Correlate_Clinical_vs_Molecular_Signatures

Lung Adenocarcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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): Correlate_Clinical_vs_Molecular_Signatures. Broad Institute of MIT and Harvard. doi:10.7908/C1H41PN8

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 341 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 correlate to 'TOBACCOSMOKINGHISTORYINDICATOR'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'TOBACCOSMOKINGHISTORYINDICATOR'.
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 5 subtypes that correlate to 'Time to Death', 'AGE', 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 correlate to 'TOBACCOSMOKINGHISTORYINDICATOR'.
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, 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)	0.119 (1.00)	0.0209 (1.00)	0.0585 (1.00)	0.087 (1.00)	0.000633 (0.0849)	0.0136 (1.00)	0.274 (1.00)	0.12 (1.00)
AGE	ANOVA	0.477 (1.00)	0.557 (1.00)	0.0138 (1.00)	0.0395 (1.00)	0.0146 (1.00)	0.00448 (0.559)	0.00176 (0.227)	0.156 (1.00)	0.532 (1.00)	0.309 (1.00)
GENDER	Fisher's exact test	0.272 (1.00)	0.383 (1.00)	0.122 (1.00)	0.0767 (1.00)	0.231 (1.00)	0.147 (1.00)	0.0417 (1.00)	0.00131 (0.172)	0.704 (1.00)	0.0492 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA			0.539 (1.00)	0.764 (1.00)	0.0491 (1.00)	0.302 (1.00)	0.431 (1.00)	0.704 (1.00)	0.928 (1.00)	0.782 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.3 (1.00)	0.274 (1.00)	0.378 (1.00)	0.0633 (1.00)	0.166 (1.00)	0.00633 (0.779)	0.0941 (1.00)	0.1 (1.00)	0.0055 (0.682)	0.00335 (0.423)
PATHOLOGY T	Chi-square test	0.489 (1.00)	0.479 (1.00)	0.0968 (1.00)	0.55 (1.00)	0.184 (1.00)	0.658 (1.00)	0.297 (1.00)	0.526 (1.00)	0.413 (1.00)	0.74 (1.00)
PATHOLOGY N	Chi-square test	0.572 (1.00)	0.651 (1.00)	0.785 (1.00)	0.391 (1.00)	0.439 (1.00)	0.481 (1.00)	0.00287 (0.365)	0.0711 (1.00)	0.391 (1.00)	0.679 (1.00)
PATHOLOGICSPREAD(M)	Chi-square test	0.504 (1.00)	1 (1.00)	0.511 (1.00)	0.687 (1.00)	0.851 (1.00)	0.761 (1.00)	0.85 (1.00)	0.791 (1.00)	0.319 (1.00)	0.74 (1.00)
TUMOR STAGE	Chi-square test	0.147 (1.00)	0.274 (1.00)	0.235 (1.00)	0.716 (1.00)	0.903 (1.00)	0.98 (1.00)	0.165 (1.00)	0.14 (1.00)	0.226 (1.00)	0.433 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	1 (1.00)	1 (1.00)	0.74 (1.00)	0.137 (1.00)	0.893 (1.00)	0.373 (1.00)	0.745 (1.00)	0.677 (1.00)	0.655 (1.00)	1 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.448 (1.00)	0.357 (1.00)	0.722 (1.00)	0.374 (1.00)	0.479 (1.00)	0.244 (1.00)	0.0871 (1.00)	0.269 (1.00)	0.226 (1.00)	0.381 (1.00)
STOPPEDSMOKINGYEAR	ANOVA	0.224 (1.00)	0.397 (1.00)	0.406 (1.00)	0.357 (1.00)	0.00862 (1.00)	0.0126 (1.00)	0.0716 (1.00)	0.0194 (1.00)	0.0776 (1.00)	0.174 (1.00)
TOBACCOSMOKINGHISTORYINDICATOR	Chi-square test	0.0492 (1.00)	0.184 (1.00)	0.000723 (0.0961)	0.000775 (0.102)	0.000452 (0.061)	0.000189 (0.0257)	0.00141 (0.183)	1.78e-05 (0.00245)	9.46e-05 (0.013)	0.00225 (0.287)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.616 (1.00)	0.68 (1.00)	0.0766 (1.00)	0.516 (1.00)	0.0307 (1.00)	0.0134 (1.00)	0.309 (1.00)	0.216 (1.00)	0.26 (1.00)	0.209 (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 'STOPPEDSMOKINGYEAR'

