Correlation between aggregated molecular cancer subtypes and selected clinical features

Lung Squamous Cell Carcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1BR8QH6

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 12 different clustering approaches and 13 clinical features across 394 patients, 4 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 correlate to 'PATHOLOGY.T.STAGE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGY.T.STAGE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGY.M.STAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 4 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	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.384 (1.00)	0.415 (1.00)	0.247 (1.00)	0.158 (1.00)	0.0101 (1.00)	0.00999 (1.00)	0.171 (1.00)	0.864 (1.00)	0.992 (1.00)	0.413 (1.00)	0.908 (1.00)	0.875 (1.00)
AGE	t-test	0.962 (1.00)	0.438 (1.00)	0.033 (1.00)	0.0972 (1.00)	0.0153 (1.00)	0.0759 (1.00)	0.0394 (1.00)	0.0437 (1.00)	0.0751 (1.00)	0.217 (1.00)	0.129 (1.00)	0.648 (1.00)
NEOPLASM DISEASESTAGE	Chi-square test	0.0228 (1.00)	0.00998 (1.00)	0.588 (1.00)	0.672 (1.00)	0.296 (1.00)	0.454 (1.00)	0.276 (1.00)	0.451 (1.00)	0.366 (1.00)	0.0578 (1.00)	0.0159 (1.00)	0.794 (1.00)
PATHOLOGY T STAGE	Chi-square test	0.00124 (0.19)	9.97e-05 (0.0154)	0.695 (1.00)	0.652 (1.00)	0.185 (1.00)	0.274 (1.00)	0.494 (1.00)	0.527 (1.00)	0.228 (1.00)	0.563 (1.00)	0.00297 (0.452)	0.277 (1.00)
PATHOLOGY N STAGE	Chi-square test	0.877 (1.00)	0.427 (1.00)	0.432 (1.00)	0.771 (1.00)	0.308 (1.00)	0.0902 (1.00)	0.413 (1.00)	0.333 (1.00)	0.307 (1.00)	0.375 (1.00)	0.172 (1.00)	0.661 (1.00)
PATHOLOGY M STAGE	Chi-square test	0.105 (1.00)	0.0642 (1.00)	0.673 (1.00)	0.316 (1.00)	0.612 (1.00)	0.304 (1.00)	0.64 (1.00)	0.372 (1.00)	4.74e-05 (0.0074)	0.0718 (1.00)	0.665 (1.00)	0.0664 (1.00)
GENDER	Fisher's exact test	0.331 (1.00)	0.00305 (0.46)	0.0445 (1.00)	0.841 (1.00)	0.266 (1.00)	0.874 (1.00)	0.681 (1.00)	0.144 (1.00)	0.888 (1.00)	0.286 (1.00)	0.98 (1.00)	0.433 (1.00)
KARNOFSKY PERFORMANCE SCORE	t-test	0.191 (1.00)	0.108 (1.00)	0.159 (1.00)	0.344 (1.00)	0.527 (1.00)	0.918 (1.00)	0.808 (1.00)	0.649 (1.00)	0.094 (1.00)	0.662 (1.00)	0.76 (1.00)	0.0888 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.32 (1.00)	0.264 (1.00)	0.132 (1.00)	0.362 (1.00)	0.134 (1.00)	0.0593 (1.00)	0.689 (1.00)	0.069 (1.00)	0.0905 (1.00)	0.474 (1.00)	0.261 (1.00)	0.232 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.55 (1.00)	0.183 (1.00)	1 (1.00)	0.279 (1.00)	0.524 (1.00)	0.583 (1.00)	0.305 (1.00)	0.303 (1.00)	0.665 (1.00)	0.00107 (0.165)	0.637 (1.00)	0.502 (1.00)
NUMBERPACKYEARSSMOKED	t-test	0.525 (1.00)	0.822 (1.00)	0.268 (1.00)	0.189 (1.00)	0.0516 (1.00)	0.105 (1.00)	0.894 (1.00)	0.652 (1.00)	0.554 (1.00)	0.793 (1.00)	0.927 (1.00)	0.476 (1.00)
YEAROFTOBACCOSMOKINGONSET	t-test	0.903 (1.00)	0.0465 (1.00)	0.263 (1.00)	0.596 (1.00)	0.151 (1.00)	0.262 (1.00)	0.276 (1.00)	0.0771 (1.00)	0.264 (1.00)	0.0511 (1.00)	0.272 (1.00)	0.114 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.546 (1.00)	0.384 (1.00)	0.238 (1.00)	0.488 (1.00)	0.033 (1.00)	0.0213 (1.00)	0.746 (1.00)	0.327 (1.00)	0.72 (1.00)	0.882 (1.00)	0.477 (1.00)	0.494 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	42	52	32	28

