 (24.7)
subtype5	35	15	0.1 - 104.1 (18.3)

Figure S119. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0016 (Kruskal-Wallis (anova)), Q value = 0.037

Table S129. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	461	67.5 (8.6)
subtype1	120	67.2 (8.2)
subtype2	128	65.9 (9.0)
subtype3	118	68.8 (8.8)
subtype4	60	66.5 (8.2)
subtype5	35	71.6 (6.5)

Figure S120. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 2e-04 (Fisher's exact test), Q value = 0.009

Table S130. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	83	142	2	60	90	3	60	18	6
subtype1	0	16	48	0	6	19	0	21	8	3
subtype2	1	15	37	1	27	27	3	17	1	0
subtype3	1	25	31	1	19	25	0	14	4	2
subtype4	0	17	20	0	7	10	0	3	3	0
subtype5	1	10	6	0	1	9	0	5	2	1

Figure S121. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0139 (Fisher's exact test), Q value = 0.12

Table S131. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	105	276	67	21
subtype1	22	80	11	8
subtype2	22	84	22	2
subtype3	30	62	24	6
subtype4	20	34	4	3
subtype5	11	16	6	2

Figure S122. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.427 (Fisher's exact test), Q value = 0.69

Table S132. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	295	125	39	4
subtype1	76	28	13	4
subtype2	75	41	12	0
subtype3	79	31	8	0
subtype4	42	16	3	0
subtype5	23	9	3	0

Figure S123. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.316 (Fisher's exact test), Q value = 0.61

Table S133. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	382	6
subtype1	113	3
subtype2	99	0
subtype3	94	2
subtype4	48	0
subtype5	28	1

Figure S124. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.507 (Fisher's exact test), Q value = 0.74

Table S134. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	121	348
subtype1	31	90
subtype2	29	101
subtype3	33	89
subtype4	15	46
subtype5	13	22

Figure S125. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 1.68e-05 (Kruskal-Wallis (anova)), Q value = 0.003

Table S135. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	97	57.1 (41.0)
subtype1	26	18.1 (34.4)
subtype2	31	77.7 (23.8)
subtype3	21	66.7 (36.9)
subtype4	12	69.2 (42.3)
subtype5	7	61.4 (43.0)

Figure S126. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.148 (Fisher's exact test), Q value = 0.42

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

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG CLEAR CELL SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	13	1	6	1	448
subtype1	1	0	1	0	119
subtype2	7	0	2	1	120
subtype3	1	1	3	0	117
subtype4	4	0	0	0	57
subtype5	0	0	0	0	35

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

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.702 (Fisher's exact test), Q value = 0.88

Table S137. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	12	457
subtype1	4	117
subtype2	3	127
subtype3	4	118
subtype4	0	61
subtype5	1	34

Figure S128. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.956 (Kruskal-Wallis (anova)), Q value = 0.98

Table S138. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	402	53.3 (31.6)
subtype1	109	56.0 (37.8)
subtype2	110	50.8 (28.0)
subtype3	102	53.3 (29.2)
subtype4	53	51.8 (28.2)
subtype5	28	55.0 (34.2)

Figure S129. 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 = 0.000146 (Kruskal-Wallis (anova)), Q value = 0.0087

Table S139. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	303	1960.6 (11.6)
subtype1	81	1957.9 (9.7)
subtype2	85	1964.7 (10.5)
subtype3	75	1958.1 (11.8)
subtype4	39	1963.6 (13.8)
subtype5	23	1957.5 (12.4)

Figure S130. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.164 (Fisher's exact test), Q value = 0.44

Table S140. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	374	11	3	20
subtype1	109	2	2	2
subtype2	99	3	0	8
subtype3	91	2	0	7
subtype4	48	4	0	2
subtype5	27	0	1	1

Figure S131. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.3 (Fisher's exact test), Q value = 0.61

