Correlation between aggregated molecular cancer subtypes and selected clinical features

Lung Squamous Cell Carcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_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/C1MS3QSQ

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 347 patients, one significant finding 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 4 subtypes that do not correlate to any clinical features.
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.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'DISTANT.METASTASIS'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 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, one significant finding 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	0.428 (1.00)	0.383 (1.00)	0.152 (1.00)	0.279 (1.00)	0.00491 (0.697)	0.00582 (0.821)	0.317 (1.00)	0.673 (1.00)	0.832 (1.00)	0.461 (1.00)	0.363 (1.00)	0.418 (1.00)
AGE	ANOVA	0.962 (1.00)	0.438 (1.00)	0.19 (1.00)	0.292 (1.00)	0.0153 (1.00)	0.0759 (1.00)	0.0877 (1.00)	0.0903 (1.00)	0.302 (1.00)	0.179 (1.00)	0.246 (1.00)	0.558 (1.00)
GENDER	Fisher's exact test	0.331 (1.00)	0.00305 (0.436)	0.0125 (1.00)	0.781 (1.00)	0.266 (1.00)	0.874 (1.00)	0.435 (1.00)	0.105 (1.00)	0.202 (1.00)	1 (1.00)	0.547 (1.00)	0.412 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA	0.191 (1.00)	0.108 (1.00)	0.479 (1.00)	0.234 (1.00)	0.527 (1.00)	0.918 (1.00)	0.756 (1.00)	0.84 (1.00)	0.0204 (1.00)	0.773 (1.00)	0.249 (1.00)	0.15 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.32 (1.00)	0.264 (1.00)	0.296 (1.00)	0.313 (1.00)	0.134 (1.00)	0.0593 (1.00)	0.452 (1.00)	0.0338 (1.00)	0.689 (1.00)	0.647 (1.00)	0.705 (1.00)	0.497 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.55 (1.00)	0.183 (1.00)	0.578 (1.00)	0.604 (1.00)	0.524 (1.00)	0.583 (1.00)	0.414 (1.00)	0.525 (1.00)	0.718 (1.00)	0.253 (1.00)	0.524 (1.00)	0.151 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.525 (1.00)	0.822 (1.00)	0.197 (1.00)	0.944 (1.00)	0.0516 (1.00)	0.105 (1.00)	0.718 (1.00)	0.606 (1.00)	0.164 (1.00)	0.8 (1.00)	0.365 (1.00)	0.763 (1.00)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.903 (1.00)	0.0465 (1.00)	0.427 (1.00)	0.626 (1.00)	0.151 (1.00)	0.262 (1.00)	0.2 (1.00)	0.104 (1.00)	0.373 (1.00)	0.767 (1.00)	0.0969 (1.00)	0.117 (1.00)
DISTANT METASTASIS	Fisher's exact test	0.105 (1.00)	0.0642 (1.00)	0.847 (1.00)	0.381 (1.00)	0.612 (1.00)	0.304 (1.00)	0.302 (1.00)	0.288 (1.00)	3.05e-08 (4.39e-06)	0.503 (1.00)	0.364 (1.00)	0.155 (1.00)
LYMPH NODE METASTASIS	Chi-square test	0.877 (1.00)	0.427 (1.00)	0.3 (1.00)	0.628 (1.00)	0.32 (1.00)	0.111 (1.00)	0.237 (1.00)	0.58 (1.00)	0.182 (1.00)	0.676 (1.00)	0.21 (1.00)	0.282 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.546 (1.00)	0.384 (1.00)	0.832 (1.00)	0.386 (1.00)	0.0396 (1.00)	0.0248 (1.00)	0.588 (1.00)	0.35 (1.00)	0.0396 (1.00)	0.273 (1.00)	0.107 (1.00)	0.0595 (1.00)
TUMOR STAGECODE	ANOVA
NEOPLASM DISEASESTAGE	Chi-square test	0.0228 (1.00)	0.00998 (1.00)	0.797 (1.00)	0.445 (1.00)	0.315 (1.00)	0.409 (1.00)	0.349 (1.00)	0.43 (1.00)	0.0104 (1.00)	0.05 (1.00)	0.0821 (1.00)	0.352 (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	42	52	32	28