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.384 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	63	0.4 - 173.8 (20.7)
subtype1	41	16	0.4 - 122.4 (19.0)
subtype2	50	19	0.4 - 99.2 (29.4)
subtype3	30	17	0.4 - 173.8 (17.5)
subtype4	27	11	0.4 - 114.0 (11.8)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.962 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	152	66.5 (8.6)
subtype1	41	66.4 (7.7)
subtype2	51	66.5 (8.2)
subtype3	32	67.2 (9.8)
subtype4	28	66.0 (9.4)

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0228 (Chi-square test), Q value = 1

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

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	20	61	7	27	19	15	4
subtype1	2	21	0	9	6	2	2
subtype2	2	24	4	9	6	7	0
subtype3	8	11	2	4	4	2	0
subtype4	8	5	1	5	3	4	2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00124 (Chi-square test), Q value = 0.19

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	30	100	12	12
subtype1	5	30	6	1
subtype2	4	41	1	6
subtype3	11	18	2	1
subtype4	10	11	3	4

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.877 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	96	40	13	5
subtype1	26	11	3	2
subtype2	28	17	5	2
subtype3	22	6	3	1
subtype4	20	6	2	0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.105 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1
ALL	146	4
subtype1	38	2
subtype2	50	0
subtype3	32	0
subtype4	26	2

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.331 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	44	110
subtype1	9	33
subtype2	13	39
subtype3	11	21
subtype4	11	17

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

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

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

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	5	1	148
subtype1	0	0	42
subtype2	3	0	49
subtype3	1	0	31
subtype4	1	1	26

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

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

P value = 0.55 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	2	152
subtype1	1	41
subtype2	0	52
subtype3	1	31
subtype4	0	28

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

'mRNA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.525 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	133	54.8 (36.5)
subtype1	38	60.2 (41.4)
subtype2	47	52.0 (25.5)
subtype3	27	48.6 (36.2)
subtype4	21	59.6 (47.7)

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

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.903 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	97	1958.0 (10.6)
subtype1	28	1957.0 (8.8)
subtype2	26	1958.5 (10.8)
subtype3	23	1957.7 (10.9)
subtype4	20	1959.2 (12.7)

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

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

P value = 0.546 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	139	3	2	5
subtype1	38	0	1	1
subtype2	47	1	1	1
subtype3	27	1	0	3
subtype4	27	1	0	0

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	22	56	23	53

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.415 (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	148	63	0.4 - 173.8 (20.7)
subtype1	22	7	0.4 - 92.7 (14.3)
subtype2	54	21	0.4 - 99.2 (27.7)
subtype3	22	10	1.0 - 122.4 (19.7)
subtype4	50	25	0.4 - 173.8 (14.5)

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 = 0.438 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	152	66.5 (8.6)
subtype1	22	64.1 (7.1)
subtype2	55	66.2 (8.0)
subtype3	22	67.1 (9.8)
subtype4	53	67.6 (9.2)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00998 (Chi-square test), Q value = 1

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

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	20	61	7	27	19	15	4
subtype1	9	4	0	2	4	2	1
subtype2	2	25	4	10	6	9	0
subtype3	3	10	1	4	1	2	2
subtype4	6	22	2	11	8	2	1

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 9.97e-05 (Chi-square test), Q value = 0.015

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	30	100	12	12
subtype1	12	6	3	1
subtype2	4	44	1	7
subtype3	4	16	1	2
subtype4	10	34	7	2

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.427 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	96	40	13	5
subtype1	17	2	3	0
subtype2	30	18	5	3
subtype3	13	8	1	1
subtype4	36	12	4	1

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0642 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1
ALL	146	4
subtype1	21	1
subtype2	54	0
subtype3	21	2
subtype4	50	1

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00305 (Fisher's exact test), Q value = 0.46

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

nPatients	FEMALE	MALE
ALL	44	110
subtype1	14	8
subtype2	13	43
subtype3	5	18
subtype4	12	41

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

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

P value = 0.108 (ANOVA), Q value = 1

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	26	24.2 (38.5)
subtype1	8	48.8 (42.6)
subtype2	5	10.0 (22.4)
subtype3	5	0.0 (0.0)
subtype4	8	23.8 (44.1)

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.264 (Chi-square test), Q value = 1

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

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	5	1	148
subtype1	1	0	21
subtype2	3	0	53
subtype3	0	1	22
subtype4	1	0	52

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

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

P value = 0.183 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	2	152
subtype1	1	21
subtype2	0	56
subtype3	0	23
subtype4	1	52

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

'mRNA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.822 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	133	54.8 (36.5)
subtype1	18	59.3 (49.1)
subtype2	51	51.1 (24.6)
subtype3	20	56.7 (30.2)
subtype4	44	56.5 (44.7)