Table S141. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	9	30	324
subtype1	3	4	73
subtype2	2	5	97
subtype3	4	13	86
subtype4	0	6	43
subtype5	0	2	25

Figure S132. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.806 (Fisher's exact test), Q value = 0.93

Table S142. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	294
subtype1	3	67
subtype2	1	83
subtype3	2	83
subtype4	1	41
subtype5	0	20

Figure S133. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S143. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	88	115	196	70

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.618 (logrank test), Q value = 0.82

Table S144. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	454	182	0.0 - 174.1 (19.3)
subtype1	85	31	0.2 - 154.3 (22.3)
subtype2	109	46	0.1 - 174.1 (18.7)
subtype3	193	77	0.1 - 133.7 (18.8)
subtype4	67	28	0.0 - 126.2 (17.5)

Figure S134. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00572 (Kruskal-Wallis (anova)), Q value = 0.086

Table S145. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	461	67.5 (8.6)
subtype1	86	65.6 (8.3)
subtype2	113	66.2 (9.4)
subtype3	194	68.4 (7.8)
subtype4	68	69.2 (9.1)

Figure S135. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.69 (Fisher's exact test), Q value = 0.87

Table S146. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	83	142	2	60	90	3	60	18	6
subtype1	0	15	30	0	11	18	1	7	6	0
subtype2	0	17	41	1	16	22	1	13	2	2
subtype3	3	39	52	1	21	35	1	33	7	2
subtype4	0	12	19	0	12	15	0	7	3	2

Figure S136. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.426 (Fisher's exact test), Q value = 0.69

Table S147. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	105	276	67	21
subtype1	19	55	8	6
subtype2	21	74	17	3
subtype3	49	107	33	7
subtype4	16	40	9	5

Figure S137. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.892 (Fisher's exact test), Q value = 0.96

Table S148. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	295	125	39	4
subtype1	52	29	6	1
subtype2	77	28	9	1
subtype3	126	50	19	1
subtype4	40	18	5	1

Figure S138. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.356 (Fisher's exact test), Q value = 0.63

Table S149. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	382	6
subtype1	75	0
subtype2	99	2
subtype3	154	2
subtype4	54	2

Figure S139. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.738 (Fisher's exact test), Q value = 0.89

Table S150. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	121	348
subtype1	19	69
subtype2	29	86
subtype3	53	143
subtype4	20	50

Figure S140. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0624 (Kruskal-Wallis (anova)), Q value = 0.29

Table S151. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	97	57.1 (41.0)
subtype1	18	70.0 (39.1)
subtype2	22	41.8 (44.4)
subtype3	41	55.6 (39.4)
subtype4	16	67.5 (38.2)

Figure S141. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.108 (Fisher's exact test), Q value = 0.34

Table S152. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG CLEAR CELL SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	13	1	6	1	448
subtype1	4	0	0	0	84
subtype2	6	0	3	0	106
subtype3	3	1	1	1	190
subtype4	0	0	2	0	68

Figure S142. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.325 (Fisher's exact test), Q value = 0.61

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

nPatients	NO	YES
ALL	12	457
subtype1	1	87
subtype2	1	114
subtype3	8	188
subtype4	2	68

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.582 (Kruskal-Wallis (anova)), Q value = 0.81

Table S154. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	402	53.3 (31.6)
subtype1	79	56.7 (31.6)
subtype2	93	53.2 (36.2)
subtype3	174	51.9 (28.3)
subtype4	56	52.9 (33.6)

Figure S144. 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 = 0.625 (Kruskal-Wallis (anova)), Q value = 0.83

Table S155. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	303	1960.6 (11.6)
subtype1	60	1962.8 (13.7)
subtype2	75	1959.9 (11.6)
subtype3	131	1959.9 (10.4)
subtype4	37	1960.8 (12.0)

Figure S145. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.221 (Fisher's exact test), Q value = 0.52

Table S156. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	374	11	3	20
subtype1	72	4	1	3
subtype2	96	2	0	5
subtype3	155	4	1	5
subtype4	51	1	1	7

