Lung Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

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 8 different clustering approaches and 15 clinical features across 223 patients, 19 significant findings detected with P value < 0.05.

CNMF clustering analysis on array-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'STOPPEDSMOKINGYEAR', and 'TOBACCOSMOKINGHISTORYINDICATOR'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'GENDER', and 'NUMBERPACKYEARSSMOKED'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', and 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'KARNOFSKY.PERFORMANCE.SCORE' and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'PATHOLOGICSPREAD(M)'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 15 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 19 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	CN CNMF	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRseq CNMF subtypes	MIRseq cHierClus subtypes
Time to Death	logrank test	0.678	0.145	0.19	0.011	0.00544	0.000721	0.0745	0.0225
AGE	ANOVA	0.623	0.394	0.0298	0.191	0.0343	0.222	0.348	0.152
GENDER	Fisher's exact test	0.0995	0.352	0.462	0.0129	5.47e-06	0.000214	0.659	0.0696
KARNOFSKY PERFORMANCE SCORE	ANOVA			0.0458	0.159	0.0171	0.065	0.0336	0.596
HISTOLOGICAL TYPE	Chi-square test	1	0.439	0.529	0.421	0.00697	0.00148	0.0143	0.122
PATHOLOGY T	Chi-square test	0.64	0.916	0.287	0.479	0.0763	0.463	0.601	0.34
PATHOLOGY N	Chi-square test	0.632	0.613	0.839	0.666	0.248	0.32	0.85	0.876
PATHOLOGICSPREAD(M)	Chi-square test	1	0.439	0.986	0.475	0.321	0.556	0.119	0.0366
TUMOR STAGE	Chi-square test	0.174	0.301	0.728	0.865	0.213	0.685	0.854	0.683
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	1	0.439	0.656	0.606	0.84	0.933	0.789	0.443
NEOADJUVANT THERAPY	Fisher's exact test	1	0.916	0.337	0.504	0.103	0.0574	0.0568	0.872
NUMBERPACKYEARSSMOKED	ANOVA	0.379	0.199	0.104	0.0483	0.113	0.81	0.739	0.523
STOPPEDSMOKINGYEAR	ANOVA	0.913		0.0386	0.352	0.81	0.178	0.347	0.485
TOBACCOSMOKINGHISTORYINDICATOR	Chi-square test	0.234	0.246	0.0144	0.169	0.219	0.319	0.249	0.204
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.742	0.892	0.952	0.299	0.504	0.521	0.197	0.351

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2
Number of samples	11	12

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.678 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	23	3	0.5 - 56.8 (15.8)
subtype1	11	2	2.0 - 56.8 (23.1)
subtype2	12	1	0.5 - 37.0 (4.0)

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 = 0.623 (t-test)

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

	nPatients	Mean (Std.Dev)
ALL	22	66.2 (9.8)
subtype1	10	65.1 (8.2)
subtype2	12	67.2 (11.2)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0995 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	13	10
subtype1	4	7
subtype2	9	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 = 1 (Fisher's exact test)

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)
ALL	1	22
subtype1	0	11
subtype2	1	11

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 = 0.64 (Fisher's exact test)

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

nPatients	T1	T2
ALL	6	17
subtype1	2	9
subtype2	4	8

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 = 0.632 (Chi-square test)

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

nPatients	N0	N1	N2+N3
ALL	16	3	3
subtype1	7	2	2
subtype2	9	1	1

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 = 1 (Fisher's exact test)

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

nPatients	M0	M1
ALL	22	1
subtype1	11	0
subtype2	11	1

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 = 0.174 (Chi-square test)

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

nPatients	I	II	III	IV
ALL	16	4	2	1
subtype1	6	3	2	0
subtype2	10	1	0	1

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)

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

nPatients	NO	YES
ALL	1	22
subtype1	0	11
subtype2	1	11

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

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

nPatients	NO	YES
ALL	6	17
subtype1	3	8
subtype2	3	9

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

'mRNA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.379 (t-test)