P value = 0.224 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	15	1998.4 (10.1)
subtype1	2	2001.5 (12.0)
subtype2	6	2002.5 (10.9)
subtype3	5	1994.2 (10.1)
subtype4	2	1993.5 (4.9)

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

'mRNA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

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

Table S13. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: '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 S12. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

Table S14. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: '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 S13. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

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 'STOPPEDSMOKINGYEAR'

P value = 0.397 (ANOVA), Q value = 1

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	15	1998.4 (10.1)
subtype1	3	1993.3 (3.5)
subtype2	6	1996.7 (10.9)
subtype3	6	2002.7 (11.1)

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

'mRNA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

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

Table S27. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: '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 S25. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

Table S28. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: '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 S26. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

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	101	128	62	46

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

P value = 0.119 (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	301	82	0.0 - 224.0 (9.4)
subtype1	90	26	0.1 - 224.0 (10.7)
subtype2	115	30	0.0 - 104.2 (9.8)
subtype3	53	19	0.1 - 88.1 (8.8)
subtype4	43	7	0.0 - 83.8 (8.0)

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 = 0.0138 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	306	65.2 (9.9)
subtype1	91	63.5 (10.2)
subtype2	115	67.6 (9.1)
subtype3	58	64.0 (10.2)
subtype4	42	64.4 (9.6)

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 = 0.122 (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	186	151
subtype1	63	38
subtype2	73	55
subtype3	28	34
subtype4	22	24

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 = 0.539 (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	28	75.4 (32.8)
subtype1	10	68.0 (37.9)
subtype2	8	90.0 (7.6)
subtype3	6	70.0 (35.2)
subtype4	4	72.5 (48.6)

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 = 0.378 (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
ALL	10	75	205	3	15	2	3	2	13	2	7
subtype1	2	20	64	1	7	1	1	0	4	1	0
subtype2	3	33	68	2	6	1	1	2	6	0	6
subtype3	1	12	45	0	0	0	0	0	2	1	1
subtype4	4	10	28	0	2	0	1	0	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 = 0.0968 (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	98	192	28	17
subtype1	26	63	8	3
subtype2	42	70	8	8
subtype3	10	40	8	3
subtype4	20	19	4	3

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 = 0.785 (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	205	68	55
subtype1	58	21	19
subtype2	81	27	17
subtype3	37	14	10
subtype4	29	6	9

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 = 0.511 (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	M1B	MX
ALL	230	17	2	80
subtype1	68	6	0	22
subtype2	88	4	1	34
subtype3	41	6	0	13
subtype4	33	1	1	11

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 = 0.235 (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	176	74	62	19
subtype1	52	20	19	6
subtype2	72	30	18	6
subtype3	25	18	13	6
subtype4	27	6	12	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 = 0.74 (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	317
subtype1	6	95
subtype2	6	122
subtype3	4	58
subtype4	4	42

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 = 0.722 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	234	40.3 (27.1)
subtype1	57	41.3 (24.4)
subtype2	90	37.9 (25.7)
subtype3	50	41.0 (28.3)
subtype4	37	43.5 (32.9)

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 'STOPPEDSMOKINGYEAR'

P value = 0.406 (ANOVA), Q value = 1

Table S41. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	168	1994.2 (13.8)
subtype1	48	1993.9 (14.3)
subtype2	68	1992.5 (13.3)
subtype3	25	1996.8 (14.9)
subtype4	27	1996.7 (12.8)

Figure S38. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

'Copy Number Ratio CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000723 (Chi-square test), Q value = 0.096

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	114	86	76	48
subtype1	33	24	19	21
subtype2	40	43	20	20
subtype3	21	9	23	7
subtype4	20	10	14	0

