Correlation between molecular cancer subtypes and selected clinical features

Lung Squamous Cell Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Lung Squamous Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1HT2M8S

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 14 clinical features across 327 patients, 2 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 do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.T'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGICSPREAD(M)'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 2 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
Time to Death	logrank test	0.256 (1.00)	0.0982 (1.00)	0.477 (1.00)	0.259 (1.00)	0.00318 (0.436)	0.00426 (0.579)	0.342 (1.00)	0.706 (1.00)	0.87 (1.00)	0.584 (1.00)
AGE	ANOVA	0.546 (1.00)	0.168 (1.00)	0.239 (1.00)	0.153 (1.00)	0.0181 (1.00)	0.0866 (1.00)	0.0871 (1.00)	0.0601 (1.00)	0.388 (1.00)	0.207 (1.00)
GENDER	Fisher's exact test	0.382 (1.00)	0.00872 (1.00)	0.0092 (1.00)	0.555 (1.00)	0.29 (1.00)	0.865 (1.00)	0.304 (1.00)	0.0531 (1.00)	0.385 (1.00)	0.924 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA	0.191 (1.00)	0.0891 (1.00)	0.444 (1.00)	0.234 (1.00)	0.527 (1.00)	0.918 (1.00)	0.756 (1.00)	0.84 (1.00)	0.0204 (1.00)	0.773 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.355 (1.00)	0.587 (1.00)	0.286 (1.00)	0.321 (1.00)	0.306 (1.00)	0.195 (1.00)	0.385 (1.00)	0.0359 (1.00)	0.586 (1.00)	0.837 (1.00)
PATHOLOGY T	Chi-square test	0.0029 (0.401)	0.000811 (0.113)	0.491 (1.00)	0.814 (1.00)	0.177 (1.00)	0.262 (1.00)	0.383 (1.00)	0.696 (1.00)	0.437 (1.00)	0.0792 (1.00)
PATHOLOGY N	Chi-square test	0.818 (1.00)	0.32 (1.00)	0.529 (1.00)	0.525 (1.00)	0.373 (1.00)	0.132 (1.00)	0.222 (1.00)	0.619 (1.00)	0.215 (1.00)	0.735 (1.00)
PATHOLOGICSPREAD(M)	Chi-square test	0.146 (1.00)	0.323 (1.00)	0.609 (1.00)	0.471 (1.00)	0.611 (1.00)	0.298 (1.00)	0.377 (1.00)	0.332 (1.00)	4.76e-07 (6.66e-05)	0.253 (1.00)
TUMOR STAGE	Chi-square test	0.798 (1.00)	0.617 (1.00)	0.841 (1.00)	0.808 (1.00)	0.216 (1.00)	0.217 (1.00)	0.524 (1.00)	0.586 (1.00)	0.0316 (1.00)	0.583 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.445 (1.00)	0.453 (1.00)	1 (1.00)	0.285 (1.00)	0.736 (1.00)	0.731 (1.00)	0.406 (1.00)	0.524 (1.00)	0.835 (1.00)	0.107 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.703 (1.00)	0.74 (1.00)	0.926 (1.00)	0.905 (1.00)	0.0565 (1.00)	0.116 (1.00)	0.731 (1.00)	0.627 (1.00)	0.087 (1.00)	0.772 (1.00)
TOBACCOSMOKINGHISTORYINDICATOR	Chi-square test	0.217 (1.00)	0.0089 (1.00)	0.161 (1.00)	0.534 (1.00)	0.079 (1.00)	0.21 (1.00)	0.314 (1.00)	0.172 (1.00)	0.391 (1.00)	0.123 (1.00)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.83 (1.00)	0.121 (1.00)	0.334 (1.00)	0.403 (1.00)	0.168 (1.00)	0.244 (1.00)	0.191 (1.00)	0.109 (1.00)	0.443 (1.00)	0.776 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.482 (1.00)	0.752 (1.00)	0.207 (1.00)	0.372 (1.00)	0.037 (1.00)	0.0263 (1.00)	0.575 (1.00)	0.348 (1.00)	0.0417 (1.00)	0.231 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	43	50	30	31