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

	nPatients	Mean (Std.Dev)
ALL	13	41.0 (14.7)
subtype1	7	44.4 (16.1)
subtype2	6	37.0 (13.1)

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

'mRNA CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.913 (t-test)

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

	nPatients	Mean (Std.Dev)
ALL	9	1994.6 (9.7)
subtype1	4	1995.0 (10.7)
subtype2	5	1994.2 (10.1)

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

'mRNA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.234 (Chi-square test)

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	5	8	5	5
subtype1	3	3	4	1
subtype2	2	5	1	4

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

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.742 (t-test)

Table S15. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	13	1967.0 (11.5)
subtype1	7	1968.0 (14.4)
subtype2	6	1965.8 (8.2)

Figure S14. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5
Number of samples	3	5	5	4	6

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.145 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	23	3	0.5 - 56.8 (15.8)
subtype1	3	0	2.0 - 19.1 (17.4)
subtype2	5	0	6.2 - 56.8 (15.8)
subtype3	5	1	0.5 - 37.0 (3.2)
subtype4	4	0	0.6 - 13.2 (4.0)
subtype5	6	2	2.0 - 45.2 (30.9)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.394 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	22	66.2 (9.8)
subtype1	3	69.3 (13.1)
subtype2	5	63.2 (6.6)
subtype3	5	60.2 (11.2)
subtype4	4	71.2 (9.2)
subtype5	5	69.4 (9.0)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.352 (Chi-square test)

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

nPatients	FEMALE	MALE
ALL	13	10
subtype1	2	1
subtype2	2	3
subtype3	2	3
subtype4	4	0
subtype5	3	3

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.439 (Chi-square test)

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	1	22
subtype1	0	3
subtype2	0	5
subtype3	1	4
subtype4	0	4
subtype5	0	6

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.916 (Chi-square test)

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2
ALL	6	17
subtype1	1	2
subtype2	2	3
subtype3	1	4
subtype4	1	3
subtype5	1	5

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.613 (Chi-square test)

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	16	3	3
subtype1	2	1	0
subtype2	4	1	0
subtype3	4	0	1
subtype4	3	0	0
subtype5	3	1	2

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.439 (Chi-square test)

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	22	1
subtype1	3	0
subtype2	5	0
subtype3	4	1
subtype4	4	0
subtype5	6	0

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.301 (Chi-square test)

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	16	4	2	1
subtype1	2	1	0	0
subtype2	4	1	0	0
subtype3	4	0	0	1
subtype4	4	0	0	0
subtype5	2	2	2	0

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.439 (Chi-square test)

Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	22
subtype1	0	3
subtype2	0	5
subtype3	1	4
subtype4	0	4
subtype5	0	6

Figure S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.916 (Chi-square test)

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	6	17
subtype1	1	2
subtype2	2	3
subtype3	1	4
subtype4	1	3
subtype5	1	5

Figure S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

'mRNA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.199 (ANOVA)

Table S27. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	13	41.0 (14.7)
subtype1	2	40.0 (24.0)
subtype2	3	56.0 (4.0)
subtype3	3	36.7 (11.5)
subtype4	2	27.5 (3.5)
subtype5	3	40.0 (17.3)

Figure S25. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

'mRNA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.246 (Chi-square test)

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	5	8	5	5
subtype1	2	1	0	0
subtype2	0	2	1	2
subtype3	0	3	1	1
subtype4	1	1	0	2
subtype5	2	1	3	0

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

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.892 (ANOVA)

Table S29. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	13	1967.0 (11.5)
subtype1	2	1979.5 (20.5)
subtype2	1	1956.0 (NA)
subtype3	3	1966.3 (11.2)
subtype4	2	1965.5 (9.2)
subtype5	5	1965.2 (9.8)

Figure S27. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #3: 'CN CNMF'