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.428 (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	62	0.4 - 173.8 (17.5)
subtype1	41	16	0.4 - 122.4 (19.0)
subtype2	50	19	0.4 - 99.2 (25.0)
subtype3	30	17	0.4 - 173.8 (15.9)
subtype4	27	10	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 'GENDER'

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

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

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

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

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

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

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: '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 S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: '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 S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: '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 S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: '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 S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'mRNA CNMF subtypes' versus 'DISTANT.METASTASIS'

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

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

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

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

'mRNA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

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

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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 S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

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

Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: '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 S11. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S14. Get Full Table 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 = 0.383 (logrank test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	62	0.4 - 173.8 (17.5)
subtype1	22	6	0.4 - 82.2 (14.3)
subtype2	54	21	0.4 - 99.2 (23.6)
subtype3	22	10	1.0 - 122.4 (19.7)
subtype4	50	25	0.4 - 173.8 (13.1)

Figure S13. 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 S16. 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 S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00305 (Fisher's exact test), Q value = 0.44

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

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

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: '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 S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: '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 S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: '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 S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: '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 S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'mRNA cHierClus subtypes' versus 'DISTANT.METASTASIS'

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

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

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

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

'mRNA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

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

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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 S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

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

Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: '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 S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: '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 S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

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

Table S27. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	121	104	120

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

P value = 0.152 (logrank test), Q value = 1

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	325	122	0.0 - 173.8 (11.8)
subtype1	112	52	0.1 - 173.8 (12.9)
subtype2	100	30	0.2 - 114.0 (13.6)
subtype3	113	40	0.0 - 122.4 (9.3)

Figure S25. 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 = 0.19 (ANOVA), Q value = 1

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	337	67.3 (8.8)
subtype1	118	67.2 (9.8)
subtype2	101	66.1 (8.1)
subtype3	118	68.3 (8.1)

Figure S26. 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 = 0.0125 (Fisher's exact test), Q value = 1

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	83	262
subtype1	39	82
subtype2	16	88
subtype3	28	92

Figure S27. 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 = 0.479 (ANOVA), Q value = 1

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	54	28.5 (39.7)
subtype1	20	35.5 (43.8)
subtype2	11	17.3 (32.9)
subtype3	23	27.8 (39.2)

Figure S28. 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 = 0.296 (Chi-square test), Q value = 1

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: '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	2	1	335
subtype1	0	1	1	119
subtype2	4	1	0	99
subtype3	3	0	0	117

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.578 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	12	333
subtype1	6	115
subtype2	3	101
subtype3	3	117

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.197 (ANOVA), Q value = 1

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	293	52.5 (32.3)
subtype1	100	49.1 (32.4)
subtype2	90	51.2 (26.1)
subtype3	103	57.0 (36.5)

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.427 (ANOVA), Q value = 1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	234	1959.3 (11.8)
subtype1	86	1958.3 (12.6)
subtype2	62	1960.9 (11.0)
subtype3	86	1959.3 (11.4)

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.847 (Chi-square test), Q value = 1

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	293	4	42
subtype1	101	1	17
subtype2	88	2	12
subtype3	104	1	13

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

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

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	216	94	28	5	2
subtype1	81	24	12	3	1
subtype2	58	37	7	1	1
subtype3	77	33	9	1	0

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

P value = 0.832 (Chi-square test), Q value = 1

Table S38. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	270	6	4	17
subtype1	92	2	2	8
subtype2	82	2	1	6
subtype3	96	2	1	3

Figure S35. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.797 (Chi-square test), Q value = 1

Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	56	117	1	37	61	45	20	4
subtype1	21	42	0	15	19	15	6	1
subtype2	11	36	1	13	19	14	8	2
subtype3	24	39	0	9	23	16	6	1

Figure S36. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	76	71	56

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.279 (logrank test), Q value = 1

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	190	71	0.0 - 173.8 (10.8)
subtype1	71	31	0.1 - 173.8 (9.5)
subtype2	66	23	0.2 - 141.3 (14.3)
subtype3	53	17	0.0 - 107.0 (9.6)

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.292 (ANOVA), Q value = 1

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	195	68.3 (8.8)
subtype1	73	69.3 (9.5)
subtype2	68	67.0 (7.7)
subtype3	54	68.4 (9.1)

