Correlation between aggregated molecular cancer subtypes and selected clinical features

Kidney Renal Clear Cell Carcinoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

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 (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DF6QDM

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 12 clinical features across 537 patients, 72 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', and 'RACE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'RACE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'RACE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'RACE'.
CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', and 'PATHOLOGY_M_STAGE'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'RACE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', and 'GENDER'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'ETHNICITY'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'GENDER', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE', 'GENDER', and 'RACE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 72 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	PATHOLOGIC STAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE	NUMBER PACK YEARS SMOKED	YEAR OF TOBACCO SMOKING ONSET	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Kruskal-Wallis (anova)	Kruskal-Wallis (anova)	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.118 (0.195)	0.873 (0.989)	0.00624 (0.017)	0.00823 (0.0215)	0.0701 (0.133)	0.121 (0.198)	0.635 (0.802)				0.00755 (0.0201)	0.313 (0.434)
mRNA cHierClus subtypes	0.0315 (0.0676)	0.168 (0.268)	0.00058 (0.00209)	0.00383 (0.011)	0.0981 (0.174)	0.0104 (0.0267)	6e-05 (0.000262)				2e-05 (0.00012)	0.474 (0.645)
Copy Number Ratio CNMF subtypes	0.000279 (0.00112)	0.0574 (0.113)	2e-05 (0.00012)	5e-05 (0.000232)	0.00383 (0.011)	4e-05 (0.000199)	0.00606 (0.0168)	0.173 (0.271)	0.909 (1.00)	0.83 (0.972)	0.011 (0.0274)	0.787 (0.937)
METHLYATION CNMF	1.25e-05 (8.97e-05)	0.0123 (0.03)	1e-05 (7.58e-05)	1e-05 (7.58e-05)	0.0463 (0.0927)	8e-05 (0.000339)	2e-05 (0.00012)	0.23 (0.338)	0.968 (1.00)	0.849 (0.986)	0.0388 (0.0799)	1 (1.00)
RPPA CNMF subtypes	1.36e-08 (7.14e-07)	0.0884 (0.159)	1e-05 (7.58e-05)	1e-05 (7.58e-05)	0.0211 (0.0483)	1e-05 (7.58e-05)	0.195 (0.295)	0.281 (0.393)	0.545 (0.72)		0.574 (0.745)	0.568 (0.744)
RPPA cHierClus subtypes	1.19e-09 (1.71e-07)	0.000705 (0.00247)	1e-05 (7.58e-05)	1e-05 (7.58e-05)	0.0458 (0.0927)	5e-05 (0.000232)	0.0659 (0.126)	0.0211 (0.0483)	0.711 (0.868)		0.0261 (0.0587)	0.125 (0.202)
RNAseq CNMF subtypes	1.03e-06 (3.7e-05)	0.113 (0.19)	1e-05 (7.58e-05)	1e-05 (7.58e-05)	0.00311 (0.00933)	0.00048 (0.00182)	3e-05 (0.00016)	0.65 (0.807)	0.925 (1.00)	0.731 (0.884)	0.0106 (0.0267)	0.103 (0.181)
RNAseq cHierClus subtypes	1.49e-08 (7.14e-07)	0.11 (0.189)	1e-05 (7.58e-05)	1e-05 (7.58e-05)	0.00058 (0.00209)	3e-05 (0.00016)	1e-05 (7.58e-05)	0.0353 (0.0748)	0.486 (0.654)	0.525 (0.7)	0.00106 (0.00347)	0.647 (0.807)
MIRSEQ CNMF	8.54e-06 (7.58e-05)	0.0365 (0.0762)	0.00013 (0.000535)	0.00044 (0.00171)	0.0021 (0.00657)	0.00075 (0.00251)	0.00126 (0.00403)	0.108 (0.187)	0.38 (0.521)	0.744 (0.893)	0.206 (0.307)	0.188 (0.287)
MIRSEQ CHIERARCHICAL	2.94e-05 (0.00016)	0.0719 (0.135)	2e-05 (0.00012)	6e-05 (0.000262)	0.0275 (0.0609)	4e-05 (0.000199)	0.00074 (0.00251)	0.269 (0.384)	0.943 (1.00)	0.59 (0.758)	0.0862 (0.157)	0.0167 (0.0401)
MIRseq Mature CNMF subtypes	0.00455 (0.0129)	0.675 (0.831)	0.252 (0.366)	0.0823 (0.152)	0.199 (0.298)	0.114 (0.19)	0.0312 (0.0676)	0.951 (1.00)			1e-05 (7.58e-05)	0.267 (0.384)
MIRseq Mature cHierClus subtypes	0.632 (0.802)	0.0622 (0.121)	0.179 (0.277)	0.0182 (0.0429)	0.171 (0.27)	0.866 (0.989)	0.00242 (0.00741)	0.82 (0.967)	1 (1.00)	0.86 (0.989)	1e-05 (7.58e-05)	0.277 (0.391)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	34	24	14

