Correlation between molecular cancer subtypes and selected clinical features

Lung Adenocarcinoma (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 Adenocarcinoma (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/C15Q4T26

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 393 patients, 10 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 do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'GENDER'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'TOBACCOSMOKINGHISTORYINDICATOR'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'TOBACCOSMOKINGHISTORYINDICATOR'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 9 subtypes that correlate to 'AGE', 'GENDER', 'HISTOLOGICAL.TYPE', and 'TOBACCOSMOKINGHISTORYINDICATOR'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'GENDER' and 'TOBACCOSMOKINGHISTORYINDICATOR'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

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, 10 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	0.821 (1.00)	0.914 (1.00)	0.128 (1.00)	0.115 (1.00)	0.0886 (1.00)	0.0915 (1.00)	0.046 (1.00)	0.0217 (1.00)	0.248 (1.00)	0.652 (1.00)
AGE	ANOVA	0.477 (1.00)	0.557 (1.00)	0.00306 (0.392)	0.136 (1.00)	0.0292 (1.00)	0.0231 (1.00)	0.00121 (0.156)	0.0242 (1.00)	0.173 (1.00)	0.0544 (1.00)
GENDER	Fisher's exact test	0.272 (1.00)	0.383 (1.00)	0.134 (1.00)	0.000846 (0.11)	0.307 (1.00)	0.159 (1.00)	3.08e-08 (4.24e-06)	0.000305 (0.0409)	0.774 (1.00)	0.00639 (0.805)
KARNOFSKY PERFORMANCE SCORE	ANOVA			0.468 (1.00)	0.191 (1.00)	0.0383 (1.00)	0.54 (1.00)	0.934 (1.00)	0.238 (1.00)	0.823 (1.00)	0.823 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.3 (1.00)	0.274 (1.00)	0.411 (1.00)	0.395 (1.00)	0.101 (1.00)	0.00532 (0.676)	0.000797 (0.104)	0.014 (1.00)	0.0155 (1.00)	0.000329 (0.0434)
PATHOLOGY T	Chi-square test	0.489 (1.00)	0.479 (1.00)	0.0548 (1.00)	0.962 (1.00)	0.242 (1.00)	0.562 (1.00)	0.382 (1.00)	0.437 (1.00)	0.819 (1.00)	0.625 (1.00)
PATHOLOGY N	Chi-square test	0.572 (1.00)	0.651 (1.00)	0.319 (1.00)	0.821 (1.00)	0.376 (1.00)	0.612 (1.00)	0.482 (1.00)	0.101 (1.00)	0.684 (1.00)	0.763 (1.00)
PATHOLOGICSPREAD(M)	Chi-square test	0.504 (1.00)	1 (1.00)	0.883 (1.00)	0.752 (1.00)	0.837 (1.00)	0.756 (1.00)	0.335 (1.00)	0.975 (1.00)	0.15 (1.00)	0.834 (1.00)
TUMOR STAGE	Chi-square test	0.147 (1.00)	0.274 (1.00)	0.0224 (1.00)	0.936 (1.00)	0.813 (1.00)	0.97 (1.00)	0.804 (1.00)	0.226 (1.00)	0.695 (1.00)	0.809 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	1 (1.00)	1 (1.00)	0.719 (1.00)	0.776 (1.00)	0.945 (1.00)	0.587 (1.00)	0.701 (1.00)	0.915 (1.00)	0.911 (1.00)	0.586 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.448 (1.00)	0.357 (1.00)	0.241 (1.00)	0.0487 (1.00)	0.458 (1.00)	0.208 (1.00)	0.0483 (1.00)	0.0611 (1.00)	0.238 (1.00)	0.375 (1.00)
TOBACCOSMOKINGHISTORYINDICATOR	Chi-square test	0.0492 (1.00)	0.184 (1.00)	0.00766 (0.957)	0.0131 (1.00)	0.000308 (0.0409)	2.01e-05 (0.00273)	1e-05 (0.00138)	0.000222 (0.0299)	0.054 (1.00)	0.0546 (1.00)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.616 (1.00)	0.68 (1.00)	0.155 (1.00)	0.729 (1.00)	0.068 (1.00)	0.0502 (1.00)	0.0141 (1.00)	0.11 (1.00)	0.0643 (1.00)	0.0463 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.188 (1.00)	0.58 (1.00)	0.995 (1.00)	0.939 (1.00)	0.12 (1.00)	0.497 (1.00)	0.895 (1.00)	0.547 (1.00)	0.497 (1.00)	0.669 (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	5	9	12	6

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.821 (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	31	4	0.5 - 56.8 (8.3)
subtype1	4	0	6.0 - 48.6 (9.7)
subtype2	9	1	4.0 - 56.8 (8.3)
subtype3	12	1	0.5 - 37.0 (3.3)
subtype4	6	2	2.0 - 45.2 (30.9)