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.781 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	155
subtype1	20	56
subtype2	15	56
subtype3	13	43

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.234 (ANOVA), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	31	29.4 (40.2)
subtype1	5	42.0 (49.2)
subtype2	14	15.7 (33.4)
subtype3	12	40.0 (42.0)

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.313 (Chi-square test), Q value = 1

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: '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	4	1	1	197
subtype1	0	0	0	76
subtype2	2	1	0	68
subtype3	2	0	1	53

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.604 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	10	193
subtype1	5	71
subtype2	2	69
subtype3	3	53

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.944 (ANOVA), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	171	50.6 (29.7)
subtype1	64	50.2 (30.3)
subtype2	58	50.1 (23.6)
subtype3	49	51.8 (35.3)

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.626 (ANOVA), Q value = 1

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	142	1959.2 (11.9)
subtype1	53	1958.0 (11.1)
subtype2	46	1960.3 (12.2)
subtype3	43	1959.6 (12.5)

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.381 (Chi-square test), Q value = 1

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	163	1	37
subtype1	57	1	18
subtype2	61	0	10
subtype3	45	0	9

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.628 (Chi-square test), Q value = 1

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	NX
ALL	127	58	16	2
subtype1	46	20	9	1
subtype2	47	21	3	0
subtype3	34	17	4	1

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

P value = 0.386 (Chi-square test), Q value = 1

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	142	3	2	11
subtype1	50	0	0	6
subtype2	58	1	1	3
subtype3	34	2	1	2

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.445 (Chi-square test), Q value = 1

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	1	41	60	1	27	36	28	5	1
subtype1	0	17	20	0	9	16	12	0	1
subtype2	1	12	25	1	13	8	9	2	0
subtype3	0	12	15	0	5	12	7	3	0

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S53. Get Full Table 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 = 0.00491 (logrank test), Q value = 0.7

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	182	73	0.0 - 173.8 (16.2)
subtype1	57	16	0.0 - 173.8 (22.9)
subtype2	69	30	0.2 - 115.6 (14.1)
subtype3	56	27	0.1 - 119.8 (12.4)

Figure S49. 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 S55. 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 S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

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

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S51. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

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

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: '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 S52. 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 = 0.134 (Chi-square test), Q value = 1

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: '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 S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: '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 S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: '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 S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

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

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

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

Figure S57. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'RPPA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

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

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	124	50	16	4	1
subtype1	34	17	7	2	0
subtype2	44	22	5	2	0
subtype3	46	11	4	0	1

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0396 (Chi-square test), Q value = 1

Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	145	4	4	9
subtype1	42	0	2	5
subtype2	55	4	2	4
subtype3	48	0	0	0

Figure S59. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.315 (Chi-square test), Q value = 1

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB
ALL	1	27	70	1	21	35	23	14
subtype1	0	10	17	0	7	11	7	8
subtype2	0	7	26	0	9	17	8	4
subtype3	1	10	27	1	5	7	8	2

Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S66. Get Full Table 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 = 0.00582 (logrank test), Q value = 0.82

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	182	73	0.0 - 173.8 (16.2)
subtype1	54	20	0.0 - 173.8 (16.9)
subtype2	44	15	0.2 - 99.2 (23.0)
subtype3	46	18	0.2 - 115.6 (11.9)
subtype4	38	20	0.1 - 82.2 (10.2)

Figure S61. 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 S68. 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 S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

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

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S63. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

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

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: '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 S64. 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 = 0.0593 (Chi-square test), Q value = 1

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: '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 S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: '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 S67. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

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

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: '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 S68. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

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

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

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

Figure S69. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'RPPA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.111 (Chi-square test), Q value = 1

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0248 (Chi-square test), Q value = 1

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	145	4	4	9
subtype1	46	0	0	5
subtype2	37	0	2	0
subtype3	36	4	2	2
subtype4	26	0	0	2

Figure S71. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.409 (Chi-square test), Q value = 1

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB
ALL	1	27	70	1	21	35	23	14
subtype1	0	10	15	0	8	10	9	6
subtype2	1	7	20	0	3	7	6	4
subtype3	0	3	18	0	7	12	4	4
subtype4	0	7	17	1	3	6	4	0