Table S30. Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels	1	2	3
Number of samples	97	50	76

'CN CNMF' versus 'Time to Death'

P value = 0.19 (logrank test)

Table S31. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	195	62	0.0 - 224.0 (13.9)
subtype1	86	23	0.1 - 104.2 (13.1)
subtype2	42	16	0.1 - 88.1 (14.9)
subtype3	67	23	0.0 - 224.0 (15.8)

Figure S28. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.0298 (ANOVA)

Table S32. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	199	65.9 (9.7)
subtype1	85	68.0 (8.3)
subtype2	45	64.9 (10.1)
subtype3	69	64.1 (10.5)

Figure S29. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.462 (Fisher's exact test)

Table S33. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	125	98
subtype1	52	45
subtype2	26	24
subtype3	47	29

Figure S30. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0458 (ANOVA)

Table S34. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	17	67.1 (39.8)
subtype1	8	91.2 (6.4)
subtype2	2	40.0 (56.6)
subtype3	7	47.1 (46.4)

Figure S31. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.529 (Chi-square test)

Table S35. Clustering Approach #3: 'CN 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 MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA
ALL	5	54	136	3	10	2	1	8	4
subtype1	3	27	52	2	4	1	1	3	4
subtype2	2	11	34	0	1	0	0	2	0
subtype3	0	16	50	1	5	1	0	3	0

Figure S32. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.287 (Chi-square test)

Table S36. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	60	130	17	15
subtype1	26	56	7	7
subtype2	14	24	7	5
subtype3	20	50	3	3

Figure S33. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.839 (Chi-square test)

Table S37. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	132	43	42
subtype1	59	17	19
subtype2	32	9	8
subtype3	41	17	15

Figure S34. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.986 (Chi-square test)

Table S38. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	159	8	51
subtype1	69	3	24
subtype2	37	2	11
subtype3	53	3	16

Figure S35. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.728 (Chi-square test)

Table S39. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	115	47	47	9
subtype1	55	17	21	3
subtype2	22	14	12	2
subtype3	38	16	14	4

Figure S36. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.656 (Fisher's exact test)

Table S40. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	15	208
subtype1	5	92
subtype2	4	46
subtype3	6	70

Figure S37. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.337 (Fisher's exact test)

Table S41. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	36	187
subtype1	16	81
subtype2	5	45
subtype3	15	61

Figure S38. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

'CN CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.104 (ANOVA)

Table S42. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	155	40.0 (26.7)
subtype1	70	35.7 (26.7)
subtype2	38	47.1 (29.8)
subtype3	47	40.8 (22.9)

Figure S39. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

'CN CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.0386 (ANOVA)

Table S43. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	110	1991.8 (14.2)
subtype1	52	1988.9 (14.9)
subtype2	22	1998.0 (9.0)
subtype3	36	1992.0 (14.8)

Figure S40. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

'CN CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0144 (Chi-square test)

Table S44. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	69	67	45	32
subtype1	28	38	13	14
subtype2	17	8	17	4
subtype3	24	21	15	14

Figure S41. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

'CN CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.952 (ANOVA)

Table S45. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	105	1962.0 (12.8)
subtype1	47	1961.6 (14.3)
subtype2	27	1962.3 (10.9)
subtype3	31	1962.4 (12.2)

Figure S42. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3	4
Number of samples	41	45	45	43

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.011 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	150	47	0.0 - 224.0 (11.9)
subtype1	36	7	0.1 - 224.0 (9.8)
subtype2	38	19	0.1 - 88.1 (17.5)
subtype3	40	10	0.0 - 77.9 (5.9)
subtype4	36	11	0.8 - 55.4 (12.8)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.191 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	151	66.1 (9.8)
subtype1	34	69.2 (6.9)
subtype2	40	65.3 (9.5)
subtype3	42	65.8 (10.6)
subtype4	35	64.4 (11.2)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0129 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	99	75
subtype1	30	11
subtype2	19	26
subtype3	22	23
subtype4	28	15