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

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0465 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	97	1958.0 (10.6)
subtype1	17	1963.3 (10.5)
subtype2	29	1959.5 (10.7)
subtype3	15	1955.5 (10.3)
subtype4	36	1955.4 (9.9)

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

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

P value = 0.384 (Chi-square test), Q value = 1

Table S28. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	139	3	2	5
subtype1	21	0	0	0
subtype2	51	1	1	1
subtype3	22	0	1	0
subtype4	45	2	0	4

Figure S26. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

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

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

Cluster Labels	1	2	3
Number of samples	158	129	104

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

P value = 0.247 (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	357	122	0.0 - 173.8 (11.7)
subtype1	141	56	0.0 - 173.8 (12.0)
subtype2	121	36	0.1 - 114.0 (13.6)
subtype3	95	30	0.0 - 122.4 (8.9)

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 = 0.033 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	383	67.2 (8.8)
subtype1	155	67.0 (9.7)
subtype2	126	65.9 (8.2)
subtype3	102	68.9 (7.7)

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 'NEOPLASM.DISEASESTAGE'

P value = 0.588 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	1	67	131	1	45	65	53	20	5
subtype1	0	30	57	0	19	26	18	5	1
subtype2	0	20	44	1	15	19	17	11	2
subtype3	1	17	30	0	11	20	18	4	2

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

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

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	92	239	42	18
subtype1	37	95	21	5
subtype2	30	78	14	7
subtype3	25	66	7	6

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.432 (Chi-square test), Q value = 1

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	245	103	33	5
subtype1	109	33	11	2
subtype2	75	40	11	2
subtype3	61	30	11	1

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.673 (Chi-square test), Q value = 1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	333	5	47
subtype1	133	1	23
subtype2	110	2	13
subtype3	90	2	11

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0445 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	98	293
subtype1	50	108
subtype2	25	104
subtype3	23	81

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

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

P value = 0.159 (ANOVA), Q value = 1

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	54	28.5 (39.7)
subtype1	21	41.4 (43.9)
subtype2	15	18.7 (34.8)
subtype3	18	21.7 (36.2)

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

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

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	12	5	1	373
subtype1	2	3	1	152
subtype2	8	2	0	119
subtype3	2	0	0	102

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

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

P value = 1 (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	12	379
subtype1	5	153
subtype2	4	125
subtype3	3	101

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 = 0.268 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	331	52.4 (31.7)
subtype1	133	49.5 (30.6)
subtype2	112	52.4 (31.6)
subtype3	86	56.7 (33.4)

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 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.263 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	259	1959.6 (12.0)
subtype1	108	1959.2 (12.5)
subtype2	75	1961.4 (11.3)
subtype3	76	1958.4 (11.9)

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

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

P value = 0.238 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	317	7	4	18
subtype1	122	2	2	11
subtype2	106	3	1	7
subtype3	89	2	1	0

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

Clustering Approach #4: 'METHLYATION CNMF'

Table S43. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	106	82	72

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.158 (logrank test), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	230	70	0.0 - 173.8 (9.0)
subtype1	91	31	0.0 - 173.8 (6.9)
subtype2	75	22	0.1 - 141.3 (11.8)
subtype3	64	17	0.0 - 107.0 (10.2)

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0972 (ANOVA), Q value = 1

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	252	67.6 (9.0)
subtype1	103	68.8 (9.8)
subtype2	80	65.9 (8.8)
subtype3	69	67.6 (7.7)

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.672 (Chi-square test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	2	52	76	1	41	42	37	5	2
subtype1	1	24	29	0	15	21	13	1	2
subtype2	1	15	29	1	14	8	12	2	0
subtype3	0	13	18	0	12	13	12	2	0

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.652 (Chi-square test), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	70	152	31	7
subtype1	28	62	13	3
subtype2	26	42	12	2
subtype3	16	48	6	2

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.771 (Chi-square test), Q value = 1

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	164	69	22
subtype1	66	26	9
subtype2	56	20	6
subtype3	42	23	7

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.316 (Chi-square test), Q value = 1

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	209	2	47
subtype1	81	2	23
subtype2	68	0	14
subtype3	60	0	10

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.841 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	64	196
subtype1	28	78
subtype2	20	62
subtype3	16	56

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.344 (ANOVA), Q value = 1

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	31	29.4 (40.2)
subtype1	5	42.0 (49.2)
subtype2	13	16.9 (34.5)
subtype3	13	36.9 (41.7)

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.362 (Chi-square test), Q value = 1

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	9	4	1	246
subtype1	1	2	0	103
subtype2	5	1	0	76
subtype3	3	1	1	67

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.279 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	10	250
subtype1	5	101
subtype2	1	81
subtype3	4	68

Figure S49. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.189 (ANOVA), Q value = 1