Figure S39. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0766 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	169	1965.0 (13.0)
subtype1	40	1962.3 (10.7)
subtype2	63	1963.8 (13.5)
subtype3	34	1969.6 (14.6)
subtype4	32	1965.9 (11.9)

Figure S40. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	67	96	98

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0209 (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	231	61	0.0 - 224.0 (8.5)
subtype1	58	12	0.1 - 224.0 (9.0)
subtype2	86	18	0.0 - 88.1 (8.2)
subtype3	87	31	0.0 - 77.9 (9.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0395 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	233	65.2 (10.0)
subtype1	58	67.8 (8.2)
subtype2	90	63.5 (9.9)
subtype3	85	65.1 (10.8)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0767 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	143	118
subtype1	44	23
subtype2	46	50
subtype3	53	45

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

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

P value = 0.764 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	21	73.8 (32.5)
subtype1	8	70.0 (43.4)
subtype2	9	80.0 (11.2)
subtype3	4	67.5 (45.7)

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 = 0.0633 (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
ALL	10	60	150	3	14	1	2	2	12	2	5
subtype1	5	15	30	1	7	0	1	2	4	0	2
subtype2	4	28	55	1	3	0	1	0	4	0	0
subtype3	1	17	65	1	4	1	0	0	4	2	3

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.55 (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	78	146	20	14
subtype1	22	37	5	2
subtype2	26	52	10	8
subtype3	30	57	5	4

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.391 (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	157	52	44
subtype1	44	12	8
subtype2	61	18	15
subtype3	52	22	21

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.687 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	168	11	75
subtype1	42	1	20
subtype2	61	6	26
subtype3	65	4	29

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.716 (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	134	58	51	12
subtype1	38	15	10	1
subtype2	48	21	19	6
subtype3	48	22	22	5

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 = 0.137 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	16	245
subtype1	2	65
subtype2	4	92
subtype3	10	88

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.374 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	178	38.6 (25.8)
subtype1	39	33.8 (20.4)
subtype2	73	41.0 (30.1)
subtype3	66	38.7 (23.4)

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

'METHLYATION CNMF' versus 'STOPPEDSMOKINGYEAR'

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

Table S56. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	136	1994.1 (13.3)
subtype1	40	1991.8 (10.7)
subtype2	55	1995.8 (14.4)
subtype3	41	1994.1 (14.2)

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

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000775 (Chi-square test), Q value = 0.1

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	87	68	56	37
subtype1	18	25	5	17
subtype2	37	21	29	8
subtype3	32	22	22	12

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

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.516 (ANOVA), Q value = 1

Table S58. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	133	1964.7 (12.7)
subtype1	30	1962.5 (11.5)
subtype2	61	1964.9 (13.6)
subtype3	42	1966.0 (12.4)

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

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	90	75	71

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0585 (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	213	66	0.0 - 224.0 (12.3)
subtype1	75	27	0.0 - 163.1 (12.1)
subtype2	71	17	0.2 - 224.0 (13.7)
subtype3	67	22	0.0 - 83.8 (9.0)

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 = 0.0146 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	215	64.8 (9.9)
subtype1	78	64.2 (10.2)
subtype2	72	67.4 (9.0)
subtype3	65	62.7 (9.9)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.231 (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	105
subtype1	56	34
subtype2	40	35
subtype3	35	36

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 = 0.0491 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	20	68.0 (36.1)
subtype1	7	44.3 (42.8)
subtype2	6	91.7 (7.5)
subtype3	7	71.4 (31.8)

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 = 0.166 (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	150	3	10	1	2	2	6	1	3
subtype1	2	19	58	1	3	1	1	1	2	1	1
subtype2	3	21	36	2	6	0	1	0	4	0	2
subtype3	1	12	56	0	1	0	0	1	0	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 = 0.184 (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	64	141	17	13
subtype1	26	50	9	4
subtype2	23	43	2	7
subtype3	15	48	6	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 = 0.439 (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	140	46	44
subtype1	48	22	16
subtype2	49	13	12
subtype3	43	11	16