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S79. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	71	97	100	53

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.317 (logrank test), Q value = 1

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	304	118	0.0 - 173.8 (12.2)
subtype1	69	27	0.0 - 122.4 (12.0)
subtype2	92	33	0.2 - 141.3 (21.9)
subtype3	95	33	0.0 - 114.0 (7.3)
subtype4	48	25	0.4 - 173.8 (20.1)

Figure S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0877 (ANOVA), Q value = 1

Table S81. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	313	67.4 (8.7)
subtype1	70	66.6 (9.3)
subtype2	95	66.0 (8.0)
subtype3	97	68.8 (8.0)
subtype4	51	68.4 (9.9)

Figure S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.435 (Fisher's exact test), Q value = 1

Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	79	242
subtype1	15	56
subtype2	20	77
subtype3	29	71
subtype4	15	38

Figure S75. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.756 (ANOVA), Q value = 1

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	53	27.4 (39.1)
subtype1	6	40.0 (43.8)
subtype2	13	20.8 (35.2)
subtype3	19	24.7 (38.4)
subtype4	15	31.3 (43.7)

Figure S76. 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 = 0.452 (Chi-square test), Q value = 1

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: '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	2	1	311
subtype1	0	1	0	70
subtype2	3	1	0	93
subtype3	4	0	1	95
subtype4	0	0	0	53

Figure S77. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.414 (Fisher's exact test), Q value = 1

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	10	311
subtype1	2	69
subtype2	1	96
subtype3	4	96
subtype4	3	50

Figure S78. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.718 (ANOVA), Q value = 1

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	270	52.6 (32.4)
subtype1	62	50.2 (34.8)
subtype2	83	51.3 (27.6)
subtype3	80	56.0 (34.4)
subtype4	45	52.2 (34.2)

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

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.2 (ANOVA), Q value = 1

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	212	1958.9 (11.5)
subtype1	48	1958.0 (13.3)
subtype2	57	1961.5 (12.3)
subtype3	70	1958.4 (10.0)
subtype4	37	1956.8 (9.7)

Figure S80. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.302 (Chi-square test), Q value = 1

Table S88. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	279	4	32
subtype1	60	0	9
subtype2	89	0	6
subtype3	85	3	11
subtype4	45	1	6

Figure S81. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.237 (Chi-square test), Q value = 1

Table S89. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	200	89	25	5	2
subtype1	43	19	6	2	1
subtype2	59	31	6	1	0
subtype3	62	32	5	1	0
subtype4	36	7	8	1	1

Figure S82. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

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

Table S90. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	256	6	4	16
subtype1	53	2	2	3
subtype2	84	2	0	4
subtype3	80	2	1	4
subtype4	39	0	1	5

Figure S83. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.349 (Chi-square test), Q value = 1

Table S91. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	1	53	110	1	29	58	42	19	4
subtype1	0	8	29	0	4	13	11	5	0
subtype2	1	10	39	1	10	18	11	7	0
subtype3	0	21	26	0	10	20	12	6	3
subtype4	0	14	16	0	5	7	8	1	1

Figure S84. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S92. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	102	63	156

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.673 (logrank test), Q value = 1

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	304	118	0.0 - 173.8 (12.2)
subtype1	96	34	0.2 - 141.3 (14.3)
subtype2	62	20	0.0 - 114.0 (7.2)
subtype3	146	64	0.0 - 173.8 (16.1)

Figure S85. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0903 (ANOVA), Q value = 1

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	313	67.4 (8.7)
subtype1	99	66.0 (8.3)
subtype2	62	68.9 (7.8)
subtype3	152	67.7 (9.2)

Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

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

Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	79	242
subtype1	19	83
subtype2	21	42
subtype3	39	117

Figure S87. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.84 (ANOVA), Q value = 1

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	53	27.4 (39.1)
subtype1	15	23.3 (36.6)
subtype2	15	26.0 (39.2)
subtype3	23	30.9 (42.0)

Figure S88. 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 = 0.0338 (Chi-square test), Q value = 1

Table S97. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: '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	2	1	311
subtype1	3	1	0	98
subtype2	4	0	1	58
subtype3	0	1	0	155