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.118 (logrank test), Q value = 0.2

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	72	15	0.5 - 117.8 (38.0)
subtype1	34	5	1.4 - 115.0 (37.1)
subtype2	24	9	0.5 - 114.4 (41.2)
subtype3	14	1	10.5 - 117.8 (38.8)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.873 (Kruskal-Wallis (anova)), Q value = 0.99

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

	nPatients	Mean (Std.Dev)
ALL	71	60.5 (12.4)
subtype1	33	60.2 (13.8)
subtype2	24	59.9 (11.1)
subtype3	14	62.6 (11.3)

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00624 (Fisher's exact test), Q value = 0.017

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	40	13	14	5
subtype1	23	4	6	1
subtype2	9	3	8	4
subtype3	8	6	0	0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00823 (Fisher's exact test), Q value = 0.022

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	41	14	17
subtype1	23	4	7
subtype2	10	4	10
subtype3	8	6	0

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 = 0.0701 (Fisher's exact test), Q value = 0.13

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

nPatients	0	1
ALL	35	3
subtype1	18	0
subtype2	10	3
subtype3	7	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 = 0.121 (Fisher's exact test), Q value = 0.2

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

nPatients	0	1
ALL	67	5
subtype1	33	1
subtype2	20	4
subtype3	14	0

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 = 0.635 (Fisher's exact test), Q value = 0.8

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

nPatients	FEMALE	MALE
ALL	29	43
subtype1	15	19
subtype2	10	14
subtype3	4	10

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.00755 (Fisher's exact test), Q value = 0.02

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	62
subtype1	0	0	32
subtype2	0	2	20
subtype3	1	3	10

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.313 (Fisher's exact test), Q value = 0.43

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	42
subtype1	5	19
subtype2	2	11
subtype3	0	12

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S11. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	12	12	4	11	13	9	11

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0315 (logrank test), Q value = 0.068

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	72	15	0.5 - 117.8 (38.0)
subtype1	12	1	18.4 - 47.2 (31.6)
subtype2	12	2	0.5 - 90.3 (36.8)
subtype3	4	2	1.7 - 37.6 (17.3)
subtype4	11	6	14.2 - 114.4 (54.6)
subtype5	13	3	11.1 - 106.2 (48.4)
subtype6	9	1	1.4 - 115.0 (38.5)
subtype7	11	0	12.1 - 117.8 (40.7)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.168 (Kruskal-Wallis (anova)), Q value = 0.27

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	71	60.5 (12.4)
subtype1	11	63.3 (13.7)
subtype2	12	62.2 (10.8)
subtype3	4	57.2 (10.9)
subtype4	11	56.2 (10.9)
subtype5	13	64.1 (13.5)
subtype6	9	50.7 (10.7)
subtype7	11	65.4 (10.9)

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00058 (Fisher's exact test), Q value = 0.0021

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	40	13	14	5
subtype1	8	2	2	0
subtype2	8	2	2	0
subtype3	2	2	0	0
subtype4	0	1	6	4
subtype5	7	2	3	1
subtype6	8	0	1	0
subtype7	7	4	0	0

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00383 (Fisher's exact test), Q value = 0.011

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	41	14	17
subtype1	8	2	2
subtype2	8	2	2
subtype3	2	2	0
subtype4	1	2	8
subtype5	7	2	4
subtype6	8	0	1
subtype7	7	4	0

Figure S13. 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 = 0.0981 (Fisher's exact test), Q value = 0.17

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	35	3
subtype1	6	0
subtype2	5	0
subtype3	3	0
subtype4	5	3
subtype5	9	0
subtype6	3	0
subtype7	4	0

Figure S14. 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 = 0.0104 (Fisher's exact test), Q value = 0.027

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	67	5
subtype1	12	0
subtype2	12	0
subtype3	4	0
subtype4	7	4
subtype5	12	1
subtype6	9	0
subtype7	11	0