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 = 0.477 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	30	65.7 (10.8)
subtype1	4	58.5 (15.5)
subtype2	9	65.0 (9.1)
subtype3	12	67.1 (11.1)
subtype4	5	69.4 (9.0)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.272 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	18	14
subtype1	3	2
subtype2	3	6
subtype3	9	3
subtype4	3	3

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.3 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG CLEAR CELL ADENOCARCINOMA
ALL	1	30	1
subtype1	0	4	1
subtype2	0	9	0
subtype3	1	11	0
subtype4	0	6	0

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.489 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3
ALL	12	19	1
subtype1	3	2	0
subtype2	4	4	1
subtype3	4	8	0
subtype4	1	5	0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.572 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	23	4	4
subtype1	3	1	1
subtype2	8	1	0
subtype3	9	1	1
subtype4	3	1	2

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.504 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	30	2
subtype1	4	1
subtype2	9	0
subtype3	11	1
subtype4	6	0

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.147 (Chi-square test), Q value = 1

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	23	4	3	2
subtype1	3	1	0	1
subtype2	8	0	1	0
subtype3	10	1	0	1
subtype4	2	2	2	0

Figure S8. 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 = 1 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	31
subtype1	0	5
subtype2	0	9
subtype3	1	11
subtype4	0	6

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.448 (ANOVA), Q value = 1

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	20	41.1 (15.0)
subtype1	2	29.0 (12.7)
subtype2	9	47.0 (15.5)
subtype3	6	37.0 (13.1)
subtype4	3	40.0 (17.3)

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'mRNA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0492 (Chi-square test), Q value = 1

Table S12. 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	12	10	5	5
subtype1	3	0	0	2
subtype2	5	3	1	0
subtype3	2	6	1	3
subtype4	2	1	3	0

Figure S11. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.616 (ANOVA), Q value = 1

Table S13. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1968.5 (11.4)
subtype1	2	1977.0 (18.4)
subtype2	6	1971.2 (14.2)
subtype3	6	1965.8 (8.2)
subtype4	5	1965.2 (9.8)

Figure S12. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.188 (Chi-square test), Q value = 1

Table S14. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R2	RX
ALL	26	1	1
subtype1	3	0	1
subtype2	7	1	0
subtype3	10	0	0
subtype4	6	0	0

Figure S13. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S15. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	7	13	12

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.914 (logrank test), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	31	4	0.5 - 56.8 (8.3)
subtype1	7	2	2.0 - 48.6 (38.7)
subtype2	13	1	0.5 - 37.0 (3.4)
subtype3	11	1	4.0 - 56.8 (8.1)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.557 (ANOVA), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	30	65.7 (10.8)
subtype1	5	69.4 (9.0)
subtype2	13	66.5 (10.9)
subtype3	12	63.3 (11.6)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.383 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	18	14
subtype1	4	3
subtype2	9	4
subtype3	5	7

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.274 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG CLEAR CELL ADENOCARCINOMA
ALL	1	30	1
subtype1	0	6	1
subtype2	1	12	0
subtype3	0	12	0

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.479 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3
ALL	12	19	1
subtype1	2	5	0
subtype2	4	9	0
subtype3	6	5	1

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.651 (Chi-square test), Q value = 1

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	23	4	4
subtype1	4	1	2
subtype2	10	1	1
subtype3	9	2	1

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

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

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	30	2
subtype1	7	0
subtype2	12	1
subtype3	11	1

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.274 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	23	4	3	2
subtype1	3	2	2	0
subtype2	11	1	0	1
subtype3	9	1	1	1

Figure S21. 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 = 1 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	31
subtype1	0	7
subtype2	1	12
subtype3	0	12

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.357 (ANOVA), Q value = 1

Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	20	41.1 (15.0)
subtype1	3	40.0 (17.3)
subtype2	7	35.0 (13.1)
subtype3	10	45.8 (15.4)

Figure S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'mRNA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.184 (Chi-square test), Q value = 1

Table S26. 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	12	10	5	5
subtype1	3	1	3	0
subtype2	3	6	1	3
subtype3	6	3	1	2

Figure S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.68 (ANOVA), Q value = 1

Table S27. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1968.5 (11.4)
subtype1	6	1965.0 (8.8)
subtype2	7	1969.9 (13.0)
subtype3	6	1970.5 (12.9)

Figure S25. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.58 (Chi-square test), Q value = 1

Table S28. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R2	RX
ALL	26	1	1
subtype1	6	0	0
subtype2	10	0	0
subtype3	10	1	1

Figure S26. 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 S29. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	120	152	74	43

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

P value = 0.128 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	349	85	0.0 - 224.0 (8.1)
subtype1	107	28	0.0 - 224.0 (9.3)
subtype2	137	29	0.0 - 104.2 (6.6)
subtype3	66	20	0.1 - 88.1 (8.2)
subtype4	39	8	0.1 - 83.8 (8.4)

Figure S27. 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 = 0.00306 (ANOVA), Q value = 0.39