Table S54. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	220	51.3 (29.3)
subtype1	88	49.2 (28.2)
subtype2	69	48.7 (23.9)
subtype3	63	56.9 (35.3)

Figure S50. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.596 (ANOVA), Q value = 1

Table S55. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	177	1960.2 (12.3)
subtype1	71	1959.3 (12.3)
subtype2	53	1961.6 (11.9)
subtype3	53	1960.0 (12.8)

Figure S51. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.488 (Chi-square test), Q value = 1

Table S56. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	198	4	2	13
subtype1	77	1	0	8
subtype2	65	2	1	4
subtype3	56	1	1	1

Figure S52. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S57. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	60	73	62

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0101 (logrank test), Q value = 1

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	182	75	0.0 - 173.8 (18.4)
subtype1	57	17	0.0 - 173.8 (22.9)
subtype2	69	30	0.2 - 119.5 (16.6)
subtype3	56	28	0.1 - 119.8 (14.8)

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0153 (ANOVA), Q value = 1

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	187	67.4 (9.5)
subtype1	59	65.5 (10.5)
subtype2	69	66.6 (8.5)
subtype3	59	70.3 (9.1)

Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.296 (Chi-square test), Q value = 1

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB
ALL	1	28	69	1	23	34	23	14
subtype1	0	10	17	0	7	11	7	8
subtype2	0	7	26	0	11	16	8	4
subtype3	1	11	26	1	5	7	8	2

Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.185 (Chi-square test), Q value = 1

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	44	120	20	11
subtype1	15	35	4	6
subtype2	11	51	9	2
subtype3	18	34	7	3

Figure S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.308 (Chi-square test), Q value = 1

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	124	50	16	4
subtype1	34	17	7	2
subtype2	44	22	5	2
subtype3	46	11	4	0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.612 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	176	16
subtype1	54	6
subtype2	67	4
subtype3	55	6

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.266 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	146
subtype1	12	48
subtype2	17	56
subtype3	20	42

Figure S59. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

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

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.134 (Chi-square test), Q value = 1

Table S66. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	3	2	190
subtype1	2	2	56
subtype2	0	0	73
subtype3	1	0	61

Figure S61. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

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

Table S67. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	186
subtype1	4	56
subtype2	2	71
subtype3	3	59

Figure S62. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0516 (ANOVA), Q value = 1

Table S68. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	165	51.7 (32.3)
subtype1	51	60.6 (39.7)
subtype2	62	49.3 (30.0)
subtype3	52	45.9 (25.1)

Figure S63. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.151 (ANOVA), Q value = 1

Table S69. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	131	1958.5 (12.0)
subtype1	39	1961.3 (11.6)
subtype2	50	1958.2 (11.0)
subtype3	42	1956.1 (13.3)

Figure S64. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.033 (Chi-square test), Q value = 1

Table S70. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	149	4	4	9
subtype1	43	0	2	5
subtype2	55	4	2	4
subtype3	51	0	0	0

Figure S65. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S71. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	58	48	49	40

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00999 (logrank test), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	182	75	0.0 - 173.8 (18.4)
subtype1	54	20	0.0 - 173.8 (20.0)
subtype2	44	17	0.2 - 99.2 (23.0)
subtype3	46	18	0.2 - 119.5 (17.3)
subtype4	38	20	0.1 - 92.7 (12.0)

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0759 (ANOVA), Q value = 1

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	187	67.4 (9.5)
subtype1	56	65.7 (10.6)
subtype2	46	69.4 (8.9)
subtype3	46	65.8 (7.9)
subtype4	39	69.4 (9.9)

Figure S67. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.454 (Chi-square test), Q value = 1

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB
ALL	1	28	69	1	23	34	23	14
subtype1	0	10	15	0	8	10	9	6
subtype2	1	8	19	0	3	7	6	4
subtype3	0	3	18	0	8	11	4	4
subtype4	0	7	17	1	4	6	4	0

Figure S68. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

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

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	44	120	20	11
subtype1	14	33	5	6
subtype2	16	25	5	2
subtype3	6	36	6	1
subtype4	8	26	4	2

Figure S69. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0902 (Chi-square test), Q value = 1

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

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

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	MX
ALL	176	16
subtype1	53	5
subtype2	45	3
subtype3	45	2
subtype4	33	6

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.874 (Fisher's exact test), Q value = 1

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	49	146
subtype1	15	43
subtype2	14	34
subtype3	11	38
subtype4	9	31

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

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

Table S79. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Figure S73. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0593 (Chi-square test), Q value = 1

Table S80. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	3	2	190
subtype1	3	2	53
subtype2	0	0	48
subtype3	0	0	49
subtype4	0	0	40

Figure S74. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.583 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	186
subtype1	4	54
subtype2	3	45
subtype3	1	48
subtype4	1	39