Figure S89. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.525 (Fisher's exact test), Q value = 1

Table S98. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	10	311
subtype1	2	100
subtype2	1	62
subtype3	7	149

Figure S90. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.606 (ANOVA), Q value = 1

Table S99. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	270	52.6 (32.4)
subtype1	87	50.7 (28.1)
subtype2	51	56.4 (37.6)
subtype3	132	52.4 (33.0)

Figure S91. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.104 (ANOVA), Q value = 1

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	212	1958.9 (11.5)
subtype1	58	1961.3 (12.0)
subtype2	48	1959.2 (11.0)
subtype3	106	1957.4 (11.2)

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

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.288 (Chi-square test), Q value = 1

Table S101. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	279	4	32
subtype1	93	0	7
subtype2	53	2	8
subtype3	133	2	17

Figure S93. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

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

Table S102. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	200	89	25	5	2
subtype1	62	33	5	2	0
subtype2	40	18	5	0	0
subtype3	98	38	15	3	2

Figure S94. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

P value = 0.35 (Chi-square test), Q value = 1

Table S103. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	256	6	4	16
subtype1	89	2	1	3
subtype2	52	0	0	2
subtype3	115	4	3	11

Figure S95. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.43 (Chi-square test), Q value = 1

Table S104. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	1	53	110	1	29	58	42	19	4
subtype1	1	12	40	1	9	19	13	7	0
subtype2	0	14	15	0	8	8	10	4	2
subtype3	0	27	55	0	12	31	19	8	2

Figure S96. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S105. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	131	128	55

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.832 (logrank test), Q value = 1

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	297	110	0.0 - 173.8 (12.0)
subtype1	126	53	0.0 - 173.8 (16.1)
subtype2	118	38	0.0 - 107.0 (6.8)
subtype3	53	19	0.2 - 99.2 (21.0)

Figure S97. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.302 (ANOVA), Q value = 1

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	307	67.8 (8.6)
subtype1	130	67.0 (8.6)
subtype2	123	68.0 (8.8)
subtype3	54	69.1 (8.3)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.202 (Fisher's exact test), Q value = 1

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	77	237
subtype1	39	92
subtype2	27	101
subtype3	11	44

Figure S99. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0204 (ANOVA), Q value = 1

Table S109. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	48	27.1 (39.0)
subtype1	27	14.4 (31.7)
subtype2	13	50.0 (40.8)
subtype3	8	32.5 (45.0)

Figure S100. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

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

Table S110. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: '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	5	2	1	306
subtype1	1	1	0	129
subtype2	2	1	1	124
subtype3	2	0	0	53

Figure S101. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.718 (Fisher's exact test), Q value = 1

Table S111. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	11	303
subtype1	4	127
subtype2	6	122
subtype3	1	54

Figure S102. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

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

Table S112. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	267	53.5 (33.2)
subtype1	116	57.6 (38.4)
subtype2	103	49.1 (28.5)
subtype3	48	53.1 (27.7)

Figure S103. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.373 (ANOVA), Q value = 1

Table S113. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	212	1958.8 (11.5)
subtype1	87	1957.6 (10.1)
subtype2	90	1959.5 (11.8)
subtype3	35	1960.4 (13.7)

Figure S104. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 3.05e-08 (Chi-square test), Q value = 4.4e-06

Table S114. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	266	3	39
subtype1	125	3	1
subtype2	94	0	33
subtype3	47	0	5

Figure S105. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.182 (Chi-square test), Q value = 1

Table S115. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	194	86	28	4	2
subtype1	81	32	15	3	0
subtype2	75	39	12	0	2
subtype3	38	15	1	1	0

Figure S106. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0396 (Chi-square test), Q value = 1

Table S116. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	246	5	3	15
subtype1	119	1	2	3
subtype2	80	2	1	11
subtype3	47	2	0	1

Figure S107. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0104 (Chi-square test), Q value = 1

Table S117. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	1	52	106	1	31	57	43	17	3
subtype1	0	19	50	0	6	22	23	8	3
subtype2	1	23	34	1	21	25	18	3	0
subtype3	0	10	22	0	4	10	2	6	0

Figure S108. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S118. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	92	160	62