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	358	65.2 (9.8)
subtype1	110	63.5 (10.0)
subtype2	139	67.6 (9.1)
subtype3	70	64.1 (9.9)
subtype4	39	63.5 (10.0)

Figure S28. 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 = 0.134 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	211	178
subtype1	70	50
subtype2	87	65
subtype3	37	37
subtype4	17	26

Figure S29. 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 = 0.468 (ANOVA), Q value = 1

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	29	75.9 (32.4)
subtype1	11	70.0 (36.6)
subtype2	8	91.2 (8.3)
subtype3	6	73.3 (37.2)
subtype4	4	65.0 (43.6)

Figure S30. 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 = 0.411 (Chi-square test), Q value = 1

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	11	79	243	4	17	2	3	2	18	3	4	3
subtype1	3	21	76	2	8	1	2	0	6	1	0	0
subtype2	3	34	87	2	7	1	1	2	9	0	3	3
subtype3	1	14	53	0	2	0	0	0	2	1	1	0
subtype4	4	10	27	0	0	0	0	0	1	1	0	0

Figure S31. 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 = 0.0548 (Chi-square test), Q value = 1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	80	169	22	16
subtype1	24	53	4	3
subtype2	34	61	7	8
subtype3	8	40	9	4
subtype4	14	15	2	1

Figure S32. 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 = 0.319 (Chi-square test), Q value = 1

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	172	58	52
subtype1	48	16	18
subtype2	69	24	16
subtype3	35	16	10
subtype4	20	2	8

Figure S33. 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 = 0.883 (Chi-square test), Q value = 1

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	202	16	63
subtype1	56	6	20
subtype2	78	4	26
subtype3	44	4	11
subtype4	24	2	6

Figure S34. 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 = 0.0224 (Chi-square test), Q value = 1

Table S38. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	149	61	57	16
subtype1	43	16	17	6
subtype2	64	23	16	5
subtype3	22	21	14	4
subtype4	20	1	10	1

Figure S35. 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 = 0.719 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	20	369
subtype1	5	115
subtype2	7	145
subtype3	5	69
subtype4	3	40

Figure S36. 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 = 0.241 (ANOVA), Q value = 1

Table S40. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	274	41.3 (27.0)
subtype1	76	45.4 (25.0)
subtype2	104	38.5 (28.1)
subtype3	61	43.4 (26.8)
subtype4	33	36.6 (28.1)

Figure S37. 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 = 0.00766 (Chi-square test), Q value = 0.96

Table S41. 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	100	77	62	36
subtype1	30	20	17	14
subtype2	35	39	14	16
subtype3	23	9	22	5
subtype4	12	9	9	1

Figure S38. 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 = 0.155 (ANOVA), Q value = 1

Table S42. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	206	1965.4 (12.6)
subtype1	55	1963.9 (12.6)
subtype2	77	1963.8 (12.4)
subtype3	47	1968.4 (13.3)
subtype4	27	1967.4 (10.7)

Figure S39. 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 = 0.995 (Chi-square test), Q value = 1

Table S43. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	233	9	4	14
subtype1	72	3	1	6
subtype2	88	3	2	5
subtype3	49	2	1	2
subtype4	24	1	0	1

Figure S40. 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 S44. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	69	78	73	81	22

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.115 (logrank test), Q value = 1

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	289	64	0.0 - 224.0 (6.5)
subtype1	61	11	0.0 - 224.0 (5.9)
subtype2	69	21	0.1 - 88.1 (8.5)
subtype3	65	9	0.0 - 77.9 (5.2)
subtype4	74	17	0.0 - 71.5 (6.2)
subtype5	20	6	0.3 - 44.4 (7.0)

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.136 (ANOVA), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	295	65.1 (9.9)
subtype1	61	67.3 (8.9)
subtype2	72	62.9 (9.9)
subtype3	68	65.6 (10.1)
subtype4	73	65.3 (10.2)
subtype5	21	64.1 (10.7)

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.000846 (Chi-square test), Q value = 0.11

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	175	148
subtype1	45	24
subtype2	28	50
subtype3	36	37
subtype4	51	30
subtype5	15	7

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

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

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	22	74.5 (31.9)
subtype1	6	93.3 (5.2)
subtype2	4	85.0 (5.8)
subtype3	7	80.0 (12.9)
subtype4	3	60.0 (52.9)
subtype5	2	0.0 (0.0)

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.395 (Chi-square test), Q value = 1

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	11	64	195	4	17	1	2	2	17	3	4	3
subtype1	5	16	30	2	7	0	1	2	4	0	1	1
subtype2	3	16	51	0	1	0	0	0	4	1	2	0
subtype3	0	18	45	1	3	0	1	0	4	0	0	1
subtype4	1	11	55	1	4	1	0	0	4	2	1	1
subtype5	2	3	14	0	2	0	0	0	1	0	0	0

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.962 (Chi-square test), Q value = 1

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	63	127	17	13
subtype1	18	23	4	3
subtype2	13	34	5	4
subtype3	14	34	4	3
subtype4	16	31	3	2
subtype5	2	5	1	1