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.256 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	62	0.4 - 173.8 (18.2)
subtype1	42	16	0.4 - 122.4 (19.0)
subtype2	48	19	0.4 - 99.2 (24.5)
subtype3	28	16	0.4 - 82.2 (15.9)
subtype4	30	11	0.4 - 173.8 (11.8)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.546 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	152	66.5 (8.6)
subtype1	42	66.3 (7.6)
subtype2	49	66.6 (8.4)
subtype3	30	68.2 (8.6)
subtype4	31	65.0 (10.1)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.382 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	44	110
subtype1	9	34
subtype2	13	37
subtype3	11	19
subtype4	11	20

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

'mRNA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.191 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	26	24.2 (38.5)
subtype1	4	0.0 (0.0)
subtype2	4	0.0 (0.0)
subtype3	9	31.1 (46.8)
subtype4	9	38.9 (39.5)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.355 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: '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	43
subtype2	3	0	47
subtype3	1	0	29
subtype4	1	1	29

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0029 (Chi-square test), Q value = 0.4

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	30	100	12	12
subtype1	5	31	6	1
subtype2	4	39	1	6
subtype3	11	16	2	1
subtype4	10	14	3	4

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.818 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	96	40	13	5
subtype1	26	11	4	2
subtype2	27	17	4	2
subtype3	20	6	3	1
subtype4	23	6	2	0

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

'mRNA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.146 (Fisher's exact test), Q value = 1

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	146	4
subtype1	39	2
subtype2	48	0
subtype3	30	0
subtype4	29	2

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.798 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	81	34	34	4
subtype1	23	9	9	2
subtype2	25	13	12	0
subtype3	17	6	6	0
subtype4	16	6	7	2

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

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

P value = 0.445 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	2	152
subtype1	1	42
subtype2	0	50
subtype3	1	29
subtype4	0	31

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

'mRNA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.703 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	133	54.8 (36.5)
subtype1	39	59.1 (41.5)
subtype2	45	51.5 (24.0)
subtype3	25	50.9 (36.6)
subtype4	24	58.4 (47.1)

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

'mRNA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.217 (Chi-square test), Q value = 1

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	81	39	26	6
subtype1	25	10	4	2
subtype2	27	10	13	0
subtype3	16	10	3	1
subtype4	13	9	6	3

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

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.83 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	97	1958.0 (10.6)
subtype1	29	1957.1 (8.6)
subtype2	24	1958.6 (11.2)
subtype3	22	1957.1 (10.8)
subtype4	22	1959.5 (12.4)

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

'mRNA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.482 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	139	3	2	5
subtype1	39	0	1	1
subtype2	45	1	1	1
subtype3	25	1	0	3
subtype4	30	1	0	0

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S16. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	28	54	72

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0982 (logrank test), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	62	0.4 - 173.8 (18.2)
subtype1	27	7	0.4 - 173.8 (15.6)
subtype2	52	21	0.4 - 99.2 (23.6)
subtype3	69	34	0.4 - 122.4 (18.8)

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.168 (ANOVA), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	152	66.5 (8.6)
subtype1	28	63.9 (8.4)
subtype2	53	66.5 (7.9)
subtype3	71	67.5 (9.0)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00872 (Fisher's exact test), Q value = 1

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	44	110
subtype1	15	13
subtype2	12	42
subtype3	17	55

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0891 (ANOVA), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	26	24.2 (38.5)
subtype1	8	48.8 (42.6)
subtype2	5	10.0 (22.4)
subtype3	13	14.6 (35.7)

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.587 (Chi-square test), Q value = 1

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: '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	1	0	27
subtype2	3	0	51
subtype3	1	1	70

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.000811 (Chi-square test), Q value = 0.11

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	30	100	12	12
subtype1	12	11	3	2
subtype2	4	42	1	7
subtype3	14	47	8	3

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.32 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	96	40	13	5
subtype1	22	3	3	0
subtype2	29	18	5	2
subtype3	45	19	5	3

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'mRNA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.323 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	146	4
subtype1	27	1
subtype2	52	0
subtype3	67	3

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.617 (Chi-square test), Q value = 1

Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	81	34	34	4
subtype1	17	4	6	1
subtype2	26	14	14	0
subtype3	38	16	14	3

Figure S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.453 (Fisher's exact test), Q value = 1

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	152
subtype1	1	27
subtype2	0	54
subtype3	1	71