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 6e-05 (Fisher's exact test), Q value = 0.00026

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	29	43
subtype1	0	12
subtype2	3	9
subtype3	1	3
subtype4	6	5
subtype5	12	1
subtype6	3	6
subtype7	4	7

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00012

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	62
subtype1	0	0	11
subtype2	0	1	10
subtype3	1	3	0
subtype4	0	0	10
subtype5	0	0	13
subtype6	0	0	8
subtype7	0	1	10

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.474 (Fisher's exact test), Q value = 0.64

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	42
subtype1	1	7
subtype2	0	4
subtype3	0	2
subtype4	2	6
subtype5	3	7
subtype6	1	5
subtype7	0	11

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

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

Table S21. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	238	135	155

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

P value = 0.000279 (logrank test), Q value = 0.0011

Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	526	175	0.1 - 149.2 (38.5)
subtype1	238	59	0.1 - 149.2 (38.8)
subtype2	133	47	0.1 - 133.7 (43.2)
subtype3	155	69	0.1 - 129.4 (33.5)

Figure S19. 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 'YEARS_TO_BIRTH'

P value = 0.0574 (Kruskal-Wallis (anova)), Q value = 0.11

Table S23. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	527	60.5 (12.1)
subtype1	238	59.9 (12.7)
subtype2	135	59.5 (11.3)
subtype3	154	62.3 (11.7)

Figure S20. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00012

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	263	57	125	83
subtype1	141	29	48	20
subtype2	68	13	30	24
subtype3	54	15	47	39

Figure S21. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00023

Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	268	69	180	11
subtype1	141	34	62	1
subtype2	69	16	44	6
subtype3	58	19	74	4

Figure S22. 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 = 0.00383 (Fisher's exact test), Q value = 0.011

Table S26. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	234	17
subtype1	109	2
subtype2	58	4
subtype3	67	11

Figure S23. 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 = 4e-05 (Fisher's exact test), Q value = 2e-04

Table S27. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	418	78
subtype1	203	19
subtype2	105	21
subtype3	110	38

Figure S24. 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 = 0.00606 (Fisher's exact test), Q value = 0.017

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	189	339
subtype1	92	146
subtype2	57	78
subtype3	40	115

Figure S25. 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 = 0.173 (Kruskal-Wallis (anova)), Q value = 0.27

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	53	85.3 (26.0)
subtype1	20	93.0 (8.0)
subtype2	11	93.6 (6.7)
subtype3	22	74.1 (37.0)

Figure S26. 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 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.909 (Kruskal-Wallis (anova)), Q value = 1

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	21	28.3 (16.1)
subtype1	9	29.6 (18.8)
subtype2	5	29.2 (15.9)
subtype3	7	26.1 (14.7)

Figure S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.83 (Kruskal-Wallis (anova)), Q value = 0.97

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	12	1979.2 (17.8)
subtype1	5	1974.4 (21.8)
subtype2	3	1984.3 (13.2)
subtype3	4	1981.5 (18.5)

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.011 (Fisher's exact test), Q value = 0.027

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	8	56	457
subtype1	2	21	212
subtype2	4	24	106
subtype3	2	11	139

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.787 (Fisher's exact test), Q value = 0.94

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	26	350
subtype1	13	151
subtype2	7	97
subtype3	6	102

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S34. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	130	78	111

'METHLYATION CNMF' versus 'Time to Death'

P value = 1.25e-05 (logrank test), Q value = 9e-05

Table S35. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	317	105	0.1 - 149.2 (35.0)
subtype1	129	23	0.1 - 149.2 (38.4)
subtype2	77	25	0.5 - 130.7 (30.2)
subtype3	111	57	0.6 - 133.7 (31.1)

Figure S31. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0123 (Kruskal-Wallis (anova)), Q value = 0.03

Table S36. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	319	61.4 (11.8)
subtype1	130	59.2 (12.7)
subtype2	78	62.0 (11.4)
subtype3	111	63.5 (10.7)

Figure S32. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S37. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	156	31	73	59
subtype1	89	17	13	11
subtype2	44	2	20	12
subtype3	23	12	40	36

Figure S33. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S38. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	159	41	111	8
subtype1	89	21	20	0
subtype2	46	3	25	4
subtype3	24	17	66	4

Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0463 (Fisher's exact test), Q value = 0.093