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.821 (Chi-square test), Q value = 1

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	131	44	42
subtype1	33	8	7
subtype2	34	9	12
subtype3	31	13	10
subtype4	28	11	12
subtype5	5	3	1

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

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

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	141	10	65
subtype1	33	1	13
subtype2	37	4	14
subtype3	31	3	18
subtype4	34	1	18
subtype5	6	1	2

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.936 (Chi-square test), Q value = 1

Table S53. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	112	47	47	11
subtype1	29	9	8	1
subtype2	26	12	15	3
subtype3	25	12	11	4
subtype4	27	12	12	2
subtype5	5	2	1	1

Figure S49. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.776 (Chi-square test), Q value = 1

Table S54. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	16	307
subtype1	2	67
subtype2	4	74
subtype3	3	70
subtype4	6	75
subtype5	1	21

Figure S50. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0487 (ANOVA), Q value = 1

Table S55. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	224	40.6 (26.9)
subtype1	43	33.2 (21.1)
subtype2	64	46.7 (31.8)
subtype3	51	40.1 (26.9)
subtype4	51	36.7 (22.6)
subtype5	15	50.2 (27.3)

Figure S51. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0131 (Chi-square test), Q value = 1

Table S56. 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	77	59	46	27
subtype1	13	20	4	9
subtype2	22	9	20	3
subtype3	17	16	14	7
subtype4	22	11	7	6
subtype5	3	3	1	2

Figure S52. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.729 (ANOVA), Q value = 1

Table S57. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	176	1965.4 (12.3)
subtype1	33	1963.6 (11.7)
subtype2	54	1966.5 (14.3)
subtype3	39	1965.7 (11.8)
subtype4	36	1966.1 (10.8)
subtype5	14	1962.4 (10.7)

Figure S53. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.939 (Chi-square test), Q value = 1

Table S58. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	176	7	1	12
subtype1	33	1	0	3
subtype2	46	2	0	3
subtype3	44	2	0	3
subtype4	38	1	1	3
subtype5	15	1	0	0

Figure S54. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S59. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	86	75	76

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0886 (logrank test), Q value = 1

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	214	66	0.0 - 224.0 (12.2)
subtype1	71	25	0.0 - 163.1 (12.1)
subtype2	71	17	0.1 - 224.0 (13.7)
subtype3	72	24	0.0 - 83.8 (9.8)

Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0292 (ANOVA), Q value = 1

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	216	64.8 (9.8)
subtype1	75	64.1 (10.1)
subtype2	71	67.3 (8.9)
subtype3	70	63.0 (10.1)

Figure S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.307 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	131	106
subtype1	53	33
subtype2	40	35
subtype3	38	38

Figure S57. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0383 (ANOVA), Q value = 1

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	21	69.0 (35.5)
subtype1	6	41.7 (46.2)
subtype2	6	91.7 (7.5)
subtype3	9	72.2 (28.6)

Figure S58. 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 = 0.101 (Chi-square test), Q value = 1

Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA
ALL	6	52	151	3	10	1	2	2	6	1	3
subtype1	2	19	56	0	3	1	1	1	1	1	1
subtype2	3	21	36	3	6	0	1	0	3	0	2
subtype3	1	12	59	0	1	0	0	1	2	0	0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.242 (Chi-square test), Q value = 1

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	60	138	17	13
subtype1	24	48	8	4
subtype2	20	41	2	7
subtype3	16	49	7	2

Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.376 (Chi-square test), Q value = 1

Table S66. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	133	46	44
subtype1	44	22	15
subtype2	44	13	12
subtype3	45	11	17

Figure S61. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

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

Table S67. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	160	12	53
subtype1	59	5	19
subtype2	46	4	19
subtype3	55	3	15

Figure S62. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.813 (Chi-square test), Q value = 1

Table S68. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	116	48	49	13
subtype1	42	20	16	5
subtype2	34	16	14	5
subtype3	40	12	19	3

Figure S63. 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 = 0.945 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	15	222
subtype1	6	80
subtype2	4	71
subtype3	5	71

Figure S64. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.458 (ANOVA), Q value = 1

Table S70. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	171	40.5 (26.7)
subtype1	52	42.1 (29.5)
subtype2	64	37.3 (25.2)
subtype3	55	42.9 (25.7)

Figure S65. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000308 (Chi-square test), Q value = 0.041

Table S71. 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	82	57	51	29
subtype1	26	24	11	19
subtype2	25	23	15	5
subtype3	31	10	25	5

Figure S66. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.068 (ANOVA), Q value = 1

Table S72. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	119	1964.7 (13.7)
subtype1	32	1965.2 (13.1)
subtype2	46	1961.4 (12.8)
subtype3	41	1968.1 (14.4)

Figure S67. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.12 (Chi-square test), Q value = 1

Table S73. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	127	6	3	12
subtype1	43	2	3	5
subtype2	43	3	0	1
subtype3	41	1	0	6