Figure S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.74 (ANOVA), Q value = 1

Table S27. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	133	54.8 (36.5)
subtype1	22	57.5 (45.1)
subtype2	49	51.6 (25.0)
subtype3	62	56.4 (41.0)

Figure S25. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'mRNA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0089 (Chi-square test), Q value = 1

Table S28. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	81	39	26	6
subtype1	17	4	4	3
subtype2	26	12	16	0
subtype3	38	23	6	3

Figure S26. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.121 (ANOVA), Q value = 1

Table S29. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	97	1958.0 (10.6)
subtype1	19	1961.4 (11.6)
subtype2	28	1959.3 (10.8)
subtype3	50	1956.0 (9.8)

Figure S27. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

'mRNA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.752 (Chi-square test), Q value = 1

Table S30. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	139	3	2	5
subtype1	26	1	0	0
subtype2	49	1	1	1
subtype3	64	1	1	4

Figure S28. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

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

Table S31. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	113	108	104

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

P value = 0.477 (logrank test), Q value = 1

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	308	120	0.0 - 173.8 (13.1)
subtype1	107	50	0.1 - 173.8 (14.1)
subtype2	104	37	0.2 - 114.0 (16.2)
subtype3	97	33	0.0 - 122.4 (11.7)

Figure S29. 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 'AGE'

P value = 0.239 (ANOVA), Q value = 1

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	317	67.5 (8.6)
subtype1	111	67.3 (9.8)
subtype2	105	66.6 (7.7)
subtype3	101	68.6 (8.1)

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0092 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	83	242
subtype1	39	74
subtype2	18	90
subtype3	26	78

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.444 (ANOVA), Q value = 1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	54	28.5 (39.7)
subtype1	19	37.4 (44.2)
subtype2	14	20.0 (35.7)
subtype3	21	26.2 (38.1)

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.286 (Chi-square test), Q value = 1

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	7	1	1	1	315
subtype1	0	0	1	1	111
subtype2	4	1	0	0	103
subtype3	3	0	0	0	101

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.491 (Chi-square test), Q value = 1

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	78	196	33	18
subtype1	27	68	14	4
subtype2	22	66	13	7
subtype3	29	62	6	7

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

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

P value = 0.529 (Chi-square test), Q value = 1

Table S38. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	201	91	26	5
subtype1	76	24	10	2
subtype2	60	37	8	2
subtype3	65	30	8	1

Figure S35. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.609 (Chi-square test), Q value = 1

Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	285	4	30
subtype1	97	1	14
subtype2	96	2	7
subtype3	92	1	9

Figure S36. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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

P value = 0.841 (Chi-square test), Q value = 1

Table S40. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	165	90	62	4
subtype1	61	32	17	1
subtype2	51	31	24	2
subtype3	53	27	21	1

Figure S37. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

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

Table S41. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	11	314
subtype1	4	109
subtype2	4	104
subtype3	3	101

Figure S38. 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 'NUMBERPACKYEARSSMOKED'

P value = 0.926 (ANOVA), Q value = 1

Table S42. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	274	52.5 (32.6)
subtype1	92	52.1 (33.7)
subtype2	94	51.8 (32.2)
subtype3	88	53.6 (32.3)

Figure S39. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.161 (Chi-square test), Q value = 1

Table S43. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	158	74	72	14
subtype1	53	30	18	7
subtype2	47	24	32	4
subtype3	58	20	22	3

Figure S40. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.334 (ANOVA), Q value = 1

Table S44. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	217	1958.7 (11.6)
subtype1	80	1957.2 (12.1)
subtype2	61	1960.0 (11.2)
subtype3	76	1959.2 (11.5)

Figure S41. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.207 (Chi-square test), Q value = 1

Table S45. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	253	6	4	16
subtype1	84	1	2	10
subtype2	88	3	1	5
subtype3	81	2	1	1

Figure S42. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S46. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	71	69	53

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.259 (logrank test), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	180	69	0.0 - 173.8 (11.4)
subtype1	66	29	0.1 - 173.8 (9.3)
subtype2	65	24	0.2 - 141.3 (17.8)
subtype3	49	16	0.0 - 107.0 (11.5)

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.153 (ANOVA), Q value = 1

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	185	68.3 (8.8)
subtype1	68	69.9 (9.4)
subtype2	67	67.0 (7.7)
subtype3	50	67.9 (9.1)