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.461 (logrank test), Q value = 1

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	297	110	0.0 - 173.8 (12.0)
subtype1	88	34	0.0 - 114.0 (15.9)
subtype2	150	57	0.0 - 173.8 (11.3)
subtype3	59	19	0.2 - 141.3 (13.6)

Figure S109. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.179 (ANOVA), Q value = 1

Table S120. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	307	67.8 (8.6)
subtype1	89	68.8 (8.1)
subtype2	158	66.9 (8.9)
subtype3	60	68.5 (8.5)

Figure S110. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S121. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	77	237
subtype1	23	69
subtype2	39	121
subtype3	15	47

Figure S111. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

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

P value = 0.773 (ANOVA), Q value = 1

Table S122. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	48	27.1 (39.0)
subtype1	15	26.0 (39.1)
subtype2	28	29.6 (40.5)
subtype3	5	16.0 (35.8)

Figure S112. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.647 (Chi-square test), Q value = 1

Table S123. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: '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	5	2	1	306
subtype1	1	0	0	91
subtype2	2	2	1	155
subtype3	2	0	0	60

Figure S113. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.253 (Fisher's exact test), Q value = 1

Table S124. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	11	303
subtype1	5	87
subtype2	3	157
subtype3	3	59

Figure S114. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.8 (ANOVA), Q value = 1

Table S125. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	267	53.5 (33.2)
subtype1	81	51.5 (31.3)
subtype2	133	54.3 (36.2)
subtype3	53	54.8 (28.0)

Figure S115. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.767 (ANOVA), Q value = 1

Table S126. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	212	1958.8 (11.5)
subtype1	66	1958.1 (12.0)
subtype2	107	1958.9 (10.1)
subtype3	39	1959.8 (14.0)

Figure S116. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.503 (Chi-square test), Q value = 1

Table S127. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	266	3	39
subtype1	79	0	12
subtype2	134	3	21
subtype3	53	0	6

Figure S117. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.676 (Chi-square test), Q value = 1

Table S128. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	194	86	28	4	2
subtype1	58	25	8	1	0
subtype2	96	42	18	2	2
subtype3	40	19	2	1	0

Figure S118. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

P value = 0.273 (Chi-square test), Q value = 1

Table S129. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	246	5	3	15
subtype1	72	1	0	4
subtype2	124	2	1	10
subtype3	50	2	2	1

Figure S119. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.05 (Chi-square test), Q value = 1

Table S130. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	1	52	106	1	31	57	43	17	3
subtype1	1	21	27	0	8	15	16	4	0
subtype2	0	20	56	1	18	30	25	5	3
subtype3	0	11	23	0	5	12	2	8	0

Figure S120. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S131. Get Full Table Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	93	55	30

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

P value = 0.363 (logrank test), Q value = 1

Table S132. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	167	58	0.0 - 107.0 (7.3)
subtype1	87	34	0.0 - 107.0 (6.5)
subtype2	51	14	0.0 - 86.8 (12.2)
subtype3	29	10	0.2 - 73.1 (2.8)

Figure S121. 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 = 0.246 (ANOVA), Q value = 1

Table S133. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	173	68.2 (8.7)
subtype1	90	67.2 (8.4)
subtype2	53	69.7 (8.4)
subtype3	30	68.2 (9.6)

Figure S122. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.547 (Fisher's exact test), Q value = 1

Table S134. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	38	140
subtype1	21	72
subtype2	13	42
subtype3	4	26

Figure S123. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.249 (ANOVA), Q value = 1

Table S135. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	28	32.5 (41.1)
subtype1	15	36.0 (41.7)
subtype2	6	48.3 (46.7)
subtype3	7	11.4 (30.2)

Figure S124. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.705 (Chi-square test), Q value = 1

Table S136. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: '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	2	1	1	174
subtype1	1	1	1	90
subtype2	0	0	0	55
subtype3	1	0	0	29

Figure S125. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

Table S137. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	169
subtype1	3	90
subtype2	4	51
subtype3	2	28

Figure S126. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

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

Table S138. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	148	50.8 (28.7)
subtype1	77	47.7 (26.6)
subtype2	45	55.2 (32.9)
subtype3	26	52.4 (26.9)