Figure S68. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S74. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	23	90	66	58

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0915 (logrank test), Q value = 1

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	214	66	0.0 - 224.0 (12.2)
subtype1	18	4	0.0 - 63.7 (7.1)
subtype2	77	30	0.7 - 163.1 (13.2)
subtype3	63	19	0.0 - 83.8 (8.8)
subtype4	56	13	0.1 - 224.0 (14.5)

Figure S69. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0231 (ANOVA), Q value = 1

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	216	64.8 (9.8)
subtype1	20	68.0 (6.8)
subtype2	79	63.4 (10.0)
subtype3	62	63.1 (10.7)
subtype4	55	67.5 (8.9)

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.159 (Fisher's exact test), Q value = 1

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	131	106
subtype1	15	8
subtype2	56	34
subtype3	33	33
subtype4	27	31

Figure S71. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.54 (ANOVA), Q value = 1

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	21	69.0 (35.5)
subtype1	4	70.0 (46.9)
subtype2	6	53.3 (42.7)
subtype3	6	70.0 (34.6)
subtype4	5	86.0 (11.4)

Figure S72. 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 = 0.00532 (Chi-square test), Q value = 0.68

Table S79. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA
ALL	6	52	151	3	10	1	2	2	6	1	3
subtype1	1	4	12	0	3	0	2	0	1	0	0
subtype2	1	20	63	0	2	0	0	0	2	1	1
subtype3	2	10	49	0	2	1	0	1	1	0	0
subtype4	2	18	27	3	3	0	0	1	2	0	2

Figure S73. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.562 (Chi-square test), Q value = 1

Table S80. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	60	138	17	13
subtype1	7	10	2	3
subtype2	23	56	3	5
subtype3	16	39	7	2
subtype4	14	33	5	3

Figure S74. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.612 (Chi-square test), Q value = 1

Table S81. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	133	46	44
subtype1	14	4	4
subtype2	46	21	16
subtype3	38	9	16
subtype4	35	12	8

Figure S75. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.756 (Chi-square test), Q value = 1

Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	160	12	53
subtype1	13	1	8
subtype2	61	6	19
subtype3	46	2	15
subtype4	40	3	11

Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.97 (Chi-square test), Q value = 1

Table S83. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	116	48	49	13
subtype1	11	5	5	1
subtype2	46	19	15	6
subtype3	31	12	18	3
subtype4	28	12	11	3

Figure S77. 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 = 0.587 (Fisher's exact test), Q value = 1

Table S84. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	15	222
subtype1	0	23
subtype2	6	84
subtype3	6	60
subtype4	3	55

Figure S78. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.208 (ANOVA), Q value = 1

Table S85. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	171	40.5 (26.7)
subtype1	17	48.7 (34.8)
subtype2	49	37.5 (25.2)
subtype3	53	44.6 (27.7)
subtype4	52	36.7 (23.5)

Figure S79. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 2.01e-05 (Chi-square test), Q value = 0.0027

Table S86. 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	82	57	51	29
subtype1	13	4	0	4
subtype2	26	23	15	18
subtype3	26	8	25	3
subtype4	17	22	11	4

Figure S80. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0502 (ANOVA), Q value = 1

Table S87. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	119	1964.7 (13.7)
subtype1	11	1962.1 (10.6)
subtype2	31	1965.6 (12.8)
subtype3	39	1968.9 (13.9)
subtype4	38	1960.5 (14.0)

Figure S81. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.497 (Chi-square test), Q value = 1

Table S88. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	127	6	3	12
subtype1	12	0	0	0
subtype2	46	3	3	4
subtype3	34	1	0	5
subtype4	35	2	0	3

Figure S82. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S89. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5	6	7	8	9
Number of samples	68	50	50	39	26	43	36	21	21

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.046 (logrank test), Q value = 1

Table S90. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	315	81	0.0 - 224.0 (8.5)
subtype1	60	16	0.0 - 77.9 (8.6)
subtype2	46	10	0.1 - 224.0 (6.5)
subtype3	46	18	0.0 - 68.8 (10.6)
subtype4	33	13	0.5 - 104.2 (18.9)
subtype5	22	3	0.1 - 48.5 (2.7)
subtype6	40	4	0.2 - 83.8 (14.3)
subtype7	32	12	0.2 - 55.4 (8.6)
subtype8	20	2	0.1 - 49.3 (6.3)
subtype9	16	3	0.1 - 85.3 (3.3)

Figure S83. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00121 (ANOVA), Q value = 0.16

Table S91. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	323	65.2 (9.8)
subtype1	64	62.4 (11.0)
subtype2	47	65.9 (9.5)
subtype3	45	64.4 (8.6)
subtype4	37	67.8 (7.6)
subtype5	23	71.3 (8.4)
subtype6	38	61.4 (9.5)
subtype7	32	65.7 (9.2)
subtype8	20	66.5 (11.2)
subtype9	17	68.2 (9.5)