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 = 0.851 (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	165	12	55
subtype1	63	5	19
subtype2	50	4	21
subtype3	52	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 = 0.903 (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	122	49	49	13
subtype1	45	20	17	5
subtype2	39	17	14	5
subtype3	38	12	18	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 = 0.893 (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	221
subtype1	6	84
subtype2	4	71
subtype3	5	66

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 = 0.479 (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	55	42.7 (28.8)
subtype2	64	37.3 (25.3)
subtype3	52	42.2 (26.2)

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

'RPPA CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.00862 (ANOVA), Q value = 1

Table S71. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	122	1994.3 (13.5)
subtype1	45	1990.2 (14.9)
subtype2	43	1994.3 (12.4)
subtype3	34	1999.6 (11.2)

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

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000452 (Chi-square test), Q value = 0.061

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	84	59	52	31
subtype1	29	25	12	19
subtype2	25	26	16	6
subtype3	30	8	24	6

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

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0307 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	119	1964.7 (13.7)
subtype1	33	1964.2 (14.3)
subtype2	46	1961.4 (12.8)
subtype3	40	1969.1 (13.2)

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

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	73	84	56

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.087 (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	213	66	0.0 - 224.0 (12.3)
subtype1	18	4	0.0 - 63.7 (7.1)
subtype2	69	22	0.0 - 83.8 (8.8)
subtype3	72	27	0.7 - 163.1 (13.5)
subtype4	54	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 = 0.00448 (ANOVA), Q value = 0.56

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

	nPatients	Mean (Std.Dev)
ALL	215	64.8 (9.9)
subtype1	20	68.0 (6.8)
subtype2	67	62.7 (11.0)
subtype3	75	63.5 (9.8)
subtype4	53	68.2 (8.2)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.147 (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	105
subtype1	15	8
subtype2	37	36
subtype3	53	31
subtype4	26	30

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 = 0.302 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	20	68.0 (36.1)
subtype1	4	70.0 (46.9)
subtype2	7	71.4 (31.8)
subtype3	5	44.0 (41.6)
subtype4	4	90.0 (8.2)

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 = 0.00633 (Chi-square test), Q value = 0.78

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	150	3	10	1	2	2	6	1	3
subtype1	1	4	12	0	3	0	2	0	1	0	0
subtype2	2	12	54	0	2	1	0	1	1	0	0
subtype3	1	19	58	0	2	0	0	0	2	1	1
subtype4	2	17	26	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 = 0.658 (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	64	141	17	13
subtype1	7	11	2	3
subtype2	19	44	7	3
subtype3	21	55	3	4
subtype4	17	31	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 = 0.481 (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	140	46	44
subtype1	15	4	4
subtype2	45	10	17
subtype3	43	21	15
subtype4	37	11	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 = 0.761 (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	165	12	55
subtype1	14	1	8
subtype2	53	2	17
subtype3	58	6	18
subtype4	40	3	12

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 = 0.98 (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	122	49	49	13
subtype1	12	5	5	1
subtype2	37	14	19	3
subtype3	44	18	14	6
subtype4	29	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 = 0.373 (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	221
subtype1	0	23
subtype2	7	66
subtype3	6	78
subtype4	2	54

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 = 0.244 (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	57	43.9 (27.2)
subtype3	47	38.4 (25.4)
subtype4	50	36.0 (23.8)

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

'RPPA cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.0126 (ANOVA), Q value = 1

Table S86. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	122	1994.3 (13.5)
subtype1	16	1994.6 (11.5)
subtype2	32	2000.8 (10.7)
subtype3	39	1991.3 (15.3)
subtype4	35	1991.7 (13.1)

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

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000189 (Chi-square test), Q value = 0.026

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	84	59	52	31
subtype1	13	5	0	4
subtype2	30	8	25	7
subtype3	25	23	15	16
subtype4	16	23	12	4

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

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0134 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	119	1964.7 (13.7)
subtype1	11	1962.1 (10.6)
subtype2	43	1969.5 (13.7)
subtype3	29	1964.8 (12.4)
subtype4	36	1959.8 (14.0)