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	133	8
subtype1	53	0
subtype2	31	3
subtype3	49	5

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 8e-05 (Fisher's exact test), Q value = 0.00034

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	234	53
subtype1	103	10
subtype2	58	10
subtype3	73	33

Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 2e-05 (Fisher's exact test), Q value = 0.00012

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	114	205
subtype1	62	68
subtype2	31	47
subtype3	21	90

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.23 (Kruskal-Wallis (anova)), Q value = 0.34

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	44	89.3 (16.3)
subtype1	22	90.0 (9.8)
subtype2	10	95.0 (7.1)
subtype3	12	83.3 (27.4)

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.968 (Kruskal-Wallis (anova)), Q value = 1

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	20	29.0 (16.2)
subtype1	7	29.4 (20.1)
subtype2	7	29.6 (17.0)
subtype3	6	27.8 (12.9)

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.849 (Kruskal-Wallis (anova)), Q value = 0.99

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	11	1978.2 (18.3)
subtype1	4	1975.0 (24.1)
subtype2	3	1977.0 (16.6)
subtype3	4	1982.2 (17.7)

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0388 (Fisher's exact test), Q value = 0.08

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	49	266
subtype1	0	21	109
subtype2	0	18	60
subtype3	1	10	97

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

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

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	260
subtype1	4	101
subtype2	2	64
subtype3	4	95

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S47. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	89	87	91	84	94	33

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 1.36e-08 (logrank test), Q value = 7.1e-07

Table S48. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	478	166	0.1 - 149.2 (37.5)
subtype1	89	26	0.5 - 117.8 (37.2)
subtype2	87	22	0.2 - 133.9 (45.1)
subtype3	91	58	0.5 - 130.7 (30.6)
subtype4	84	23	0.5 - 149.2 (38.2)
subtype5	94	34	0.1 - 133.7 (37.4)
subtype6	33	3	0.9 - 105.4 (37.0)

Figure S43. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0884 (Kruskal-Wallis (anova)), Q value = 0.16

Table S49. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	477	60.4 (12.2)
subtype1	89	61.3 (11.6)
subtype2	87	61.2 (10.9)
subtype3	91	61.6 (11.9)
subtype4	83	62.0 (12.6)
subtype5	94	58.3 (12.0)
subtype6	33	55.3 (15.6)

Figure S44. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S50. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	235	48	114	81
subtype1	56	7	18	8
subtype2	50	12	17	8
subtype3	17	9	30	35
subtype4	36	9	26	13
subtype5	47	10	20	17
subtype6	29	1	3	0

Figure S45. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S51. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	240	59	168	11
subtype1	57	7	24	1
subtype2	50	14	22	1
subtype3	20	14	50	7
subtype4	35	11	38	0
subtype5	49	12	31	2
subtype6	29	1	3	0

Figure S46. 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 = 0.0211 (Fisher's exact test), Q value = 0.048

Table S52. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	214	15
subtype1	44	3
subtype2	34	0
subtype3	41	9
subtype4	42	1
subtype5	43	2
subtype6	10	0

Figure S47. 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 = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S53. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	381	76
subtype1	77	8
subtype2	76	6
subtype3	56	32
subtype4	69	13
subtype5	72	17
subtype6	31	0

Figure S48. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.195 (Fisher's exact test), Q value = 0.3

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	162	316
subtype1	30	59
subtype2	39	48
subtype3	29	62
subtype4	25	59
subtype5	26	68
subtype6	13	20

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.281 (Kruskal-Wallis (anova)), Q value = 0.39

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	47	88.3 (20.8)
subtype1	3	96.7 (5.8)
subtype2	9	90.0 (13.2)
subtype3	13	76.2 (34.8)
subtype4	9	94.4 (10.1)
subtype5	7	91.4 (3.8)
subtype6	6	95.0 (5.5)

Figure S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.545 (Kruskal-Wallis (anova)), Q value = 0.72

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	16	27.8 (14.1)
subtype1	4	30.0 (18.3)
subtype3	5	30.0 (12.2)
subtype4	4	20.2 (14.2)
subtype5	2	30.5 (21.9)
subtype6	1	33.0 (NA)

Figure S51. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.574 (Fisher's exact test), Q value = 0.75

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	8	44	420
subtype1	2	11	76
subtype2	1	8	77
subtype3	2	5	84
subtype4	0	5	76
subtype5	3	11	78
subtype6	0	4	29