Figure S84. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 3.08e-08 (Chi-square test), Q value = 4.2e-06

Table S92. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	163
subtype1	37	31
subtype2	43	7
subtype3	20	30
subtype4	10	29
subtype5	9	17
subtype6	21	22
subtype7	20	16
subtype8	13	8
subtype9	18	3

Figure S85. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.934 (ANOVA), Q value = 1

Table S93. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	29	75.9 (32.4)
subtype1	9	75.6 (31.7)
subtype2	4	75.0 (50.0)
subtype3	3	86.7 (5.8)
subtype4	1	90.0 (NA)
subtype6	5	66.0 (37.1)
subtype7	3	63.3 (55.1)
subtype8	1	90.0 (NA)
subtype9	3	86.7 (5.8)

Figure S86. 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 = 0.000797 (Chi-square test), Q value = 0.1

Table S94. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	10	75	214	4	17	2	3	2	17	3	4	3
subtype1	1	14	46	0	2	0	2	0	3	0	0	0
subtype2	2	9	30	2	4	0	0	0	3	0	0	0
subtype3	0	10	32	0	2	1	0	0	2	3	0	0
subtype4	1	16	15	0	2	0	0	0	5	0	0	0
subtype5	2	6	11	2	0	0	0	1	1	0	2	1
subtype6	3	8	28	0	2	1	0	0	0	0	1	0
subtype7	0	6	27	0	0	0	0	0	1	0	1	1
subtype8	1	5	11	0	1	0	0	1	1	0	0	1
subtype9	0	1	14	0	4	0	1	0	1	0	0	0

Figure S87. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.382 (Chi-square test), Q value = 1

Table S95. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	79	168	23	16
subtype1	17	33	7	4
subtype2	12	21	1	1
subtype3	12	29	1	2
subtype4	6	19	1	4
subtype5	4	13	4	0
subtype6	7	25	2	1
subtype7	7	16	4	2
subtype8	6	4	1	1
subtype9	8	8	2	1

Figure S88. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

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

Table S96. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	171	59	51
subtype1	27	18	15
subtype2	23	5	5
subtype3	27	8	9
subtype4	19	7	4
subtype5	15	3	3
subtype6	24	3	7
subtype7	16	7	6
subtype8	7	3	1
subtype9	13	5	1

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

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

P value = 0.335 (Chi-square test), Q value = 1

Table S97. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	200	16	64
subtype1	41	3	15
subtype2	23	2	8
subtype3	28	3	12
subtype4	24	3	3
subtype5	15	0	6
subtype6	25	1	8
subtype7	25	2	2
subtype8	5	2	5
subtype9	14	0	5

Figure S90. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.804 (Chi-square test), Q value = 1

Table S98. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	148	61	57	16
subtype1	24	16	14	4
subtype2	22	6	5	2
subtype3	24	8	10	2
subtype4	17	4	6	3
subtype5	13	5	3	0
subtype6	19	7	8	1
subtype7	12	7	8	2
subtype8	7	3	0	2
subtype9	10	5	3	0

Figure S91. 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 = 0.701 (Chi-square test), Q value = 1

Table S99. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	19	335
subtype1	6	62
subtype2	2	48
subtype3	2	48
subtype4	1	38
subtype5	0	26
subtype6	2	41
subtype7	2	34
subtype8	2	19
subtype9	2	19

Figure S92. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0483 (ANOVA), Q value = 1

Table S100. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	244	40.7 (27.0)
subtype1	51	44.6 (25.0)
subtype2	26	29.4 (20.4)
subtype3	35	49.2 (30.5)
subtype4	30	43.6 (31.8)
subtype5	18	40.8 (27.0)
subtype6	34	36.7 (24.9)
subtype7	25	43.6 (29.5)
subtype8	11	39.7 (24.8)
subtype9	14	24.7 (15.7)

Figure S93. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 1e-05 (Chi-square test), Q value = 0.0014

Table S101. 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	100	78	61	36
subtype1	24	18	14	3
subtype2	8	12	2	12
subtype3	21	4	15	3
subtype4	8	14	6	1
subtype5	4	10	1	5
subtype6	15	4	13	3
subtype7	13	6	4	4
subtype8	1	5	2	1
subtype9	6	5	4	4

Figure S94. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0141 (ANOVA), Q value = 1

Table S102. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	179	1965.2 (12.8)
subtype1	35	1966.4 (14.1)
subtype2	20	1962.0 (10.7)
subtype3	25	1968.8 (14.3)
subtype4	26	1956.8 (12.9)
subtype5	10	1962.0 (11.4)
subtype6	29	1967.4 (10.2)
subtype7	14	1968.4 (10.5)
subtype8	9	1969.8 (13.9)
subtype9	11	1967.8 (10.3)

Figure S95. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.895 (Chi-square test), Q value = 1