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

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
Number of samples	94	67	49	74	40

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.000633 (logrank test), Q value = 0.085

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	288	80	0.0 - 224.0 (10.1)
subtype1	85	23	0.0 - 163.1 (8.1)
subtype2	59	13	0.1 - 104.2 (10.8)
subtype3	44	20	0.0 - 47.6 (9.5)
subtype4	64	17	0.1 - 88.1 (15.7)
subtype5	36	7	0.1 - 224.0 (12.1)

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 = 0.00176 (ANOVA), Q value = 0.23

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

	nPatients	Mean (Std.Dev)
ALL	293	65.4 (9.8)
subtype1	85	63.4 (10.7)
subtype2	61	68.3 (7.7)
subtype3	44	68.8 (9.4)
subtype4	67	63.4 (9.3)
subtype5	36	64.9 (10.3)

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

'RNAseq CNMF subtypes' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	177	147
subtype1	52	42
subtype2	44	23
subtype3	22	27
subtype4	33	41
subtype5	26	14

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 = 0.431 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	28	75.4 (32.8)
subtype1	10	67.0 (37.4)
subtype2	7	92.9 (7.6)
subtype4	7	74.3 (33.6)
subtype5	4	67.5 (45.7)

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 = 0.0941 (Chi-square test), Q value = 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
ALL	10	74	197	3	14	2	3	2	13	2	4
subtype1	1	15	68	0	3	1	2	0	4	0	0
subtype2	4	18	35	1	6	0	1	0	2	0	0
subtype3	1	12	28	1	0	1	0	1	2	0	3
subtype4	2	17	46	0	4	0	0	0	3	2	0
subtype5	2	12	20	1	1	0	0	1	2	0	1

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 = 0.297 (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	94	185	26	17
subtype1	24	58	7	5
subtype2	28	33	3	3
subtype3	8	30	6	4
subtype4	19	45	6	4
subtype5	15	19	4	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 = 0.00287 (Chi-square test), Q value = 0.37

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

nPatients	N0	N1	N2+N3
ALL	197	65	53
subtype1	50	24	19
subtype2	48	11	7
subtype3	20	14	14
subtype4	57	8	8
subtype5	22	8	5

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 = 0.85 (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	225	17	74
subtype1	66	4	22
subtype2	48	2	15
subtype3	38	3	8
subtype4	49	5	18
subtype5	24	3	11

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 = 0.165 (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	169	71	61	17
subtype1	42	25	20	5
subtype2	41	13	8	2
subtype3	18	12	16	3
subtype4	45	13	12	4
subtype5	23	8	5	3

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 = 0.745 (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	18	306
subtype1	6	88
subtype2	4	63
subtype3	4	45
subtype4	2	72
subtype5	2	38

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 = 0.0871 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	227	39.7 (26.2)
subtype1	64	42.7 (24.3)
subtype2	45	30.2 (21.4)
subtype3	37	39.5 (27.1)
subtype4	55	43.8 (30.7)
subtype5	26	40.5 (24.5)

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

'RNAseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.0716 (ANOVA), Q value = 1

Table S101. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	165	1993.9 (13.7)
subtype1	47	1995.7 (13.3)
subtype2	39	1989.7 (14.2)
subtype3	24	1991.4 (15.8)
subtype4	33	1998.2 (12.3)
subtype5	22	1993.4 (11.6)

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

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.00141 (Chi-square test), Q value = 0.18

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	110	87	71	45
subtype1	35	22	19	16
subtype2	16	30	7	11
subtype3	19	11	11	5
subtype4	27	11	28	7
subtype5	13	13	6	6

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

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.309 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	163	1964.7 (13.0)
subtype1	43	1967.2 (14.0)
subtype2	34	1960.8 (11.1)
subtype3	25	1964.1 (14.8)
subtype4	42	1965.5 (12.9)
subtype5	19	1965.3 (11.2)