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.555 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	144
subtype1	21	50
subtype2	15	54
subtype3	13	40

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.234 (ANOVA), Q value = 1

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	31	29.4 (40.2)
subtype1	5	42.0 (49.2)
subtype2	14	15.7 (33.4)
subtype3	12	40.0 (42.0)

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.321 (Chi-square test), Q value = 1

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARCINOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	4	1	1	187
subtype1	0	0	0	71
subtype2	2	1	0	66
subtype3	2	0	1	50

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.814 (Chi-square test), Q value = 1

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	56	108	22	7
subtype1	19	42	8	2
subtype2	22	35	10	2
subtype3	15	31	4	3

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.525 (Chi-square test), Q value = 1

Table S53. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	119	57	15
subtype1	43	19	8
subtype2	46	20	3
subtype3	30	18	4

Figure S49. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.471 (Chi-square test), Q value = 1

Table S54. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	160	1	30
subtype1	56	1	14
subtype2	61	0	8
subtype3	43	0	8

Figure S50. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.808 (Chi-square test), Q value = 1

Table S55. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	96	62	31	1
subtype1	35	23	11	1
subtype2	38	21	10	0
subtype3	23	18	10	0

Figure S51. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.285 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	184
subtype1	4	67
subtype2	1	68
subtype3	4	49

Figure S52. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.905 (ANOVA), Q value = 1

Table S57. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	162	50.9 (30.2)
subtype1	58	49.7 (30.1)
subtype2	57	50.8 (25.2)
subtype3	47	52.4 (35.9)

Figure S53. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.534 (Chi-square test), Q value = 1

Table S58. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	85	41	54	8
subtype1	30	19	16	3
subtype2	28	13	25	3
subtype3	27	9	13	2

Figure S54. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.403 (ANOVA), Q value = 1

Table S59. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	134	1958.9 (12.0)
subtype1	49	1957.0 (11.3)
subtype2	45	1960.0 (12.4)
subtype3	40	1959.8 (12.5)

Figure S55. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.372 (Chi-square test), Q value = 1

Table S60. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	133	3	2	11
subtype1	44	0	0	6
subtype2	56	1	1	3
subtype3	33	2	1	2

Figure S56. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S61. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	59	73	62

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.00318 (logrank test), Q value = 0.44

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	181	72	0.0 - 173.8 (15.8)
subtype1	56	15	0.0 - 173.8 (23.0)
subtype2	69	30	0.2 - 115.6 (14.1)
subtype3	56	27	0.1 - 119.8 (12.4)

Figure S57. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0181 (ANOVA), Q value = 1

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	186	67.5 (9.5)
subtype1	58	65.6 (10.6)
subtype2	69	66.6 (8.5)
subtype3	59	70.3 (9.1)

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.29 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	145
subtype1	12	47
subtype2	17	56
subtype3	20	42

Figure S59. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.527 (ANOVA), Q value = 1

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	34	30.3 (39.3)
subtype1	8	43.8 (35.0)
subtype2	14	28.6 (40.0)
subtype3	12	23.3 (42.3)

Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.306 (Chi-square test), Q value = 1

Table S66. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	3	1	1	189
subtype1	2	1	1	55
subtype2	0	0	0	73
subtype3	1	0	0	61

Figure S61. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.177 (Chi-square test), Q value = 1

Table S67. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	44	119	20	11
subtype1	15	34	4	6
subtype2	11	51	9	2
subtype3	18	34	7	3

Figure S62. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.373 (Chi-square test), Q value = 1

Table S68. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	124	50	15	4
subtype1	34	17	6	2
subtype2	44	22	5	2
subtype3	46	11	4	0

Figure S63. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RPPA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.611 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	MX
ALL	175	16
subtype1	53	6
subtype2	67	4
subtype3	55	6

Figure S64. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

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

Table S70. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III
ALL	98	57	36
subtype1	27	18	14
subtype2	33	26	12
subtype3	38	13	10

Figure S65. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.736 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	8	186
subtype1	3	56
subtype2	2	71
subtype3	3	59

Figure S66. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0565 (ANOVA), Q value = 1