Figure S75. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.105 (ANOVA), Q value = 1

Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	165	51.7 (32.3)
subtype1	50	61.0 (44.8)
subtype2	40	49.0 (25.0)
subtype3	42	47.4 (24.2)
subtype4	33	46.3 (24.0)

Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.262 (ANOVA), Q value = 1

Table S83. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	131	1958.5 (12.0)
subtype1	39	1959.2 (10.8)
subtype2	33	1958.5 (13.9)
subtype3	35	1960.5 (11.6)
subtype4	24	1954.3 (11.6)

Figure S77. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.0213 (Chi-square test), Q value = 1

Table S84. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	149	4	4	9
subtype1	47	0	0	5
subtype2	38	0	2	0
subtype3	36	4	2	2
subtype4	28	0	0	2

Figure S78. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S85. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	93	113	114	66

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.171 (logrank test), Q value = 1

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	352	119	0.0 - 173.8 (11.6)
subtype1	82	27	0.0 - 122.4 (12.6)
subtype2	104	32	0.1 - 141.3 (15.6)
subtype3	107	35	0.0 - 173.8 (8.1)
subtype4	59	25	0.0 - 92.7 (12.2)

Figure S79. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0394 (ANOVA), Q value = 1

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	378	67.1 (8.7)
subtype1	91	66.8 (9.3)
subtype2	111	65.4 (8.3)
subtype3	111	68.1 (8.4)
subtype4	65	68.7 (8.9)

Figure S80. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.276 (Chi-square test), Q value = 1

Table S88. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	2	68	127	1	46	63	52	19	5
subtype1	1	14	35	0	7	18	13	3	1
subtype2	1	13	44	1	14	19	13	8	0
subtype3	0	22	29	0	18	20	16	6	2
subtype4	0	19	19	0	7	6	10	2	2

Figure S81. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.494 (Chi-square test), Q value = 1

Table S89. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	94	233	42	17
subtype1	18	61	11	3
subtype2	23	72	11	7
subtype3	31	64	13	6
subtype4	22	36	7	1

Figure S82. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.413 (Chi-square test), Q value = 1

Table S90. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	244	100	32	5
subtype1	57	25	8	1
subtype2	71	32	9	1
subtype3	74	33	6	1
subtype4	42	10	9	2

Figure S83. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.64 (Chi-square test), Q value = 1

Table S91. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	328	5	47
subtype1	78	1	12
subtype2	100	0	11
subtype3	96	2	15
subtype4	54	2	9

Figure S84. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.681 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	97	289
subtype1	23	70
subtype2	25	88
subtype3	29	85
subtype4	20	46

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

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

P value = 0.808 (ANOVA), Q value = 1

Table S93. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	53	27.4 (39.1)
subtype1	9	35.6 (42.2)
subtype2	13	20.8 (35.2)
subtype3	16	24.4 (38.5)
subtype4	15	31.3 (43.7)

Figure S86. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.689 (Chi-square test), Q value = 1

Table S94. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	11	5	1	369
subtype1	2	2	0	89
subtype2	4	1	0	108
subtype3	5	1	1	107
subtype4	0	1	0	65

Figure S87. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.305 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	11	375
subtype1	2	91
subtype2	1	112
subtype3	5	109
subtype4	3	63

Figure S88. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.894 (ANOVA), Q value = 1

Table S96. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	326	52.4 (31.6)
subtype1	81	52.2 (34.1)
subtype2	96	50.8 (27.4)
subtype3	93	54.3 (33.1)
subtype4	56	52.0 (33.0)

Figure S89. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.276 (ANOVA), Q value = 1

Table S97. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	254	1959.7 (11.8)
subtype1	64	1957.9 (13.2)
subtype2	67	1961.7 (11.6)
subtype3	80	1960.2 (10.2)
subtype4	43	1958.6 (12.3)

Figure S90. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.746 (Chi-square test), Q value = 1

Table S98. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	312	7	4	18
subtype1	74	2	2	3
subtype2	97	3	0	5
subtype3	90	2	1	5
subtype4	51	0	1	5

Figure S91. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S99. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	80	112	194

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.864 (logrank test), Q value = 1

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	352	119	0.0 - 173.8 (11.6)
subtype1	76	25	0.0 - 114.0 (9.5)
subtype2	102	33	0.1 - 141.3 (13.6)
subtype3	174	61	0.0 - 173.8 (12.1)

Figure S92. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0437 (ANOVA), Q value = 1

Table S101. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	378	67.1 (8.7)
subtype1	78	68.3 (7.7)
subtype2	109	65.4 (8.6)
subtype3	191	67.6 (9.1)

Figure S93. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.451 (Chi-square test), Q value = 1