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

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	139	97	88

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0136 (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	288	80	0.0 - 224.0 (10.1)
subtype1	123	29	0.0 - 224.0 (13.2)
subtype2	87	28	0.1 - 83.8 (11.6)
subtype3	78	23	0.0 - 77.9 (8.2)

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 = 0.156 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	293	65.4 (9.8)
subtype1	126	66.7 (9.1)
subtype2	88	64.4 (9.6)
subtype3	79	64.6 (11.1)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00131 (Fisher's exact test), Q value = 0.17

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

nPatients	FEMALE	MALE
ALL	177	147
subtype1	85	54
subtype2	38	59
subtype3	54	34

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 = 0.704 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	28	75.4 (32.8)
subtype1	12	76.7 (36.5)
subtype2	6	83.3 (5.2)
subtype3	10	69.0 (38.7)

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 = 0.1 (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
ALL	10	74	197	3	14	2	3	2	13	2	4
subtype1	7	36	73	3	9	0	1	2	6	0	2
subtype2	2	23	62	0	2	1	0	0	3	2	2
subtype3	1	15	62	0	3	1	2	0	4	0	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 = 0.526 (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	94	185	26	17
subtype1	49	72	11	6
subtype2	23	60	7	6
subtype3	22	53	8	5

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 = 0.0711 (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	197	65	53
subtype1	92	28	14
subtype2	58	16	21
subtype3	47	21	18

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 = 0.791 (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	225	17	74
subtype1	94	7	33
subtype2	66	7	22
subtype3	65	3	19

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 = 0.14 (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	169	71	61	17
subtype1	81	30	16	7
subtype2	48	19	24	6
subtype3	40	22	21	4

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 = 0.677 (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	18	306
subtype1	6	133
subtype2	6	91
subtype3	6	82

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 = 0.269 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	227	39.7 (26.2)
subtype1	93	36.4 (26.7)
subtype2	75	42.7 (27.7)
subtype3	59	41.2 (23.1)

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

'RNAseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.0194 (ANOVA), Q value = 1

Table S116. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	165	1993.9 (13.7)
subtype1	79	1990.9 (14.1)
subtype2	45	1997.7 (12.2)
subtype3	41	1995.4 (13.5)

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

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 1.78e-05 (Chi-square test), Q value = 0.0025

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	110	87	71	45
subtype1	43	52	16	22
subtype2	39	15	34	7
subtype3	28	20	21	16

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

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.216 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	163	1964.7 (13.0)
subtype1	67	1962.6 (12.7)
subtype2	55	1966.4 (12.8)
subtype3	41	1966.0 (13.6)

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

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	137	148	49

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.274 (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	298	80	0.0 - 224.0 (9.3)
subtype1	123	29	0.1 - 163.1 (9.4)
subtype2	129	37	0.0 - 83.8 (9.8)
subtype3	46	14	0.0 - 224.0 (8.4)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.532 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	304	65.2 (9.9)
subtype1	127	65.9 (9.9)
subtype2	136	64.5 (10.1)
subtype3	41	65.5 (9.2)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.704 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	184	150
subtype1	77	60
subtype2	78	70
subtype3	29	20

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

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

P value = 0.928 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	26	73.5 (33.3)
subtype1	11	73.6 (37.5)
subtype2	13	72.3 (32.7)
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 = 0.0055 (Chi-square test), Q value = 0.68

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
ALL	10	74	204	3	15	2	2	2	13	2	7
subtype1	5	32	72	2	10	0	2	2	8	0	4
subtype2	5	38	96	0	3	2	0	0	2	2	0
subtype3	0	4	36	1	2	0	0	0	3	0	3

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

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.413 (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	97	188	29	17
subtype1	49	69	11	7
subtype2	35	89	15	8
subtype3	13	30	3	2

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

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.391 (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	202	67	55
subtype1	85	27	19
subtype2	93	27	25
subtype3	24	13	11

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

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.319 (Chi-square test), Q value = 1

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

nPatients	M0	M1	M1B	MX
ALL	227	15	2	82
subtype1	89	6	1	37
subtype2	109	8	1	28
subtype3	29	1	0	17

