Lung Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Lung Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 10 different clustering approaches and 14 clinical features across 374 patients, 9 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to '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' and 'AGE'.
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 correlate to 'TOBACCOSMOKINGHISTORYINDICATOR'.

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, 9 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
Time to Death	logrank test	0.821 (1.00)	0.914 (1.00)	0.128 (1.00)	0.119 (1.00)	0.0886 (1.00)	0.0915 (1.00)	0.000718 (0.0955)	0.0133 (1.00)	0.228 (1.00)	0.102 (1.00)
AGE	ANOVA	0.477 (1.00)	0.557 (1.00)	0.00994 (1.00)	0.365 (1.00)	0.0308 (1.00)	0.0243 (1.00)	0.00119 (0.157)	0.15 (1.00)	0.464 (1.00)	0.226 (1.00)
GENDER	Fisher's exact test	0.272 (1.00)	0.383 (1.00)	0.106 (1.00)	0.00294 (0.376)	0.351 (1.00)	0.176 (1.00)	0.0331 (1.00)	0.00071 (0.0951)	0.596 (1.00)	0.016 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA			0.444 (1.00)	0.219 (1.00)	0.0451 (1.00)	0.554 (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)	0.343 (1.00)	0.331 (1.00)	0.11 (1.00)	0.00589 (0.73)	0.109 (1.00)	0.115 (1.00)	0.00423 (0.529)	0.00384 (0.484)
PATHOLOGY T	Chi-square test	0.489 (1.00)	0.479 (1.00)	0.0644 (1.00)	0.537 (1.00)	0.253 (1.00)	0.577 (1.00)	0.206 (1.00)	0.385 (1.00)	0.211 (1.00)	0.764 (1.00)
PATHOLOGY N	Chi-square test	0.572 (1.00)	0.651 (1.00)	0.222 (1.00)	0.626 (1.00)	0.299 (1.00)	0.574 (1.00)	0.0035 (0.444)	0.0611 (1.00)	0.354 (1.00)	0.651 (1.00)
PATHOLOGICSPREAD(M)	Chi-square test	0.504 (1.00)	1 (1.00)	0.544 (1.00)	0.821 (1.00)	0.764 (1.00)	0.827 (1.00)	0.887 (1.00)	0.654 (1.00)	0.337 (1.00)	0.531 (1.00)
TUMOR STAGE	Chi-square test	0.147 (1.00)	0.274 (1.00)	0.0264 (1.00)	0.893 (1.00)	0.813 (1.00)	0.962 (1.00)	0.148 (1.00)	0.0997 (1.00)	0.26 (1.00)	0.569 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	1 (1.00)	1 (1.00)	0.746 (1.00)	0.786 (1.00)	0.946 (1.00)	0.545 (1.00)	0.761 (1.00)	0.677 (1.00)	0.622 (1.00)	1 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.448 (1.00)	0.357 (1.00)	0.186 (1.00)	0.0179 (1.00)	0.458 (1.00)	0.208 (1.00)	0.0613 (1.00)	0.186 (1.00)	0.164 (1.00)	0.297 (1.00)
TOBACCOSMOKINGHISTORYINDICATOR	Chi-square test	0.0492 (1.00)	0.184 (1.00)	0.00776 (0.954)	0.00131 (0.171)	0.000622 (0.084)	3.41e-05 (0.00468)	0.00204 (0.264)	2.32e-05 (0.0032)	0.000159 (0.0216)	0.00182 (0.236)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.616 (1.00)	0.68 (1.00)	0.0982 (1.00)	0.907 (1.00)	0.068 (1.00)	0.0502 (1.00)	0.362 (1.00)	0.221 (1.00)	0.29 (1.00)	0.17 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.188 (1.00)	0.58 (1.00)	0.999 (1.00)	0.921 (1.00)	0.153 (1.00)	0.577 (1.00)	0.0123 (1.00)	0.697 (1.00)	0.194 (1.00)	0.364 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	5	9	12	6

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.821 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	31	4	0.5 - 56.8 (8.3)
subtype1	4	0	6.0 - 48.6 (9.7)
subtype2	9	1	4.0 - 56.8 (8.3)
subtype3	12	1	0.5 - 37.0 (3.3)
subtype4	6	2	2.0 - 45.2 (30.9)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.477 (ANOVA), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