Figure S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.568 (Fisher's exact test), Q value = 0.74

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	21	315
subtype1	4	51
subtype2	7	54
subtype3	3	66
subtype4	3	63
subtype5	3	65
subtype6	1	16

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S59. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	137	114	85	142

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 1.19e-09 (logrank test), Q value = 1.7e-07

Table S60. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	478	166	0.1 - 149.2 (37.5)
subtype1	137	40	0.5 - 133.9 (37.2)
subtype2	114	44	0.2 - 126.3 (39.9)
subtype3	85	51	0.1 - 130.7 (25.9)
subtype4	142	31	0.8 - 149.2 (45.1)

Figure S54. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000705 (Kruskal-Wallis (anova)), Q value = 0.0025

Table S61. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	477	60.4 (12.2)
subtype1	137	57.2 (12.5)
subtype2	113	63.3 (11.7)
subtype3	85	61.5 (11.2)
subtype4	142	60.6 (12.2)

Figure S55. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S62. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	235	48	114	81
subtype1	86	10	22	19
subtype2	46	16	30	22
subtype3	25	6	25	29
subtype4	78	16	37	11

Figure S56. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S63. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	240	59	168	11
subtype1	87	13	36	1
subtype2	48	19	47	0
subtype3	25	9	42	9
subtype4	80	18	43	1

Figure S57. 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 = 0.0458 (Fisher's exact test), Q value = 0.093

Table S64. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	214	15
subtype1	59	3
subtype2	59	3
subtype3	43	8
subtype4	53	1

Figure S58. 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 = 5e-05 (Fisher's exact test), Q value = 0.00023

Table S65. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	381	76
subtype1	107	19
subtype2	90	21
subtype3	57	27
subtype4	127	9

Figure S59. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0659 (Fisher's exact test), Q value = 0.13

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	162	316
subtype1	48	89
subtype2	31	83
subtype3	24	61
subtype4	59	83

Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0211 (Kruskal-Wallis (anova)), Q value = 0.048

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	47	88.3 (20.8)
subtype1	14	87.1 (25.5)
subtype2	13	79.2 (26.0)
subtype3	6	95.0 (8.4)
subtype4	14	95.0 (8.5)

Figure S61. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.711 (Kruskal-Wallis (anova)), Q value = 0.87

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	16	27.8 (14.1)
subtype1	5	21.0 (14.4)
subtype2	3	30.0 (17.3)
subtype3	2	45.0 (7.1)
subtype4	6	26.7 (11.6)

Figure S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.0261 (Fisher's exact test), Q value = 0.059

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	8	44	420
subtype1	4	20	111
subtype2	1	4	107
subtype3	2	9	74
subtype4	1	11	128

Figure S63. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.125 (Fisher's exact test), Q value = 0.2

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	21	315
subtype1	4	96
subtype2	4	75
subtype3	2	60
subtype4	11	84

Figure S64. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S71. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	116	223	194

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.03e-06 (logrank test), Q value = 3.7e-05

Table S72. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	531	175	0.1 - 149.2 (38.5)
subtype1	115	33	0.1 - 130.7 (38.4)
subtype2	222	52	0.1 - 149.2 (44.1)
subtype3	194	90	0.5 - 133.7 (31.8)

Figure S65. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.113 (Kruskal-Wallis (anova)), Q value = 0.19

Table S73. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	532	60.6 (12.1)
subtype1	116	58.5 (12.7)
subtype2	222	61.4 (12.2)
subtype3	194	60.9 (11.7)

Figure S66. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S74. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	268	57	124	84
subtype1	72	12	19	13
subtype2	135	23	40	25
subtype3	61	22	65	46

Figure S67. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S75. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	273	69	180	11
subtype1	72	13	28	3
subtype2	135	27	59	2
subtype3	66	29	93	6

Figure S68. 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 = 0.00311 (Fisher's exact test), Q value = 0.0093

Table S76. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	240	16
subtype1	50	3
subtype2	101	1
subtype3	89	12

Figure S69. 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 = 0.00048 (Fisher's exact test), Q value = 0.0018

Table S77. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	422	79
subtype1	96	11
subtype2	185	23
subtype3	141	45

Figure S70. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 3e-05 (Fisher's exact test), Q value = 0.00016

Table S78. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	188	345
subtype1	40	76
subtype2	101	122
subtype3	47	147

Figure S71. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.65 (Kruskal-Wallis (anova)), Q value = 0.81