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

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.226 (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	175	72	63	18
subtype1	77	29	18	8
subtype2	79	30	31	8
subtype3	19	13	14	2

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 = 0.655 (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	315
subtype1	10	127
subtype2	7	141
subtype3	2	47

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

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.226 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	231	40.4 (27.2)
subtype1	92	36.9 (26.9)
subtype2	110	43.5 (28.1)
subtype3	29	39.9 (24.1)

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

'MIRSEQ CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.0776 (ANOVA), Q value = 1

Table S131. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	164	1994.0 (13.8)
subtype1	81	1991.5 (14.1)
subtype2	65	1996.6 (13.2)
subtype3	18	1995.7 (13.3)

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

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 9.46e-05 (Chi-square test), Q value = 0.013

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	110	86	76	49
subtype1	43	50	20	17
subtype2	51	28	47	18
subtype3	16	8	9	14

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

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.26 (ANOVA), Q value = 1

Table S133. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	167	1964.7 (13.0)
subtype1	71	1963.3 (13.7)
subtype2	79	1966.5 (12.5)
subtype3	17	1962.8 (11.2)

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

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	141	95	98

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.12 (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	298	80	0.0 - 224.0 (9.3)
subtype1	127	31	0.1 - 163.1 (8.5)
subtype2	86	30	0.0 - 224.0 (8.6)
subtype3	85	19	0.0 - 83.8 (12.7)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.309 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	304	65.2 (9.9)
subtype1	130	66.1 (9.8)
subtype2	86	64.0 (9.9)
subtype3	88	65.2 (10.1)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0492 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	184	150
subtype1	82	59
subtype2	58	37
subtype3	44	54

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

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

P value = 0.782 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	26	73.5 (33.3)
subtype1	11	70.0 (36.6)
subtype2	9	80.0 (30.8)
subtype3	6	70.0 (35.2)

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 = 0.00335 (Chi-square test), Q value = 0.42

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
ALL	10	74	204	3	15	2	2	2	13	2	7
subtype1	6	31	73	2	10	0	2	2	9	0	6
subtype2	2	15	70	1	3	0	0	0	4	0	0
subtype3	2	28	61	0	2	2	0	0	0	2	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 = 0.74 (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	97	188	29	17
subtype1	45	74	12	8
subtype2	21	59	9	5
subtype3	31	55	8	4

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.679 (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	202	67	55
subtype1	86	30	18
subtype2	56	19	19
subtype3	60	18	18

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.74 (Chi-square test), Q value = 1

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

nPatients	M0	M1	M1B	MX
ALL	227	15	2	82
subtype1	91	6	1	39
subtype2	65	6	0	21
subtype3	71	3	1	22

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

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.433 (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	175	72	63	18
subtype1	76	32	19	9
subtype2	47	18	23	6
subtype3	52	22	21	3

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 = 1 (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	315
subtype1	8	133
subtype2	5	90
subtype3	6	92

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.381 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	231	40.4 (27.2)
subtype1	92	37.5 (25.2)
subtype2	65	43.5 (24.2)
subtype3	74	41.3 (31.6)

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

'MIRSEQ CHIERARCHICAL' versus 'STOPPEDSMOKINGYEAR'

P value = 0.174 (ANOVA), Q value = 1

Table S146. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	164	1994.0 (13.8)
subtype1	81	1992.0 (13.6)
subtype2	39	1994.9 (14.8)
subtype3	44	1996.7 (12.9)

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

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.00225 (Chi-square test), Q value = 0.29

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	110	86	76	49
subtype1	46	48	19	20
subtype2	30	19	24	19
subtype3	34	19	33	10

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

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.209 (ANOVA), Q value = 1

Table S148. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	167	1964.7 (13.0)
subtype1	72	1962.9 (12.7)
subtype2	37	1965.0 (13.2)
subtype3	58	1966.9 (13.0)

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

Methods & Data

Input

Cluster data file = LUAD-TP.mergedcluster.txt
Clinical data file = LUAD-TP.clin.merged.picked.txt
Number of patients = 341
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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