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

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

P value = 0.159 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	14	67.9 (38.5)
subtype1	6	93.3 (5.2)
subtype2	2	40.0 (56.6)
subtype3	4	77.5 (17.1)
subtype4	2	0.0 (0.0)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.421 (Chi-square test)

Table S51. 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 MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA
ALL	5	41	100	3	10	2	1	8	4
subtype1	4	8	21	1	4	1	1	0	1
subtype2	0	12	26	0	2	0	0	3	2
subtype3	0	13	26	1	1	1	0	3	0
subtype4	1	8	27	1	3	0	0	2	1

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.479 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	50	97	13	13
subtype1	16	20	2	2
subtype2	10	26	3	6
subtype3	11	27	3	4
subtype4	13	24	5	1

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.666 (Chi-square test)

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

nPatients	N0	N1	N2+N3
ALL	105	32	33
subtype1	27	7	6
subtype2	28	10	6
subtype3	27	8	9
subtype4	23	7	12

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.475 (Chi-square test)

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

nPatients	M0	M1	MX
ALL	112	6	51
subtype1	25	0	13
subtype2	33	3	9
subtype3	27	2	14
subtype4	27	1	15

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.865 (Chi-square test)

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

nPatients	I	II	III	IV
ALL	89	36	37	7
subtype1	23	8	8	0
subtype2	24	9	9	3
subtype3	22	9	9	3
subtype4	20	10	11	1

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

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

P value = 0.606 (Fisher's exact test)

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

nPatients	NO	YES
ALL	12	162
subtype1	2	39
subtype2	3	42
subtype3	2	43
subtype4	5	38

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.504 (Fisher's exact test)

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

nPatients	NO	YES
ALL	27	147
subtype1	8	33
subtype2	4	41
subtype3	8	37
subtype4	7	36

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0483 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	120	38.3 (25.8)
subtype1	26	30.3 (18.7)
subtype2	33	48.0 (30.6)
subtype3	30	38.4 (28.9)
subtype4	31	34.7 (19.1)

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

'METHLYATION CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.352 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	93	1992.5 (14.0)
subtype1	24	1990.2 (13.2)
subtype2	27	1995.3 (11.5)
subtype3	22	1989.4 (17.1)
subtype4	20	1994.8 (14.0)

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

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.169 (Chi-square test)

Table S60. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	57	52	32	23
subtype1	10	15	3	10
subtype2	18	12	8	4
subtype3	13	14	12	5
subtype4	16	11	9	4

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

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.299 (ANOVA)

Table S61. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	84	1962.0 (12.4)
subtype1	17	1960.0 (11.2)
subtype2	26	1959.9 (14.3)
subtype3	23	1962.3 (12.1)
subtype4	18	1966.6 (10.2)

Figure S57. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S62. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	65	79	79

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00544 (logrank test)

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	195	62	0.0 - 224.0 (13.9)
subtype1	56	12	0.1 - 224.0 (10.4)
subtype2	68	27	0.0 - 77.9 (12.8)
subtype3	71	23	0.1 - 88.1 (17.5)

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0343 (ANOVA)

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	199	65.9 (9.7)
subtype1	56	68.0 (8.6)
subtype2	70	63.7 (10.5)
subtype3	73	66.5 (9.2)

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 5.47e-06 (Fisher's exact test)

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	125	98
subtype1	50	15
subtype2	46	33
subtype3	29	50

Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0171 (ANOVA)

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	17	67.1 (39.8)
subtype1	10	84.0 (30.3)
subtype2	5	26.0 (35.8)
subtype3	2	85.0 (7.1)

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00697 (Chi-square test)

Table S67. Clustering Approach #5: '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 MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA
ALL	5	54	136	3	10	2	1	8	4
subtype1	3	11	41	2	6	1	0	1	0
subtype2	1	14	57	0	3	1	0	3	0
subtype3	1	29	38	1	1	0	1	4	4