Table S102. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	2	68	127	1	46	63	52	19	5
subtype1	0	18	18	0	14	10	12	5	2
subtype2	1	16	40	1	13	19	15	7	0
subtype3	1	34	69	0	19	34	25	7	3

Figure S94. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.527 (Chi-square test), Q value = 1

Table S103. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	94	233	42	17
subtype1	26	42	7	5
subtype2	25	69	13	5
subtype3	43	122	22	7

Figure S95. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.333 (Chi-square test), Q value = 1

Table S104. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	244	100	32	5
subtype1	49	26	5	0
subtype2	70	33	7	2
subtype3	125	41	20	3

Figure S96. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

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

Table S105. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	328	5	47
subtype1	68	2	10
subtype2	100	0	10
subtype3	160	3	27

Figure S97. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.144 (Fisher's exact test), Q value = 1

Table S106. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	97	289
subtype1	27	53
subtype2	25	87
subtype3	45	149

Figure S98. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.649 (ANOVA), Q value = 1

Table S107. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	53	27.4 (39.1)
subtype1	14	27.9 (40.0)
subtype2	14	19.3 (34.3)
subtype3	25	31.6 (41.9)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.069 (Chi-square test), Q value = 1

Table S108. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	11	5	1	369
subtype1	5	0	1	74
subtype2	4	1	0	107
subtype3	2	4	0	188

Figure S100. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.303 (Fisher's exact test), Q value = 1

Table S109. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	11	375
subtype1	2	78
subtype2	1	111
subtype3	8	186

Figure S101. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.652 (ANOVA), Q value = 1

Table S110. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	326	52.4 (31.6)
subtype1	69	53.6 (35.0)
subtype2	96	49.8 (28.5)
subtype3	161	53.3 (32.0)

Figure S102. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0771 (ANOVA), Q value = 1

Table S111. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	254	1959.7 (11.8)
subtype1	60	1960.0 (10.4)
subtype2	67	1962.3 (11.8)
subtype3	127	1958.3 (12.2)

Figure S103. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.327 (Chi-square test), Q value = 1

Table S112. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	312	7	4	18
subtype1	63	1	0	3
subtype2	97	3	0	3
subtype3	152	3	4	12

Figure S104. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S113. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	138	89	93	48

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.992 (logrank test), Q value = 1

Table S114. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	335	110	0.0 - 173.8 (11.7)
subtype1	131	52	0.0 - 173.8 (17.9)
subtype2	77	18	0.1 - 96.8 (5.4)
subtype3	82	25	0.0 - 107.0 (8.9)
subtype4	45	15	0.2 - 92.7 (11.7)

Figure S105. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0751 (ANOVA), Q value = 1

Table S115. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	361	67.3 (8.8)
subtype1	137	66.5 (8.4)
subtype2	88	67.5 (8.3)
subtype3	89	69.3 (9.4)
subtype4	47	66.0 (9.2)

Figure S106. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.366 (Chi-square test), Q value = 1

Table S116. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	2	63	122	1	43	63	51	17	4
subtype1	0	20	51	1	10	22	22	9	3
subtype2	1	13	27	0	14	17	14	2	0
subtype3	1	20	28	0	14	18	10	1	1
subtype4	0	10	16	0	5	6	5	5	0

Figure S107. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.228 (Chi-square test), Q value = 1

Table S117. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	86	226	40	16
subtype1	27	91	12	8
subtype2	18	56	13	2
subtype3	27	52	12	2
subtype4	14	27	3	4

Figure S108. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.307 (Chi-square test), Q value = 1

Table S118. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	229	97	33	4
subtype1	87	31	16	4
subtype2	54	29	6	0
subtype3	58	23	7	0
subtype4	30	14	4	0

Figure S109. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 4.74e-05 (Chi-square test), Q value = 0.0074

Table S119. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	312	4	46
subtype1	130	3	3
subtype2	68	0	20
subtype3	73	1	19
subtype4	41	0	4

Figure S110. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.888 (Fisher's exact test), Q value = 1

Table S120. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	92	276
subtype1	37	101
subtype2	23	66
subtype3	21	72
subtype4	11	37

Figure S111. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.094 (ANOVA), Q value = 1

Table S121. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	48	27.1 (39.0)
subtype1	25	18.8 (34.9)
subtype2	8	26.2 (39.6)
subtype3	10	54.0 (43.0)
subtype4	5	16.0 (35.8)

Figure S112. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0905 (Chi-square test), Q value = 1

Table S122. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	10	5	1	352
subtype1	1	1	0	136
subtype2	4	2	1	82
subtype3	1	2	0	90
subtype4	4	0	0	44

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

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

P value = 0.665 (Fisher's exact test), Q value = 1

Table S123. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	11	357
subtype1	4	134
subtype2	3	86
subtype3	4	89
subtype4	0	48