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

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

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

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

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

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

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

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

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

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

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

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

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

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

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

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

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

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

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

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

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

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

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

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

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

'mRNA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

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

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

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

'mRNA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

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

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	12	10	5	5
subtype1	3	0	0	2
subtype2	5	3	1	0
subtype3	2	6	1	3
subtype4	2	1	3	0

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

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

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

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

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

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

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

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	7	13	12

'mRNA cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'mRNA cHierClus subtypes' versus 'AGE'

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

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

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

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

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

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

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

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

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

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

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

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

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

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

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

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

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

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

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

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

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

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

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

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

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

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

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

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

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

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

'mRNA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

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

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

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

'mRNA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

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

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	12	10	5	5
subtype1	3	1	3	0
subtype2	3	6	1	3
subtype3	6	3	1	2

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

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

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

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

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

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

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

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

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

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

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

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

Cluster Labels	1	2	3	4
Number of samples	113	144	71	42

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

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

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	331	85	0.0 - 224.0 (8.5)
subtype1	100	28	0.0 - 224.0 (9.6)
subtype2	130	29	0.0 - 104.2 (7.2)
subtype3	63	20	0.1 - 88.1 (8.4)
subtype4	38	8	0.1 - 83.8 (8.5)

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 = 0.00994 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	339	65.3 (9.8)
subtype1	103	63.7 (9.9)
subtype2	131	67.5 (9.2)
subtype3	67	64.0 (10.1)
subtype4	38	63.9 (9.8)

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 = 0.106 (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	203	167
subtype1	68	45
subtype2	83	61
subtype3	35	36
subtype4	17	25

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 = 0.444 (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	91.2 (8.3)
subtype3	6	73.3 (37.2)
subtype4	4	65.0 (43.6)

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.343 (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	11	78	228	4	16	2	3	2	16	3	7
subtype1	3	21	70	2	8	1	2	0	5	1	0
subtype2	3	33	82	2	6	1	1	2	8	0	6
subtype3	1	14	50	0	2	0	0	0	2	1	1
subtype4	4	10	26	0	0	0	0	0	1	1	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 = 0.0644 (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	113	208	30	17
subtype1	32	70	7	4
subtype2	53	73	9	8
subtype3	12	43	11	4
subtype4	16	22	3	1

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

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

P value = 0.222 (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	232	71	58
subtype1	69	21	21
subtype2	90	32	18
subtype3	43	16	11
subtype4	30	2	8

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.544 (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	247	17	3	95
subtype1	73	6	0	32
subtype2	93	4	2	41
subtype3	48	5	1	15
subtype4	33	2	0	7

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 = 0.0264 (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	198	81	65	20
subtype1	60	23	22	6
subtype2	81	34	18	7
subtype3	28	22	15	6
subtype4	29	2	10	1

Figure S35. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.746 (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	350
subtype1	5	108
subtype2	7	137
subtype3	5	66
subtype4	3	39

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 = 0.186 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	260	41.0 (27.1)
subtype1	71	46.0 (25.2)
subtype2	98	37.9 (28.0)
subtype3	58	42.6 (26.7)
subtype4	33	36.6 (28.1)

Figure S37. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.00776 (Chi-square test), Q value = 0.95

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	125	94	84	54
subtype1	38	25	27	20
subtype2	45	46	21	25
subtype3	27	10	25	7
subtype4	15	13	11	2

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

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0982 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	189	1965.2 (12.6)
subtype1	49	1962.9 (11.9)
subtype2	70	1963.9 (12.8)
subtype3	44	1968.8 (13.7)
subtype4	26	1966.8 (10.4)

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

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

P value = 0.999 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	218	9	4	13
subtype1	67	3	1	5
subtype2	82	3	2	5
subtype3	46	2	1	2
subtype4	23	1	0	1

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

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3	4	5
Number of samples	65	73	68	77	21

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.119 (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	271	64	0.0 - 224.0 (7.1)
subtype1	58	11	0.0 - 224.0 (5.9)
subtype2	64	21	0.1 - 88.1 (9.1)
subtype3	60	9	0.0 - 77.9 (5.3)
subtype4	70	17	0.0 - 71.5 (6.2)
subtype5	19	6	0.3 - 44.4 (7.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.365 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	276	65.2 (9.9)
subtype1	57	67.1 (8.9)
subtype2	67	63.6 (9.7)
subtype3	63	65.5 (10.2)
subtype4	69	65.1 (10.3)
subtype5	20	64.0 (10.9)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00294 (Chi-square test), Q value = 0.38