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0763 (Chi-square test)

Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	60	130	17	15
subtype1	27	31	4	2
subtype2	17	49	6	7
subtype3	16	50	7	6

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.248 (Chi-square test)

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	132	43	42
subtype1	42	10	10
subtype2	39	20	18
subtype3	51	13	14

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.321 (Chi-square test)

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	159	8	51
subtype1	43	1	18
subtype2	53	4	20
subtype3	63	3	13

Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.213 (Chi-square test)

Table S71. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	115	47	47	9
subtype1	40	11	10	1
subtype2	32	19	21	5
subtype3	43	17	16	3

Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.84 (Fisher's exact test)

Table S72. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	15	208
subtype1	5	60
subtype2	6	73
subtype3	4	75

Figure S67. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.103 (Fisher's exact test)

Table S73. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	36	187
subtype1	10	55
subtype2	18	61
subtype3	8	71

Figure S68. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.113 (ANOVA)

Table S74. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	155	40.0 (26.7)
subtype1	40	32.6 (22.0)
subtype2	54	41.4 (24.7)
subtype3	61	43.7 (30.3)

Figure S69. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.81 (ANOVA)

Table S75. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	110	1991.8 (14.2)
subtype1	35	1990.7 (14.4)
subtype2	31	1993.0 (14.7)
subtype3	44	1991.7 (13.8)

Figure S70. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.219 (Chi-square test)

Table S76. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	69	67	45	32
subtype1	16	24	9	12
subtype2	24	19	19	13
subtype3	29	24	17	7

Figure S71. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.504 (ANOVA)

Table S77. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	105	1962.0 (12.8)
subtype1	29	1963.0 (11.1)
subtype2	33	1963.4 (13.7)
subtype3	43	1960.2 (13.2)

Figure S72. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S78. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	38	51	134

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000721 (logrank test)

Table S79. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	195	62	0.0 - 224.0 (13.9)
subtype1	33	14	0.1 - 48.5 (11.6)
subtype2	47	11	0.2 - 104.2 (23.4)
subtype3	115	37	0.0 - 224.0 (12.3)

Figure S73. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.222 (ANOVA)

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	199	65.9 (9.7)
subtype1	36	68.4 (9.7)
subtype2	47	65.0 (8.5)
subtype3	116	65.6 (10.0)

Figure S74. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.000214 (Fisher's exact test)

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	125	98
subtype1	15	23
subtype2	20	31
subtype3	90	44

Figure S75. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.065 (ANOVA)

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	17	67.1 (39.8)
subtype2	3	86.7 (5.8)
subtype3	14	62.9 (42.9)

Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00148 (Chi-square test)

Table S83. Clustering Approach #6: '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 MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA
ALL	5	54	136	3	10	2	1	8	4
subtype1	2	11	17	2	0	0	1	2	3
subtype2	0	19	28	0	1	0	0	2	1
subtype3	3	24	91	1	9	2	0	4	0

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.463 (Chi-square test)

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	60	130	17	15
subtype1	8	21	6	3
subtype2	12	32	3	4
subtype3	40	77	8	8

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.32 (Chi-square test)

Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	132	43	42
subtype1	19	9	10
subtype2	36	7	7
subtype3	77	27	25

Figure S79. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.556 (Chi-square test)

Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	159	8	51
subtype1	29	1	8
subtype2	41	2	8
subtype3	89	5	35

Figure S80. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.685 (Chi-square test)

Table S87. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	115	47	47	9
subtype1	17	10	10	1
subtype2	31	7	11	2
subtype3	67	30	26	6

Figure S81. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.933 (Fisher's exact test)

Table S88. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	15	208
subtype1	3	35
subtype2	3	48
subtype3	9	125

Figure S82. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0574 (Fisher's exact test)

Table S89. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	36	187
subtype1	7	31
subtype2	3	48
subtype3	26	108