Table S72. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	163	51.7 (32.5)
subtype1	50	60.6 (40.1)
subtype2	61	49.4 (30.2)
subtype3	52	45.9 (25.1)

Figure S67. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.079 (Chi-square test), Q value = 1

Table S73. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	91	48	39	10
subtype1	32	11	9	5
subtype2	37	15	14	4
subtype3	22	22	16	1

Figure S68. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.168 (ANOVA), Q value = 1

Table S74. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	129	1958.4 (12.1)
subtype1	38	1961.2 (11.8)
subtype2	49	1958.2 (11.1)
subtype3	42	1956.1 (13.3)

Figure S69. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.037 (Chi-square test), Q value = 1

Table S75. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	143	4	4	9
subtype1	41	0	2	5
subtype2	54	4	2	4
subtype3	48	0	0	0

Figure S70. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S76. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	57	48	49	40

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00426 (logrank test), Q value = 0.58

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	181	72	0.0 - 173.8 (15.8)
subtype1	53	19	0.0 - 173.8 (16.8)
subtype2	44	15	0.2 - 99.2 (23.0)
subtype3	46	18	0.2 - 115.6 (11.9)
subtype4	38	20	0.1 - 82.2 (10.2)

Figure S71. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0866 (ANOVA), Q value = 1

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	186	67.5 (9.5)
subtype1	55	65.8 (10.6)
subtype2	46	69.4 (8.9)
subtype3	46	65.8 (7.9)
subtype4	39	69.4 (9.9)

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.865 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	145
subtype1	15	42
subtype2	14	34
subtype3	11	38
subtype4	9	31

Figure S73. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.918 (ANOVA), Q value = 1

Table S80. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	34	30.3 (39.3)
subtype1	10	27.0 (33.7)
subtype2	4	42.5 (49.2)
subtype3	7	32.9 (41.1)
subtype4	13	27.7 (43.4)

Figure S74. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.195 (Chi-square test), Q value = 1

Table S81. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	3	1	1	189
subtype1	3	1	1	52
subtype2	0	0	0	48
subtype3	0	0	0	49
subtype4	0	0	0	40

Figure S75. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.262 (Chi-square test), Q value = 1

Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	44	119	20	11
subtype1	14	32	5	6
subtype2	16	25	5	2
subtype3	6	36	6	1
subtype4	8	26	4	2

Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.132 (Chi-square test), Q value = 1

Table S83. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	124	50	15	4
subtype1	33	16	8	0
subtype2	32	9	4	2
subtype3	29	17	1	2
subtype4	30	8	2	0

Figure S77. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RPPA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.298 (Fisher's exact test), Q value = 1

Table S84. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	MX
ALL	175	16
subtype1	52	5
subtype2	45	3
subtype3	45	2
subtype4	33	6

Figure S78. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.217 (Chi-square test), Q value = 1

Table S85. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III
ALL	98	57	36
subtype1	25	18	14
subtype2	28	10	10
subtype3	21	19	8
subtype4	24	10	4

Figure S79. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.731 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	8	186
subtype1	3	54
subtype2	3	45
subtype3	1	48
subtype4	1	39

Figure S80. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.116 (ANOVA), Q value = 1

Table S87. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	163	51.7 (32.5)
subtype1	49	61.1 (45.3)
subtype2	40	49.0 (25.0)
subtype3	42	47.4 (24.2)
subtype4	32	46.5 (24.3)

Figure S81. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.21 (Chi-square test), Q value = 1

Table S88. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	91	48	39	10
subtype1	26	13	10	6
subtype2	22	13	12	1
subtype3	29	8	8	2
subtype4	14	14	9	1

Figure S82. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.244 (ANOVA), Q value = 1

Table S89. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	129	1958.4 (12.1)
subtype1	38	1959.1 (10.9)
subtype2	33	1958.5 (13.9)
subtype3	35	1960.5 (11.6)
subtype4	23	1954.0 (11.8)

Figure S83. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0263 (Chi-square test), Q value = 1

Table S90. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	143	4	4	9
subtype1	45	0	0	5
subtype2	37	0	2	0
subtype3	36	4	2	2
subtype4	25	0	0	2

Figure S84. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S91. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	70	97	97	52

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.342 (logrank test), Q value = 1