Table S103. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	203	9	4	12
subtype1	35	1	1	3
subtype2	29	3	1	2
subtype3	26	2	0	1
subtype4	23	0	0	2
subtype5	15	0	0	2
subtype6	24	1	0	1
subtype7	25	1	1	0
subtype8	11	0	1	1
subtype9	15	1	0	0

Figure S96. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S104. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	146	96	112

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0217 (logrank test), Q value = 1

Table S105. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	315	81	0.0 - 224.0 (8.5)
subtype1	129	28	0.1 - 224.0 (9.8)
subtype2	84	25	0.0 - 77.9 (7.9)
subtype3	102	28	0.0 - 83.8 (8.8)

Figure S97. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0242 (ANOVA), Q value = 1

Table S106. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	323	65.2 (9.8)
subtype1	133	66.9 (9.3)
subtype2	87	63.6 (10.7)
subtype3	103	64.3 (9.2)

Figure S98. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.000305 (Fisher's exact test), Q value = 0.041

Table S107. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	163
subtype1	94	52
subtype2	53	43
subtype3	44	68

Figure S99. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.238 (ANOVA), Q value = 1

Table S108. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	29	75.9 (32.4)
subtype1	11	83.6 (28.7)
subtype2	11	62.7 (42.2)
subtype3	7	84.3 (5.3)

Figure S100. 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 = 0.014 (Chi-square test), Q value = 1

Table S109. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	10	75	214	4	17	2	3	2	17	3	4	3
subtype1	7	36	72	4	10	0	1	2	9	0	2	3
subtype2	1	16	68	0	4	1	2	0	4	0	0	0
subtype3	2	23	74	0	3	1	0	0	4	3	2	0

Figure S101. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

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

Table S110. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	79	168	23	16
subtype1	39	59	9	6
subtype2	19	50	8	6
subtype3	21	59	6	4

Figure S102. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.101 (Chi-square test), Q value = 1

Table S111. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	171	59	51
subtype1	75	24	12
subtype2	43	19	19
subtype3	53	16	20

Figure S103. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.975 (Chi-square test), Q value = 1

Table S112. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	200	16	64
subtype1	80	6	24
subtype2	59	4	19
subtype3	61	6	21

Figure S104. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.226 (Chi-square test), Q value = 1

Table S113. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	148	61	57	16
subtype1	67	23	14	6
subtype2	36	19	22	5
subtype3	45	19	21	5

Figure S105. 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 = 0.915 (Fisher's exact test), Q value = 1

Table S114. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	19	335
subtype1	7	139
subtype2	5	91
subtype3	7	105

Figure S106. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0611 (ANOVA), Q value = 1

Table S115. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	244	40.7 (27.0)
subtype1	91	35.4 (25.4)
subtype2	66	44.1 (25.8)
subtype3	87	43.6 (28.8)

Figure S107. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000222 (Chi-square test), Q value = 0.03

Table S116. 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	100	78	61	36
subtype1	29	43	15	19
subtype2	30	21	19	11
subtype3	41	14	27	6

Figure S108. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.11 (ANOVA), Q value = 1

Table S117. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	179	1965.2 (12.8)
subtype1	68	1962.8 (13.1)
subtype2	47	1965.5 (13.0)
subtype3	64	1967.5 (12.1)

Figure S109. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.547 (Chi-square test), Q value = 1

Table S118. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	203	9	4	12
subtype1	80	2	2	6
subtype2	58	2	2	3
subtype3	65	5	0	3

Figure S110. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S119. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	140	165	81

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.248 (logrank test), Q value = 1

Table S120. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	346	83	0.0 - 224.0 (8.0)
subtype1	127	26	0.0 - 163.1 (7.8)
subtype2	145	37	0.0 - 83.8 (8.5)
subtype3	74	20	0.0 - 224.0 (7.9)

Figure S111. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.173 (ANOVA), Q value = 1

Table S121. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	356	65.2 (9.9)
subtype1	131	66.5 (9.6)
subtype2	154	64.5 (10.3)
subtype3	71	64.5 (9.2)

Figure S112. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.774 (Fisher's exact test), Q value = 1

Table S122. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	209	177
subtype1	77	63
subtype2	86	79
subtype3	46	35

Figure S113. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.823 (ANOVA), Q value = 1

Table S123. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	27	74.1 (32.8)
subtype1	11	71.8 (36.6)
subtype2	14	75.0 (32.5)
subtype3	2	80.0 (28.3)

Figure S114. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0155 (Chi-square test), Q value = 1

Table S124. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	11	78	242	4	17	2	2	2	18	3	4	3
subtype1	5	29	78	3	10	0	1	2	8	0	1	3
subtype2	6	40	104	0	4	2	0	0	5	3	1	0
subtype3	0	9	60	1	3	0	1	0	5	0	2	0

Figure S115. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.819 (Chi-square test), Q value = 1

Table S125. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	79	165	23	16
subtype1	36	57	9	6
subtype2	28	69	9	7
subtype3	15	39	5	3

Figure S116. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.684 (Chi-square test), Q value = 1