Figure S146. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0276 (Fisher's exact test), Q value = 0.2

Table S157. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	9	30	324
subtype1	0	7	60
subtype2	3	2	80
subtype3	2	17	136
subtype4	4	4	48

Figure S147. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.858 (Fisher's exact test), Q value = 0.94

Table S158. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	294
subtype1	1	58
subtype2	1	64
subtype3	3	124
subtype4	2	48

Figure S148. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S159. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	87	130	111

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

P value = 0.0777 (logrank test), Q value = 0.3

Table S160. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	320	120	0.0 - 132.4 (18.3)
subtype1	84	31	0.1 - 107.0 (17.6)
subtype2	127	51	0.0 - 111.0 (16.8)
subtype3	109	38	0.1 - 132.4 (20.2)

Figure S149. 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 = 0.005 (Kruskal-Wallis (anova)), Q value = 0.084

Table S161. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	323	67.6 (8.6)
subtype1	85	69.4 (9.2)
subtype2	127	68.0 (8.0)
subtype3	111	65.8 (8.4)

Figure S150. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.101 (Fisher's exact test), Q value = 0.34

Table S162. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	65	88	2	53	64	3	39	6	3
subtype1	1	13	26	1	11	21	0	9	4	1
subtype2	1	34	36	1	21	20	0	13	0	2
subtype3	1	18	26	0	21	23	3	17	2	0

Figure S151. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0342 (Fisher's exact test), Q value = 0.21

Table S163. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	79	184	55	10
subtype1	15	49	19	4
subtype2	43	70	14	3
subtype3	21	65	22	3

Figure S152. 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 = 0.361 (Fisher's exact test), Q value = 0.63

Table S164. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	210	87	25
subtype1	58	21	6
subtype2	88	30	8
subtype3	64	36	11

Figure S153. 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 = 0.493 (Fisher's exact test), Q value = 0.73

Table S165. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	248	3
subtype1	67	1
subtype2	96	2
subtype3	85	0

Figure S154. 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 = 0.267 (Fisher's exact test), Q value = 0.58

Table S166. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	82	246
subtype1	27	60
subtype2	32	98
subtype3	23	88

Figure S155. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.942 (Kruskal-Wallis (anova)), Q value = 0.98

Table S167. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	74	68.5 (35.5)
subtype1	14	68.6 (34.8)
subtype2	28	72.1 (31.4)
subtype3	32	65.3 (39.8)

Figure S156. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0677 (Fisher's exact test), Q value = 0.29

Table S168. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG CLEAR CELL SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	10	1	5	1	311
subtype1	0	1	2	0	84
subtype2	3	0	1	1	125
subtype3	7	0	2	0	102

Figure S157. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.107 (Fisher's exact test), Q value = 0.34

Table S169. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	9	319
subtype1	0	87
subtype2	6	124
subtype3	3	108

Figure S158. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.404 (Kruskal-Wallis (anova)), Q value = 0.67

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

	nPatients	Mean (Std.Dev)
ALL	279	51.8 (28.5)
subtype1	70	54.4 (33.6)
subtype2	112	48.9 (24.0)
subtype3	97	53.2 (29.3)

Figure S159. 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 = 0.00516 (Kruskal-Wallis (anova)), Q value = 0.084

Table S171. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	213	1962.0 (11.8)
subtype1	52	1957.7 (12.9)
subtype2	86	1962.5 (11.1)
subtype3	75	1964.2 (11.1)

Figure S160. 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 'COMPLETENESS_OF_RESECTION'

P value = 0.78 (Fisher's exact test), Q value = 0.92

Table S172. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	245	9	2	17
subtype1	60	3	0	7
subtype2	96	3	1	5
subtype3	89	3	1	5

Figure S161. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.47 (Fisher's exact test), Q value = 0.71