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

nPatients	FEMALE	MALE
ALL	167	137
subtype1	43	22
subtype2	28	45
subtype3	33	35
subtype4	49	28
subtype5	14	7

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

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

P value = 0.219 (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	6	93.3 (5.2)
subtype2	3	83.3 (5.8)
subtype3	7	80.0 (12.9)
subtype4	3	60.0 (52.9)
subtype5	2	0.0 (0.0)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.331 (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	11	63	180	4	16	1	2	2	15	3	7
subtype1	5	15	28	2	7	0	1	2	3	0	2
subtype2	3	16	46	0	1	0	0	0	4	1	2
subtype3	0	18	42	1	2	0	1	0	3	0	1
subtype4	1	11	51	1	4	1	0	0	4	2	2
subtype5	2	3	13	0	2	0	0	0	1	0	0

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.537 (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	96	166	25	14
subtype1	25	31	5	3
subtype2	17	43	8	5
subtype3	16	42	7	3
subtype4	30	39	4	2
subtype5	8	11	1	1

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.626 (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	191	57	48
subtype1	46	10	7
subtype2	45	12	14
subtype3	41	14	12
subtype4	47	14	13
subtype5	12	7	2

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

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

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

nPatients	M0	M1	M1B	MX
ALL	186	11	3	97
subtype1	40	1	1	20
subtype2	47	4	2	19
subtype3	39	3	0	23
subtype4	47	2	0	28
subtype5	13	1	0	7

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.893 (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	161	67	55	15
subtype1	38	14	8	2
subtype2	33	17	18	5
subtype3	33	15	13	4
subtype4	45	16	13	3
subtype5	12	5	3	1

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

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

P value = 0.786 (Chi-square test), Q value = 1

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

nPatients	NO	YES
ALL	16	288
subtype1	2	63
subtype2	4	69
subtype3	3	65
subtype4	6	71
subtype5	1	20

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0179 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	210	40.2 (27.0)
subtype1	39	31.2 (19.3)
subtype2	61	47.3 (32.4)
subtype3	48	39.9 (26.8)
subtype4	48	35.7 (21.9)
subtype5	14	50.2 (28.3)

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

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.00131 (Chi-square test), Q value = 0.17

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	102	76	68	45
subtype1	17	24	5	16
subtype2	27	14	27	3
subtype3	25	16	18	8
subtype4	26	17	14	13
subtype5	7	5	4	5

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

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.907 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	159	1965.2 (12.3)
subtype1	29	1964.7 (11.9)
subtype2	50	1965.4 (14.0)
subtype3	35	1965.1 (12.2)
subtype4	32	1966.5 (10.7)
subtype5	13	1962.5 (11.1)

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

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

P value = 0.921 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	161	7	1	11
subtype1	31	1	0	3
subtype2	42	2	0	2
subtype3	39	2	0	3
subtype4	35	1	1	3
subtype5	14	1	0	0

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

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	86	75	75

'RPPA CNMF subtypes' versus 'Time to Death'

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

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	213	66	0.0 - 224.0 (12.3)
subtype1	71	25	0.0 - 163.1 (12.1)
subtype2	71	17	0.1 - 224.0 (13.7)
subtype3	71	24	0.0 - 83.8 (10.6)

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 = 0.0308 (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	75	64.1 (10.1)
subtype2	71	67.3 (8.9)
subtype3	69	63.0 (10.1)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.351 (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	53	33
subtype2	40	35
subtype3	38	37

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 = 0.0451 (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	6	41.7 (46.2)
subtype2	6	91.7 (7.5)
subtype3	8	70.0 (29.8)

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 = 0.11 (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	56	0	3	1	1	1	1	1	1
subtype2	3	21	36	3	6	0	1	0	3	0	2
subtype3	1	12	58	0	1	0	0	1	2	0	0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.253 (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	24	49	8	4
subtype2	23	43	2	7
subtype3	17	49	7	2

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.299 (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	45	22	15
subtype2	49	13	12
subtype3	46	11	17