Figure S83. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.81 (ANOVA)

Table S90. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	155	40.0 (26.7)
subtype1	30	38.7 (26.7)
subtype2	41	42.3 (32.3)
subtype3	84	39.4 (23.8)

Figure S84. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.178 (ANOVA)

Table S91. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	110	1991.8 (14.2)
subtype1	20	1988.2 (16.0)
subtype2	30	1995.5 (11.8)
subtype3	60	1991.1 (14.4)

Figure S85. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.319 (Chi-square test)

Table S92. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	69	67	45	32
subtype1	13	13	6	5
subtype2	20	14	13	3
subtype3	36	40	26	24

Figure S86. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.521 (ANOVA)

Table S93. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	105	1962.0 (12.8)
subtype1	16	1964.3 (17.1)
subtype2	32	1960.1 (11.5)
subtype3	57	1962.4 (12.2)

Figure S87. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S94. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	77	92	54

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0745 (logrank test)

Table S95. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	195	62	0.0 - 224.0 (13.9)
subtype1	72	18	0.1 - 163.1 (13.8)
subtype2	76	28	0.1 - 83.8 (17.4)
subtype3	47	16	0.0 - 224.0 (12.7)

Figure S88. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.348 (ANOVA)

Table S96. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	199	65.9 (9.7)
subtype1	74	67.0 (8.9)
subtype2	83	65.9 (10.0)
subtype3	42	64.3 (10.4)

Figure S89. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.659 (Fisher's exact test)

Table S97. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	125	98
subtype1	40	37
subtype2	53	39
subtype3	32	22

Figure S90. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0336 (ANOVA)

Table S98. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	17	67.1 (39.8)
subtype1	8	88.8 (9.9)
subtype2	7	38.6 (48.5)
subtype3	2	80.0 (28.3)

Figure S91. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0143 (Chi-square test)

Table S99. Clustering Approach #7: 'MIRseq 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 MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA
ALL	5	54	136	3	10	2	1	8	4
subtype1	3	23	36	2	6	0	1	5	1
subtype2	1	26	60	0	3	0	0	1	1
subtype3	1	5	40	1	1	2	0	2	2

Figure S92. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.601 (Chi-square test)

Table S100. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	60	130	17	15
subtype1	25	39	5	7
subtype2	23	55	8	6
subtype3	12	36	4	2

Figure S93. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.85 (Chi-square test)

Table S101. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	132	43	42
subtype1	46	14	15
subtype2	57	17	15
subtype3	29	12	12

Figure S94. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.119 (Chi-square test)

Table S102. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	159	8	51
subtype1	48	4	22
subtype2	75	3	14
subtype3	36	1	15

Figure S95. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.854 (Chi-square test)

Table S103. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	115	47	47	9
subtype1	39	15	15	5
subtype2	50	20	19	3
subtype3	26	12	13	1

Figure S96. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.789 (Fisher's exact test)

Table S104. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	15	208
subtype1	6	71
subtype2	5	87
subtype3	4	50

Figure S97. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0568 (Fisher's exact test)

Table S105. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	36	187
subtype1	14	63
subtype2	9	83
subtype3	13	41

Figure S98. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

'MIRseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.739 (ANOVA)

Table S106. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	155	40.0 (26.7)
subtype1	55	39.5 (28.0)
subtype2	66	41.8 (26.6)
subtype3	34	37.5 (25.0)

Figure S99. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

'MIRseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.347 (ANOVA)

Table S107. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	110	1991.8 (14.2)
subtype1	45	1990.8 (15.5)
subtype2	41	1990.7 (13.7)
subtype3	24	1995.5 (12.1)

Figure S100. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

'MIRseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.249 (Chi-square test)

Table S108. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	69	67	45	32
subtype1	26	24	15	7
subtype2	23	30	22	13
subtype3	20	13	8	12

Figure S101. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.197 (ANOVA)