Figure S127. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0969 (ANOVA), Q value = 1

Table S139. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	124	1959.8 (12.1)
subtype1	67	1961.4 (11.2)
subtype2	37	1956.2 (10.7)
subtype3	20	1961.0 (15.8)

Figure S128. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'MIRseq Mature CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.364 (Fisher's exact test), Q value = 1

Table S140. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

nPatients	M0	MX
ALL	137	39
subtype1	72	20
subtype2	40	15
subtype3	25	4

Figure S129. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.21 (Chi-square test), Q value = 1

Table S141. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	NX
ALL	112	49	15	2
subtype1	51	30	11	1
subtype2	39	11	4	1
subtype3	22	8	0	0

Figure S130. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

P value = 0.107 (Chi-square test), Q value = 1

Table S142. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	122	3	2	12
subtype1	63	2	0	5
subtype2	38	0	0	4
subtype3	21	1	2	3

Figure S131. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0821 (Chi-square test), Q value = 1

Table S143. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB
ALL	1	34	53	1	25	32	24	5
subtype1	0	15	21	1	17	20	16	2
subtype2	0	14	21	0	3	8	7	1
subtype3	1	5	11	0	5	4	1	2

Figure S132. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S144. Get Full Table Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	67	31	80

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

P value = 0.418 (logrank test), Q value = 1

Table S145. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	167	58	0.0 - 107.0 (7.3)
subtype1	60	15	0.0 - 107.0 (8.9)
subtype2	31	9	0.2 - 73.1 (2.0)
subtype3	76	34	0.0 - 104.1 (8.2)

Figure S133. 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 = 0.558 (ANOVA), Q value = 1

Table S146. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	173	68.2 (8.7)
subtype1	65	67.3 (9.3)
subtype2	31	69.3 (10.3)
subtype3	77	68.4 (7.3)

Figure S134. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.412 (Fisher's exact test), Q value = 1

Table S147. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	38	140
subtype1	14	53
subtype2	4	27
subtype3	20	60

Figure S135. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.15 (ANOVA), Q value = 1

Table S148. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	28	32.5 (41.1)
subtype1	9	54.4 (39.7)
subtype2	4	20.0 (40.0)
subtype3	15	22.7 (39.4)

Figure S136. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S149. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: '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	2	1	1	174
subtype1	0	1	0	66
subtype2	0	0	0	31
subtype3	2	0	1	77

Figure S137. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.151 (Fisher's exact test), Q value = 1

Table S150. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	169
subtype1	1	66
subtype2	3	28
subtype3	5	75

Figure S138. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.763 (ANOVA), Q value = 1

Table S151. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	148	50.8 (28.7)
subtype1	51	49.4 (30.8)
subtype2	27	54.4 (32.1)
subtype3	70	50.5 (25.9)

Figure S139. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.117 (ANOVA), Q value = 1

Table S152. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	124	1959.8 (12.1)
subtype1	43	1959.6 (10.1)
subtype2	21	1964.6 (16.8)
subtype3	60	1958.2 (11.2)

Figure S140. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'YEAROFTOBACCOSMOKINGONSET'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.155 (Fisher's exact test), Q value = 1

Table S153. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

nPatients	M0	MX
ALL	137	39
subtype1	47	20
subtype2	24	6
subtype3	66	13

Figure S141. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'DISTANT.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.282 (Chi-square test), Q value = 1

Table S154. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	NX
ALL	112	49	15	2
subtype1	42	16	7	2
subtype2	22	9	0	0
subtype3	48	24	8	0

Figure S142. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0595 (Chi-square test), Q value = 1

Table S155. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	122	3	2	12
subtype1	41	1	0	7
subtype2	22	1	2	2
subtype3	59	1	0	3

Figure S143. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.352 (Chi-square test), Q value = 1

Table S156. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB
ALL	1	34	53	1	25	32	24	5
subtype1	0	10	22	1	12	12	7	2
subtype2	0	5	12	0	4	6	1	2
subtype3	1	19	19	0	9	14	16	1

Figure S144. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = LUSC-TP.mergedcluster.txt
Clinical data file = LUSC-TP.clin.merged.picked.txt
Number of patients = 347
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

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

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

[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

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

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

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