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

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

P value = 0.764 (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	60	5	19
subtype2	49	4	21
subtype3	56	3	15

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

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

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

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

nPatients	I	II	III	IV
ALL	122	49	49	13
subtype1	43	20	16	5
subtype2	38	17	14	5
subtype3	41	12	19	3

Figure S63. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.946 (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	80
subtype2	4	71
subtype3	5	70

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

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

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

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

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

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000622 (Chi-square test), Q value = 0.084

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	84	59	52	31
subtype1	27	24	11	19
subtype2	26	25	16	6
subtype3	31	10	25	6

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

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

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

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

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

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

P value = 0.153 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	127	6	3	11
subtype1	43	2	3	5
subtype2	43	3	0	1
subtype3	41	1	0	5

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	23	90	65	58

'RPPA cHierClus subtypes' versus 'Time to Death'

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

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	213	66	0.0 - 224.0 (12.3)
subtype1	18	4	0.0 - 63.7 (7.1)
subtype2	77	30	0.7 - 163.1 (13.2)
subtype3	62	19	0.0 - 83.8 (8.9)
subtype4	56	13	0.1 - 224.0 (14.5)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0243 (ANOVA), Q value = 1

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	79	63.4 (10.0)
subtype3	61	63.2 (10.8)
subtype4	55	67.5 (8.9)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.176 (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	56	34
subtype3	33	32
subtype4	27	31

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

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

P value = 0.554 (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	6	53.3 (42.7)
subtype3	5	66.0 (37.1)
subtype4	5	86.0 (11.4)

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00589 (Chi-square test), Q value = 0.73

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	1	20	63	0	2	0	0	0	2	1	1
subtype3	2	10	48	0	2	1	0	1	1	0	0
subtype4	2	18	27	3	3	0	0	1	2	0	2

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.577 (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	23	58	3	5
subtype3	17	39	7	2
subtype4	17	33	5	3

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.574 (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	48	21	16
subtype3	39	9	16
subtype4	38	12	8

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

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

P value = 0.827 (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	62	6	20
subtype3	47	2	15
subtype4	42	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 = 0.962 (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	48	19	15	6
subtype3	32	12	18	3
subtype4	30	13	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 = 0.545 (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	6	84
subtype3	6	59
subtype4	3	55

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

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

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

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

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

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 3.41e-05 (Chi-square test), Q value = 0.0047

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	84	59	52	31
subtype1	13	5	0	4
subtype2	27	23	15	19
subtype3	26	8	25	4
subtype4	18	23	12	4

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

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

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

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

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

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

P value = 0.577 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	127	6	3	11
subtype1	12	0	0	0
subtype2	46	3	3	4
subtype3	34	1	0	4
subtype4	35	2	0	3

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4	5
Number of samples	95	70	51	75	40

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.000718 (logrank test), Q value = 0.095

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	295	80	0.0 - 224.0 (9.8)
subtype1	86	23	0.0 - 163.1 (7.9)
subtype2	62	13	0.1 - 104.2 (9.0)
subtype3	46	20	0.0 - 47.6 (8.9)
subtype4	65	17	0.1 - 88.1 (15.0)
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 = 0.00119 (ANOVA), Q value = 0.16

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

	nPatients	Mean (Std.Dev)
ALL	300	65.4 (9.8)
subtype1	86	63.3 (10.7)
subtype2	64	68.2 (7.8)
subtype3	46	68.7 (9.3)
subtype4	68	63.3 (9.4)
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 = 0.0331 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	180	151
subtype1	52	43
subtype2	46	24
subtype3	23	28
subtype4	33	42
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 = 0.109 (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	204	3	14	2	3	2	13	2	4
subtype1	1	15	69	0	3	1	2	0	4	0	0
subtype2	4	18	38	1	6	0	1	0	2	0	0
subtype3	1	12	30	1	0	1	0	1	2	0	3
subtype4	2	17	47	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 = 0.206 (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	97	188	27	17
subtype1	24	59	7	5
subtype2	30	34	3	3
subtype3	9	30	7	4
subtype4	19	46	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 = 0.0035 (Chi-square test), Q value = 0.44

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

nPatients	N0	N1	N2+N3
ALL	204	65	53
subtype1	51	24	19
subtype2	51	11	7
subtype3	22	14	14
subtype4	58	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 = 0.887 (Chi-square test), Q value = 1