Table S126. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	169	57	52
subtype1	65	25	16
subtype2	66	22	23
subtype3	38	10	13

Figure S117. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.15 (Chi-square test), Q value = 1

Table S127. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	199	14	65
subtype1	73	6	27
subtype2	87	6	18
subtype3	39	2	20

Figure S118. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.695 (Chi-square test), Q value = 1

Table S128. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	148	59	58	15
subtype1	60	22	16	7
subtype2	57	25	26	5
subtype3	31	12	16	3

Figure S119. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.911 (Fisher's exact test), Q value = 1

Table S129. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	19	367
subtype1	7	133
subtype2	9	156
subtype3	3	78

Figure S120. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.238 (ANOVA), Q value = 1

Table S130. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	271	41.4 (27.1)
subtype1	94	38.1 (24.5)
subtype2	124	42.0 (28.6)
subtype3	53	45.8 (27.5)

Figure S121. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.054 (Chi-square test), Q value = 1

Table S131. 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	96	77	62	37
subtype1	35	39	17	11
subtype2	37	26	32	14
subtype3	24	12	13	12

Figure S122. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0643 (ANOVA), Q value = 1

Table S132. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	204	1965.1 (12.5)
subtype1	72	1962.4 (12.8)
subtype2	96	1966.9 (12.7)
subtype3	36	1966.1 (10.8)

Figure S123. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.497 (Chi-square test), Q value = 1

Table S133. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	229	9	4	14
subtype1	83	1	2	5
subtype2	108	5	2	5
subtype3	38	3	0	4

Figure S124. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S134. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	133	208	45

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.652 (logrank test), Q value = 1

Table S135. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	346	83	0.0 - 224.0 (8.0)
subtype1	119	25	0.0 - 104.2 (8.2)
subtype2	186	44	0.0 - 83.8 (8.0)
subtype3	41	14	0.1 - 224.0 (6.2)

Figure S125. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0544 (ANOVA), Q value = 1

Table S136. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	356	65.2 (9.9)
subtype1	121	66.9 (9.4)
subtype2	194	64.2 (10.3)
subtype3	41	64.8 (8.7)

Figure S126. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00639 (Fisher's exact test), Q value = 0.8

Table S137. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	209	177
subtype1	71	62
subtype2	104	104
subtype3	34	11

Figure S127. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

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

P value = 0.823 (ANOVA), Q value = 1

Table S138. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	27	74.1 (32.8)
subtype1	11	71.8 (36.6)
subtype2	14	75.0 (32.5)
subtype3	2	80.0 (28.3)

Figure S128. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.000329 (Chi-square test), Q value = 0.043

Table S139. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	11	78	242	4	17	2	2	2	18	3	4	3
subtype1	4	30	72	2	10	1	0	2	7	0	3	2
subtype2	7	45	138	0	6	1	0	0	7	3	1	0
subtype3	0	3	32	2	1	0	2	0	4	0	0	1

Figure S129. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.625 (Chi-square test), Q value = 1

Table S140. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	79	165	23	16
subtype1	34	55	10	5
subtype2	37	89	12	8
subtype3	8	21	1	3

Figure S130. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.763 (Chi-square test), Q value = 1

Table S141. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	169	57	52
subtype1	62	23	16
subtype2	89	26	29
subtype3	18	8	7

Figure S131. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.834 (Chi-square test), Q value = 1

Table S142. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	199	14	65
subtype1	71	5	27
subtype2	104	7	33
subtype3	24	2	5

Figure S132. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.809 (Chi-square test), Q value = 1

Table S143. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	148	59	58	15
subtype1	58	20	17	7
subtype2	75	32	33	6
subtype3	15	7	8	2

Figure S133. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.586 (Fisher's exact test), Q value = 1

Table S144. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	19	367
subtype1	5	128
subtype2	11	197
subtype3	3	42

Figure S134. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.375 (ANOVA), Q value = 1

Table S145. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	271	41.4 (27.1)
subtype1	88	38.6 (25.0)
subtype2	155	43.4 (29.1)
subtype3	28	39.2 (20.9)

Figure S135. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0546 (Chi-square test), Q value = 1

Table S146. 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	96	77	62	37
subtype1	39	34	16	11
subtype2	49	34	41	18
subtype3	8	9	5	8

Figure S136. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0463 (ANOVA), Q value = 1

Table S147. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	204	1965.1 (12.5)
subtype1	71	1963.1 (11.7)
subtype2	114	1967.0 (13.1)
subtype3	19	1961.6 (10.7)

Figure S137. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.669 (Chi-square test), Q value = 1

Table S148. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	229	9	4	14
subtype1	72	1	2	6
subtype2	129	6	2	6
subtype3	28	2	0	2

Figure S138. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'COMPLETENESS.OF.RESECTION'

Methods & Data

Input

Cluster data file = LUAD-TP.mergedcluster.txt
Clinical data file = LUAD-TP.clin.merged.picked.txt
Number of patients = 393
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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