Figure S114. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.554 (ANOVA), Q value = 1

Table S124. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	313	53.0 (32.2)
subtype1	121	56.0 (37.1)
subtype2	73	51.5 (28.5)
subtype3	77	52.3 (29.0)
subtype4	42	48.4 (29.0)

Figure S115. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.264 (ANOVA), Q value = 1

Table S125. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	245	1959.7 (12.0)
subtype1	92	1958.8 (10.2)
subtype2	63	1961.3 (12.0)
subtype3	59	1958.1 (13.3)
subtype4	31	1962.2 (14.1)

Figure S116. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.72 (Chi-square test), Q value = 1

Table S126. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	298	6	3	16
subtype1	124	2	2	3
subtype2	66	2	1	6
subtype3	69	1	0	5
subtype4	39	1	0	2

Figure S117. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S127. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	59	116	193

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.413 (logrank test), Q value = 1

Table S128. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	335	110	0.0 - 173.8 (11.7)
subtype1	56	17	0.2 - 141.3 (10.5)
subtype2	104	31	0.0 - 114.0 (9.8)
subtype3	175	62	0.0 - 173.8 (12.2)

Figure S118. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.217 (ANOVA), Q value = 1

Table S129. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	361	67.3 (8.8)
subtype1	57	66.1 (8.9)
subtype2	115	68.4 (8.3)
subtype3	189	67.1 (9.1)

Figure S119. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0578 (Chi-square test), Q value = 1

Table S130. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	2	63	122	1	43	63	51	17	4
subtype1	0	8	21	0	7	9	6	8	0
subtype2	2	25	35	0	11	19	19	3	0
subtype3	0	30	66	1	25	35	26	6	4

Figure S120. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.563 (Chi-square test), Q value = 1

Table S131. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	86	226	40	16
subtype1	13	36	5	5
subtype2	32	67	13	4
subtype3	41	123	22	7

Figure S121. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.375 (Chi-square test), Q value = 1

Table S132. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	229	97	33	4
subtype1	33	21	4	1
subtype2	74	33	9	0
subtype3	122	43	20	3

Figure S122. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.0718 (Chi-square test), Q value = 1

Table S133. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	312	4	46
subtype1	52	0	5
subtype2	92	0	21
subtype3	168	4	20

Figure S123. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.286 (Fisher's exact test), Q value = 1

Table S134. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	92	276
subtype1	10	49
subtype2	32	84
subtype3	50	143

Figure S124. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.662 (ANOVA), Q value = 1

Table S135. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	48	27.1 (39.0)
subtype1	4	20.0 (40.0)
subtype2	13	35.4 (41.0)
subtype3	31	24.5 (38.9)

Figure S125. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.474 (Chi-square test), Q value = 1

Table S136. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	10	5	1	352
subtype1	3	0	0	56
subtype2	1	1	0	114
subtype3	6	4	1	182

Figure S126. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.00107 (Fisher's exact test), Q value = 0.16

Table S137. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	11	357
subtype1	1	58
subtype2	9	107
subtype3	1	192

Figure S127. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.793 (ANOVA), Q value = 1

Table S138. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	313	53.0 (32.2)
subtype1	53	53.6 (28.6)
subtype2	99	51.2 (26.8)
subtype3	161	53.9 (36.3)

Figure S128. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0511 (ANOVA), Q value = 1

Table S139. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	245	1959.7 (12.0)
subtype1	39	1963.9 (14.9)
subtype2	80	1958.4 (11.9)
subtype3	126	1959.2 (10.8)

Figure S129. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.882 (Chi-square test), Q value = 1

Table S140. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	298	6	3	16
subtype1	48	2	1	2
subtype2	89	1	1	5
subtype3	161	3	1	9

Figure S130. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S141. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	80	80	72

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.908 (logrank test), Q value = 1

Table S142. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	205	57	0.0 - 107.0 (6.9)
subtype1	70	23	0.0 - 96.8 (10.1)
subtype2	68	17	0.0 - 107.0 (3.9)
subtype3	67	17	0.0 - 71.8 (10.2)

Figure S131. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.129 (ANOVA), Q value = 1

Table S143. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	227	67.4 (9.0)
subtype1	79	65.9 (9.3)
subtype2	78	67.6 (9.5)
subtype3	70	68.8 (7.9)

Figure S132. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0159 (Chi-square test), Q value = 1

Table S144. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	2	45	69	1	37	38	32	5	1
subtype1	1	7	25	0	13	18	13	2	0
subtype2	1	12	24	1	13	15	11	3	0
subtype3	0	26	20	0	11	5	8	0	1

Figure S133. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00297 (Chi-square test), Q value = 0.45