Table S173. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	23	243
subtype1	2	9	56
subtype2	2	9	108
subtype3	2	5	79

Figure S162. 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 S174. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	216
subtype1	1	57
subtype2	3	95
subtype3	0	64

Figure S163. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S175. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	38	147	143

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

P value = 0.89 (logrank test), Q value = 0.96

Table S176. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	320	120	0.0 - 132.4 (18.3)
subtype1	38	13	0.1 - 104.1 (17.3)
subtype2	145	55	0.1 - 117.6 (19.1)
subtype3	137	52	0.0 - 132.4 (18.1)

Figure S164. 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 = 0.0378 (Kruskal-Wallis (anova)), Q value = 0.23

Table S177. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	323	67.6 (8.6)
subtype1	38	69.6 (7.0)
subtype2	147	66.5 (8.3)
subtype3	138	68.3 (9.1)

Figure S165. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.244 (Fisher's exact test), Q value = 0.56

Table S178. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	65	88	2	53	64	3	39	6	3
subtype1	0	7	8	1	4	7	0	9	2	0
subtype2	2	25	36	0	28	32	2	19	1	1
subtype3	1	33	44	1	21	25	1	11	3	2

Figure S166. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0939 (Fisher's exact test), Q value = 0.32

Table S179. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	79	184	55	10
subtype1	7	17	12	2
subtype2	31	90	23	3
subtype3	41	77	20	5

Figure S167. 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 = 0.251 (Fisher's exact test), Q value = 0.57

Table S180. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	210	87	25
subtype1	25	8	5
subtype2	88	46	12
subtype3	97	33	8

Figure S168. 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 = 0.733 (Fisher's exact test), Q value = 0.89

Table S181. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	248	3
subtype1	30	0
subtype2	114	1
subtype3	104	2

Figure S169. 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 = 0.722 (Fisher's exact test), Q value = 0.89

Table S182. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	82	246
subtype1	9	29
subtype2	34	113
subtype3	39	104

Figure S170. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0548 (Kruskal-Wallis (anova)), Q value = 0.28

Table S183. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	74	68.5 (35.5)
subtype1	5	44.0 (40.4)
subtype2	45	66.7 (37.5)
subtype3	24	77.1 (28.5)

Figure S171. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00646 (Fisher's exact test), Q value = 0.089

Table S184. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG CLEAR CELL SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	10	1	5	1	311
subtype1	2	1	1	0	34
subtype2	8	0	2	1	136
subtype3	0	0	2	0	141

Figure S172. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.717 (Fisher's exact test), Q value = 0.89

Table S185. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	9	319
subtype1	0	38
subtype2	5	142
subtype3	4	139

Figure S173. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.182 (Kruskal-Wallis (anova)), Q value = 0.47

Table S186. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	279	51.8 (28.5)
subtype1	31	57.1 (27.1)
subtype2	130	50.6 (25.7)
subtype3	118	51.6 (31.7)

Figure S174. 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 = 0.0116 (Kruskal-Wallis (anova)), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	213	1962.0 (11.8)
subtype1	20	1955.9 (8.6)
subtype2	100	1963.8 (11.1)
subtype3	93	1961.3 (12.7)

Figure S175. 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 'COMPLETENESS_OF_RESECTION'

P value = 0.676 (Fisher's exact test), Q value = 0.86

Table S188. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	245	9	2	17
subtype1	27	1	0	1
subtype2	117	5	2	6
subtype3	101	3	0	10

Figure S176. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0135 (Fisher's exact test), Q value = 0.12

Table S189. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	23	243
subtype1	0	6	20
subtype2	1	6	119
subtype3	5	11	104

Figure S177. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.202 (Fisher's exact test), Q value = 0.49

Table S190. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	216
subtype1	0	21
subtype2	0	94
subtype3	4	101

Figure S178. 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/15114194/LUSC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LUSC-TP/15084586/LUSC-TP.merged_data.txt
Number of patients = 495
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)

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