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

nPatients	M0	M1	M1B	MX
ALL	226	17	1	79
subtype1	66	4	0	23
subtype2	49	2	0	17
subtype3	38	3	0	10
subtype4	49	5	1	18
subtype5	24	3	0	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 = 0.148 (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	174	72	61	18
subtype1	43	25	20	5
subtype2	44	13	8	2
subtype3	19	13	16	3
subtype4	45	13	12	5
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 = 0.761 (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	313
subtype1	6	89
subtype2	4	66
subtype3	4	47
subtype4	2	73
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 = 0.0613 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	232	40.0 (26.3)
subtype1	65	43.6 (25.2)
subtype2	46	30.3 (21.2)
subtype3	39	39.0 (26.6)
subtype4	56	44.3 (30.6)
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 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.00204 (Chi-square test), Q value = 0.26

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	114	87	73	46
subtype1	35	22	20	16
subtype2	17	30	8	12
subtype3	21	11	11	5
subtype4	28	11	28	7
subtype5	13	13	6	6

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

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.362 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	168	1964.9 (12.9)
subtype1	44	1967.0 (13.8)
subtype2	35	1961.2 (11.1)
subtype3	27	1964.7 (14.3)
subtype4	43	1965.7 (12.8)
subtype5	19	1965.3 (11.2)

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

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

P value = 0.0123 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	183	9	4	11
subtype1	55	2	0	3
subtype2	40	3	0	2
subtype3	29	1	1	2
subtype4	43	3	0	1
subtype5	16	0	3	3

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	143	99	89

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0133 (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	295	80	0.0 - 224.0 (9.8)
subtype1	127	29	0.0 - 224.0 (12.3)
subtype2	89	28	0.1 - 83.8 (11.5)
subtype3	79	23	0.0 - 77.9 (8.1)

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 = 0.15 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	300	65.4 (9.8)
subtype1	130	66.6 (9.0)
subtype2	90	64.4 (9.7)
subtype3	80	64.5 (11.0)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00071 (Fisher's exact test), Q value = 0.095

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

nPatients	FEMALE	MALE
ALL	180	151
subtype1	88	55
subtype2	38	61
subtype3	54	35

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 = 0.115 (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	204	3	14	2	3	2	13	2	4
subtype1	7	36	77	3	9	0	1	2	6	0	2
subtype2	2	23	64	0	2	1	0	0	3	2	2
subtype3	1	15	63	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 = 0.385 (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	97	188	27	17
subtype1	52	73	11	6
subtype2	23	61	8	6
subtype3	22	54	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 = 0.0611 (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	204	65	53
subtype1	96	28	14
subtype2	60	16	21
subtype3	48	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 = 0.654 (Chi-square test), Q value = 1

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

nPatients	M0	M1	M1B	MX
ALL	226	17	1	79
subtype1	95	7	0	36
subtype2	66	7	1	23
subtype3	65	3	0	20

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 = 0.0997 (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	174	72	61	18
subtype1	85	30	16	7
subtype2	48	20	24	7
subtype3	41	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	313
subtype1	6	137
subtype2	6	93
subtype3	6	83

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 = 0.186 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	232	40.0 (26.3)
subtype1	95	36.2 (26.5)
subtype2	77	43.0 (27.5)
subtype3	60	42.2 (24.2)

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

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 2.32e-05 (Chi-square test), Q value = 0.0032

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	114	87	73	46
subtype1	45	52	17	23
subtype2	41	15	34	7
subtype3	28	20	22	16

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

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.221 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	168	1964.9 (12.9)
subtype1	69	1962.9 (12.6)
subtype2	57	1966.7 (12.6)
subtype3	42	1965.9 (13.5)

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

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

P value = 0.697 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	183	9	4	11
subtype1	77	3	2	6
subtype2	57	4	0	2
subtype3	49	2	2	3

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

Clustering Approach #9: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	141	155	51

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.228 (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	309	80	0.0 - 224.0 (8.7)
subtype1	127	29	0.1 - 163.1 (8.5)
subtype2	135	37	0.0 - 83.8 (8.8)
subtype3	47	14	0.0 - 224.0 (8.1)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.464 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	317	65.1 (9.9)
subtype1	131	65.9 (9.9)
subtype2	143	64.4 (10.1)
subtype3	43	65.0 (9.3)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.596 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	189	158
subtype1	79	62
subtype2	80	75
subtype3	30	21