Table S145. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	60	137	30	5
subtype1	12	55	12	1
subtype2	18	46	12	4
subtype3	30	36	6	0

Figure S134. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

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

Table S146. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	147	60	20
subtype1	46	27	7
subtype2	50	22	6
subtype3	51	11	7

Figure S135. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.665 (Chi-square test), Q value = 1

Table S147. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	183	1	46
subtype1	64	0	15
subtype2	64	0	16
subtype3	55	1	15

Figure S136. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.98 (Fisher's exact test), Q value = 1

Table S148. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	53	179
subtype1	18	62
subtype2	19	61
subtype3	16	56

Figure S137. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.76 (ANOVA), Q value = 1

Table S149. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	28	32.5 (41.1)
subtype1	12	31.7 (41.5)
subtype2	9	40.0 (43.6)
subtype3	7	24.3 (41.6)

Figure S138. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.261 (Chi-square test), Q value = 1

Table S150. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	7	4	1	220
subtype1	5	1	1	73
subtype2	0	2	0	78
subtype3	2	1	0	69

Figure S139. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.637 (Fisher's exact test), Q value = 1

Table S151. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	223
subtype1	3	77
subtype2	2	78
subtype3	4	68

Figure S140. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.927 (ANOVA), Q value = 1

Table S152. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	194	50.6 (28.1)
subtype1	68	49.6 (25.4)
subtype2	63	51.5 (26.8)
subtype3	63	50.9 (32.2)

Figure S141. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.272 (ANOVA), Q value = 1

Table S153. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	157	1960.9 (12.6)
subtype1	55	1962.8 (12.5)
subtype2	55	1958.9 (13.9)
subtype3	47	1961.2 (11.1)

Figure S142. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.477 (Chi-square test), Q value = 1

Table S154. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	174	4	2	13
subtype1	65	1	0	5
subtype2	57	2	0	5
subtype3	52	1	2	3

Figure S143. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S155. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2
Number of samples	133	99

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.875 (logrank test), Q value = 1

Table S156. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	205	57	0.0 - 107.0 (6.9)
subtype1	117	29	0.0 - 107.0 (6.5)
subtype2	88	28	0.0 - 96.8 (9.9)

Figure S144. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.648 (t-test), Q value = 1

Table S157. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	227	67.4 (9.0)
subtype1	129	67.6 (9.4)
subtype2	98	67.1 (8.4)

Figure S145. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.794 (Chi-square test), Q value = 1

Table S158. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	2	45	69	1	37	38	32	5	1
subtype1	1	30	40	1	21	21	15	3	1
subtype2	1	15	29	0	16	17	17	2	0

Figure S146. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.277 (Chi-square test), Q value = 1

Table S159. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	60	137	30	5
subtype1	39	72	18	4
subtype2	21	65	12	1

Figure S147. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.661 (Chi-square test), Q value = 1

Table S160. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	147	60	20
subtype1	86	32	10
subtype2	61	28	10

Figure S148. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0664 (Chi-square test), Q value = 1

Table S161. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	183	1	46
subtype1	99	1	33
subtype2	84	0	13

Figure S149. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.433 (Fisher's exact test), Q value = 1

Table S162. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	53	179
subtype1	33	100
subtype2	20	79

Figure S150. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0888 (t-test), Q value = 1

Table S163. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	28	32.5 (41.1)
subtype1	11	50.0 (44.9)
subtype2	17	21.2 (35.2)

Figure S151. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.232 (Chi-square test), Q value = 1

Table S164. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG BASALOID SQUAMOUS CELL CARCINOMA	LUNG PAPILLARY SQUAMOUS CELL CARICNOMA	LUNG SMALL CELL SQUAMOUS CELL CARCINOMA	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL	7	4	1	220
subtype1	2	3	0	128
subtype2	5	1	1	92

Figure S152. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.502 (Fisher's exact test), Q value = 1

Table S165. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	223
subtype1	4	129
subtype2	5	94

Figure S153. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.476 (t-test), Q value = 1

Table S166. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	194	50.6 (28.1)
subtype1	110	51.9 (29.0)
subtype2	84	49.0 (26.8)

Figure S154. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.114 (t-test), Q value = 1

Table S167. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	157	1960.9 (12.6)
subtype1	89	1959.6 (12.5)
subtype2	68	1962.8 (12.7)

Figure S155. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.494 (Chi-square test), Q value = 1

Table S168. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	174	4	2	13
subtype1	95	2	0	7
subtype2	79	2	2	6

Figure S156. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Methods & Data

Input

Cluster data file = LUSC-TP.mergedcluster.txt
Clinical data file = LUSC-TP.clin.merged.picked.txt
Number of patients = 394
Number of clustering approaches = 12
Number of selected clinical features = 13
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

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

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

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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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

[6] 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)

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

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

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