Table S92. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	299	117	0.0 - 173.8 (13.1)
subtype1	68	27	0.0 - 122.4 (12.1)
subtype2	92	33	0.2 - 141.3 (21.9)
subtype3	92	32	0.0 - 114.0 (7.8)
subtype4	47	25	0.4 - 173.8 (20.4)

Figure S85. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0871 (ANOVA), Q value = 1

Table S93. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	308	67.3 (8.6)
subtype1	69	66.5 (9.3)
subtype2	95	65.9 (7.9)
subtype3	94	68.9 (8.0)
subtype4	50	68.1 (9.7)

Figure S86. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.304 (Fisher's exact test), Q value = 1

Table S94. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	236
subtype1	15	55
subtype2	20	77
subtype3	30	67
subtype4	15	37

Figure S87. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.756 (ANOVA), Q value = 1

Table S95. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	53	27.4 (39.1)
subtype1	6	40.0 (43.8)
subtype2	13	20.8 (35.2)
subtype3	19	24.7 (38.4)
subtype4	15	31.3 (43.7)

Figure S88. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.385 (Chi-square test), Q value = 1

Table S96. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	7	1	1	1	306
subtype1	0	0	1	0	69
subtype2	3	1	0	0	93
subtype3	4	0	0	1	92
subtype4	0	0	0	0	52

Figure S89. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.383 (Chi-square test), Q value = 1

Table S97. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	76	190	33	17
subtype1	11	46	10	3
subtype2	20	62	9	6
subtype3	28	53	9	7
subtype4	17	29	5	1

Figure S90. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.222 (Chi-square test), Q value = 1

Table S98. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	195	89	25	5
subtype1	42	19	6	2
subtype2	59	31	6	1
subtype3	59	32	5	1
subtype4	35	7	8	1

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

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.377 (Chi-square test), Q value = 1

Table S99. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	279	4	27
subtype1	60	0	8
subtype2	89	0	6
subtype3	85	3	8
subtype4	45	1	5

Figure S92. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.524 (Chi-square test), Q value = 1

Table S100. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	161	87	60	4
subtype1	36	17	16	0
subtype2	50	30	17	0
subtype3	45	29	18	3
subtype4	30	11	9	1

Figure S93. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.406 (Fisher's exact test), Q value = 1

Table S101. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	10	306
subtype1	2	68
subtype2	1	96
subtype3	4	93
subtype4	3	49

Figure S94. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.731 (ANOVA), Q value = 1

Table S102. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	266	52.7 (32.6)
subtype1	62	50.2 (34.8)
subtype2	83	51.3 (27.6)
subtype3	78	56.0 (34.8)
subtype4	43	53.2 (34.6)

Figure S95. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.314 (Chi-square test), Q value = 1

Table S103. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	152	72	71	14
subtype1	32	18	13	3
subtype2	44	17	32	4
subtype3	46	25	19	5
subtype4	30	12	7	2

Figure S96. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.191 (ANOVA), Q value = 1

Table S104. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	209	1958.8 (11.5)
subtype1	48	1958.0 (13.3)
subtype2	57	1961.5 (12.3)
subtype3	68	1958.2 (10.1)
subtype4	36	1956.7 (9.8)

Figure S97. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.575 (Chi-square test), Q value = 1

Table S105. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	250	6	4	16
subtype1	51	2	2	3
subtype2	84	2	0	4
subtype3	77	2	1	4
subtype4	38	0	1	5

Figure S98. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S106. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	101	61	154

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.706 (logrank test), Q value = 1

Table S107. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	299	117	0.0 - 173.8 (13.1)
subtype1	95	34	0.2 - 141.3 (14.6)
subtype2	60	19	0.0 - 114.0 (7.4)
subtype3	144	64	0.0 - 173.8 (16.7)

Figure S99. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0601 (ANOVA), Q value = 1

Table S108. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	308	67.3 (8.6)
subtype1	98	65.9 (8.2)
subtype2	60	69.2 (7.8)
subtype3	150	67.5 (9.1)

Figure S100. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0531 (Fisher's exact test), Q value = 1

Table S109. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	236
subtype1	19	82
subtype2	22	39
subtype3	39	115

Figure S101. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.84 (ANOVA), Q value = 1

Table S110. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	53	27.4 (39.1)
subtype1	15	23.3 (36.6)
subtype2	15	26.0 (39.2)
subtype3	23	30.9 (42.0)