Table S109. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	105	1962.0 (12.8)
subtype1	42	1959.3 (13.9)
subtype2	41	1963.5 (12.6)
subtype3	22	1964.4 (10.3)

Figure S102. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S110. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	79	84	60

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0225 (logrank test)

Table S111. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	195	62	0.0 - 224.0 (13.9)
subtype1	65	27	0.1 - 83.8 (15.0)
subtype2	79	19	0.1 - 104.2 (13.9)
subtype3	51	16	0.0 - 224.0 (13.0)

Figure S103. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.152 (ANOVA)

Table S112. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	199	65.9 (9.7)
subtype1	70	65.9 (10.9)
subtype2	82	67.2 (9.0)
subtype3	47	63.8 (8.6)

Figure S104. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0696 (Fisher's exact test)

Table S113. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	125	98
subtype1	39	40
subtype2	45	39
subtype3	41	19

Figure S105. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.596 (ANOVA)

Table S114. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	17	67.1 (39.8)
subtype1	5	54.0 (49.8)
subtype2	8	67.5 (42.7)
subtype3	4	82.5 (17.1)

Figure S106. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.122 (Chi-square test)

Table S115. Clustering Approach #8: 'MIRseq 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 MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	MUCINOUS (COLLOID) ADENOCARCINOMA
ALL	5	54	136	3	10	2	1	8	4
subtype1	1	23	52	0	2	0	0	0	1
subtype2	2	23	43	2	6	0	1	5	2
subtype3	2	8	41	1	2	2	0	3	1

Figure S107. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.34 (Chi-square test)

Table S116. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	60	130	17	15
subtype1	16	48	9	6
subtype2	27	44	5	7
subtype3	17	38	3	2

Figure S108. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.876 (Chi-square test)

Table S117. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2+N3
ALL	132	43	42
subtype1	46	15	16
subtype2	52	15	13
subtype3	34	13	13

Figure S109. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.0366 (Chi-square test)

Table S118. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	159	8	51
subtype1	66	2	11
subtype2	58	4	19
subtype3	35	2	21

Figure S110. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.683 (Chi-square test)

Table S119. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	115	47	47	9
subtype1	38	19	20	2
subtype2	47	15	14	5
subtype3	30	13	13	2

Figure S111. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.443 (Fisher's exact test)

Table S120. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	15	208
subtype1	7	72
subtype2	6	78
subtype3	2	58

Figure S112. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.872 (Fisher's exact test)

Table S121. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	36	187
subtype1	12	67
subtype2	13	71
subtype3	11	49

Figure S113. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

'MIRseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.523 (ANOVA)

Table S122. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	155	40.0 (26.7)
subtype1	62	40.2 (26.7)
subtype2	57	42.5 (30.0)
subtype3	36	36.0 (20.3)

Figure S114. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBERPACKYEARSSMOKED'

'MIRseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.485 (ANOVA)

Table S123. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	110	1991.8 (14.2)
subtype1	35	1990.9 (14.2)
subtype2	48	1990.8 (15.4)
subtype3	27	1994.6 (11.9)

Figure S115. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #13: 'STOPPEDSMOKINGYEAR'

'MIRseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.204 (Chi-square test)

Table S124. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	69	67	45	32
subtype1	22	21	22	11
subtype2	28	28	15	8
subtype3	19	18	8	13

Figure S116. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #14: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.351 (ANOVA)

Table S125. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	105	1962.0 (12.8)
subtype1	38	1962.7 (13.1)
subtype2	46	1960.1 (13.1)
subtype3	21	1964.8 (11.3)

Figure S117. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #15: 'YEAROFTOBACCOSMOKINGONSET'

Methods & Data

Input

Cluster data file = LUAD.mergedcluster.txt
Clinical data file = LUAD.clin.merged.picked.txt
Number of patients = 223
Number of clustering approaches = 8
Number of selected clinical features = 15
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.test' 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

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

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

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