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 = 0.00423 (Chi-square test), Q value = 0.53

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	211	4	16	2	2	2	16	3	7
subtype1	5	32	74	3	10	0	2	2	9	0	4
subtype2	5	38	100	0	4	2	0	0	3	3	0
subtype3	0	4	37	1	2	0	0	0	4	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 = 0.211 (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	101	196	30	17
subtype1	52	70	11	7
subtype2	35	95	16	8
subtype3	14	31	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 = 0.354 (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	212	69	56
subtype1	88	27	20
subtype2	99	28	25
subtype3	25	14	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 = 0.337 (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	234	15	3	87
subtype1	92	6	1	38
subtype2	112	8	2	31
subtype3	30	1	0	18

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

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.26 (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	183	75	64	19
subtype1	80	29	19	8
subtype2	83	32	31	9
subtype3	20	14	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 = 0.622 (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	328
subtype1	10	131
subtype2	7	148
subtype3	2	49

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

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.164 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	239	40.9 (27.2)
subtype1	94	37.2 (26.8)
subtype2	114	44.3 (28.2)
subtype3	31	39.2 (23.6)

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

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000159 (Chi-square test), Q value = 0.022

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	116	86	79	53
subtype1	45	50	21	18
subtype2	53	28	49	21
subtype3	18	8	9	14

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

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.29 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	174	1965.0 (12.8)
subtype1	72	1963.4 (13.6)
subtype2	83	1966.6 (12.3)
subtype3	19	1964.2 (11.6)

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

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

P value = 0.194 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	196	9	4	12
subtype1	77	3	1	5
subtype2	100	5	3	3
subtype3	19	1	0	4

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

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels	1	2	3
Number of samples	144	100	103

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.102 (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	309	80	0.0 - 224.0 (8.7)
subtype1	130	31	0.1 - 163.1 (8.4)
subtype2	90	30	0.0 - 224.0 (7.9)
subtype3	89	19	0.0 - 83.8 (12.1)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

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

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

	nPatients	Mean (Std.Dev)
ALL	317	65.1 (9.9)
subtype1	133	66.1 (9.8)
subtype2	91	63.8 (9.8)
subtype3	93	65.0 (10.0)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.016 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	189	158
subtype1	84	60
subtype2	61	39
subtype3	44	59

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 = 0.00384 (Chi-square test), Q value = 0.48

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	211	4	16	2	2	2	16	3	7
subtype1	6	31	76	2	10	0	2	2	9	0	6
subtype2	2	15	72	2	3	0	0	0	6	0	0
subtype3	2	28	63	0	3	2	0	0	1	3	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 = 0.764 (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	101	196	30	17
subtype1	47	75	12	8
subtype2	23	62	9	5
subtype3	31	59	9	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 = 0.651 (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	212	69	56
subtype1	89	30	18
subtype2	59	21	19
subtype3	64	18	19

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.531 (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	234	15	3	87
subtype1	92	6	1	41
subtype2	68	6	0	23
subtype3	74	3	2	23

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

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.569 (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	183	75	64	19
subtype1	79	32	19	9
subtype2	50	20	23	6
subtype3	54	23	22	4

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	328
subtype1	8	136
subtype2	5	95
subtype3	6	97

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.297 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	239	40.9 (27.2)
subtype1	93	37.5 (25.1)
subtype2	68	43.9 (24.8)
subtype3	78	42.2 (31.2)

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

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.00182 (Chi-square test), Q value = 0.24

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

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	116	86	79	53
subtype1	47	48	20	21
subtype2	32	19	25	21
subtype3	37	19	34	11

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

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.17 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	174	1965.0 (12.8)
subtype1	73	1963.0 (12.7)
subtype2	40	1965.5 (13.0)
subtype3	61	1967.2 (12.7)

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

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

P value = 0.364 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	196	9	4	12
subtype1	76	3	1	8
subtype2	55	4	2	3
subtype3	65	2	1	1

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

Methods & Data

Input

Cluster data file = LUAD-TP.mergedcluster.txt
Clinical data file = LUAD-TP.clin.merged.picked.txt
Number of patients = 374
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)

Made with Nozzle