Figure S102. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0359 (Chi-square test), Q value = 1

Table S111. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	7	1	1	1	306
subtype1	3	1	0	0	97
subtype2	4	0	0	1	56
subtype3	0	0	1	0	153

Figure S103. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.696 (Chi-square test), Q value = 1

Table S112. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	76	190	33	17
subtype1	21	64	11	5
subtype2	20	31	6	4
subtype3	35	95	16	8

Figure S104. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.619 (Chi-square test), Q value = 1

Table S113. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	195	89	25	5
subtype1	61	33	5	2
subtype2	38	18	5	0
subtype3	96	38	15	3

Figure S105. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.332 (Chi-square test), Q value = 1

Table S114. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	279	4	27
subtype1	93	0	6
subtype2	53	2	6
subtype3	133	2	15

Figure S106. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.586 (Chi-square test), Q value = 1

Table S115. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	161	87	60	4
subtype1	52	30	19	0
subtype2	28	15	14	2
subtype3	81	42	27	2

Figure S107. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.524 (Fisher's exact test), Q value = 1

Table S116. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	10	306
subtype1	2	99
subtype2	1	60
subtype3	7	147

Figure S108. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.627 (ANOVA), Q value = 1

Table S117. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	266	52.7 (32.6)
subtype1	87	50.7 (28.1)
subtype2	49	56.3 (38.3)
subtype3	130	52.7 (33.1)

Figure S109. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

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

Table S118. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	152	72	71	14
subtype1	45	19	33	4
subtype2	30	17	10	2
subtype3	77	36	28	8

Figure S110. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.109 (ANOVA), Q value = 1

Table S119. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	209	1958.8 (11.5)
subtype1	58	1961.3 (12.0)
subtype2	46	1959.0 (11.2)
subtype3	105	1957.4 (11.3)

Figure S111. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.348 (Chi-square test), Q value = 1

Table S120. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	250	6	4	16
subtype1	88	2	1	3
subtype2	50	0	0	2
subtype3	112	4	3	11

Figure S112. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S121. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	131	119	52

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.87 (logrank test), Q value = 1

Table S122. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	286	108	0.0 - 173.8 (13.1)
subtype1	126	53	0.0 - 173.8 (16.7)
subtype2	110	36	0.0 - 107.0 (7.0)
subtype3	50	19	0.2 - 99.2 (23.3)

Figure S113. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.388 (ANOVA), Q value = 1

Table S123. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	295	67.7 (8.6)
subtype1	130	67.0 (8.6)
subtype2	114	68.1 (8.9)
subtype3	51	68.8 (8.3)

Figure S114. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.385 (Fisher's exact test), Q value = 1

Table S124. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	78	224
subtype1	39	92
subtype2	28	91
subtype3	11	41

Figure S115. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0204 (ANOVA), Q value = 1

Table S125. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	48	27.1 (39.0)
subtype1	27	14.4 (31.7)
subtype2	13	50.0 (40.8)
subtype3	8	32.5 (45.0)

Figure S116. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.586 (Chi-square test), Q value = 1

Table S126. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	5	1	1	1	294
subtype1	1	0	1	0	129
subtype2	2	1	0	1	115
subtype3	2	0	0	0	50

Figure S117. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.437 (Chi-square test), Q value = 1

Table S127. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	72	182	32	16
subtype1	27	84	11	9
subtype2	32	66	17	4
subtype3	13	32	4	3

Figure S118. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.215 (Chi-square test), Q value = 1

Table S128. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	185	85	26	4
subtype1	81	32	15	3
subtype2	69	38	10	0
subtype3	35	15	1	1

Figure S119. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 4.76e-07 (Chi-square test), Q value = 6.7e-05

Table S129. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	263	3	30
subtype1	125	3	1
subtype2	92	0	26
subtype3	46	0	3

Figure S120. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.0316 (Chi-square test), Q value = 1

Table S130. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	154	84	58	3
subtype1	69	28	31	3
subtype2	54	44	19	0
subtype3	31	12	8	0

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

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

P value = 0.835 (Fisher's exact test), Q value = 1

Table S131. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	10	292
subtype1	4	127
subtype2	5	114
subtype3	1	51

Figure S122. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.087 (ANOVA), Q value = 1

Table S132. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	256	53.2 (33.3)
subtype1	116	57.6 (38.4)
subtype2	96	47.5 (28.2)
subtype3	44	53.7 (27.2)

Figure S123. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.391 (Chi-square test), Q value = 1

Table S133. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	146	68	70	12
subtype1	65	34	26	6
subtype2	57	23	27	6
subtype3	24	11	17	0

Figure S124. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.443 (ANOVA), Q value = 1

Table S134. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	203	1958.7 (11.7)
subtype1	87	1957.6 (10.1)
subtype2	83	1959.2 (12.1)
subtype3	33	1960.3 (14.2)

Figure S125. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0417 (Chi-square test), Q value = 1

Table S135. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	234	5	3	14
subtype1	119	1	2	3
subtype2	71	2	1	10
subtype3	44	2	0	1

Figure S126. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S136. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	88	155	59

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.584 (logrank test), Q value = 1

Table S137. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	286	108	0.0 - 173.8 (13.1)
subtype1	84	34	0.0 - 114.0 (18.1)
subtype2	146	55	0.0 - 173.8 (12.0)
subtype3	56	19	0.2 - 141.3 (15.5)

Figure S127. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.207 (ANOVA), Q value = 1

Table S138. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	295	67.7 (8.6)
subtype1	85	69.0 (8.2)
subtype2	153	66.9 (8.9)
subtype3	57	68.0 (8.4)

Figure S128. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.924 (Fisher's exact test), Q value = 1

Table S139. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	78	224
subtype1	24	64
subtype2	39	116
subtype3	15	44

Figure S129. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.773 (ANOVA), Q value = 1

Table S140. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	48	27.1 (39.0)
subtype1	15	26.0 (39.1)
subtype2	28	29.6 (40.5)
subtype3	5	16.0 (35.8)

Figure S130. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.837 (Chi-square test), Q value = 1

Table S141. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	5	1	1	1	294
subtype1	1	0	0	0	87
subtype2	2	1	1	1	150
subtype3	2	0	0	0	57

Figure S131. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.0792 (Chi-square test), Q value = 1

Table S142. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	72	182	32	16
subtype1	27	44	14	3
subtype2	30	102	15	8
subtype3	15	36	3	5

Figure S132. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.735 (Chi-square test), Q value = 1

Table S143. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	185	85	26	4
subtype1	55	25	7	1
subtype2	93	41	17	2
subtype3	37	19	2	1

Figure S133. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.253 (Chi-square test), Q value = 1

Table S144. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	263	3	30
subtype1	79	0	8
subtype2	131	3	19
subtype3	53	0	3

Figure S134. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.583 (Chi-square test), Q value = 1

Table S145. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	154	84	58	3
subtype1	47	22	19	0
subtype2	74	47	29	3
subtype3	33	15	10	0

Figure S135. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.107 (Fisher's exact test), Q value = 1

Table S146. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	10	292
subtype1	5	83
subtype2	2	153
subtype3	3	56

Figure S136. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.772 (ANOVA), Q value = 1

Table S147. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	256	53.2 (33.3)
subtype1	77	50.9 (31.8)
subtype2	128	54.0 (36.3)
subtype3	51	54.6 (27.4)

Figure S137. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.123 (Chi-square test), Q value = 1

Table S148. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	146	68	70	12
subtype1	43	16	24	3
subtype2	71	42	29	9
subtype3	32	10	17	0

Figure S138. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.776 (ANOVA), Q value = 1

Table S149. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	203	1958.7 (11.7)
subtype1	62	1958.1 (12.4)
subtype2	102	1958.6 (10.2)
subtype3	39	1959.8 (14.0)

Figure S139. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.231 (Chi-square test), Q value = 1

Table S150. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	234	5	3	14
subtype1	69	1	0	3
subtype2	118	2	1	10
subtype3	47	2	2	1

Figure S140. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Methods & Data

Input

Cluster data file = LUSC-TP.mergedcluster.txt
Clinical data file = LUSC-TP.clin.merged.picked.txt
Number of patients = 327
Number of clustering approaches = 10
Number of selected clinical features = 14
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Q value calculation

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

Download Results

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

References

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

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

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

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] 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)

[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

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

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