Table S79. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	52	86.9 (23.3)
subtype1	11	93.6 (6.7)
subtype2	22	90.0 (9.8)
subtype3	19	79.5 (36.1)

Figure S72. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.925 (Kruskal-Wallis (anova)), Q value = 1

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	21	28.3 (16.1)
subtype1	5	26.0 (15.7)
subtype2	6	31.7 (22.4)
subtype3	10	27.5 (13.3)

Figure S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.731 (Kruskal-Wallis (anova)), Q value = 0.88

Table S81. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	12	1979.2 (17.8)
subtype1	2	1993.0 (8.5)
subtype2	3	1971.0 (27.8)
subtype3	7	1978.9 (14.7)

Figure S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0106 (Fisher's exact test), Q value = 0.027

Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	8	56	462
subtype1	4	21	90
subtype2	2	20	197
subtype3	2	15	175

Figure S75. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.103 (Fisher's exact test), Q value = 0.18

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	26	355
subtype1	3	82
subtype2	16	142
subtype3	7	131

Figure S76. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S84. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	118	35	184	133	35	28

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1.49e-08 (logrank test), Q value = 7.1e-07

Table S85. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	531	175	0.1 - 149.2 (38.5)
subtype1	118	34	0.1 - 126.0 (35.3)
subtype2	34	6	0.5 - 129.4 (37.4)
subtype3	183	41	0.1 - 149.2 (47.2)
subtype4	133	73	0.5 - 133.7 (31.8)
subtype5	35	15	0.1 - 130.7 (48.5)
subtype6	28	6	3.6 - 117.8 (44.1)

Figure S77. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.11 (Kruskal-Wallis (anova)), Q value = 0.19

Table S86. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	532	60.6 (12.1)
subtype1	118	58.8 (12.3)
subtype2	35	57.5 (13.8)
subtype3	183	61.5 (12.3)
subtype4	133	62.3 (11.4)
subtype5	35	58.3 (11.9)
subtype6	28	61.1 (11.4)

Figure S78. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S87. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	268	57	124	84
subtype1	62	12	24	20
subtype2	26	4	2	3
subtype3	117	19	35	13
subtype4	37	13	47	36
subtype5	5	5	14	11
subtype6	21	4	2	1

Figure S79. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S88. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	273	69	180	11
subtype1	63	17	36	2
subtype2	26	4	5	0
subtype3	117	21	46	0
subtype4	39	15	72	7
subtype5	7	8	19	1
subtype6	21	4	2	1

Figure S80. 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 = 0.00058 (Fisher's exact test), Q value = 0.0021

Table S89. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	240	16
subtype1	43	1
subtype2	15	1
subtype3	85	0
subtype4	67	10
subtype5	17	4
subtype6	13	0

Figure S81. 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 = 3e-05 (Fisher's exact test), Q value = 0.00016

Table S90. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	422	79
subtype1	94	18
subtype2	28	3
subtype3	158	13
subtype4	95	34
subtype5	22	10
subtype6	25	1

Figure S82. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S91. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	188	345
subtype1	16	102
subtype2	17	18
subtype3	101	83
subtype4	31	102
subtype5	17	18
subtype6	6	22

Figure S83. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0353 (Kruskal-Wallis (anova)), Q value = 0.075

Table S92. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	52	86.9 (23.3)
subtype1	10	98.0 (4.2)
subtype2	7	92.9 (11.1)
subtype3	17	90.0 (9.4)
subtype4	13	70.0 (40.6)
subtype5	1	80.0 (NA)
subtype6	4	92.5 (5.0)

Figure S84. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.486 (Kruskal-Wallis (anova)), Q value = 0.65

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	21	28.3 (16.1)
subtype1	4	30.8 (13.1)
subtype2	3	38.7 (12.1)
subtype3	5	28.8 (23.8)
subtype4	5	28.0 (16.4)
subtype5	4	18.0 (9.6)

Figure S85. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.525 (Kruskal-Wallis (anova)), Q value = 0.7

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	12	1979.2 (17.8)
subtype1	3	1977.7 (18.5)
subtype2	1	1987.0 (NA)
subtype3	2	1973.5 (38.9)
subtype4	3	1975.7 (17.6)
subtype5	3	1985.7 (13.8)

Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.00106 (Fisher's exact test), Q value = 0.0035

Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	8	56	462
subtype1	3	11	100
subtype2	2	10	23
subtype3	1	17	164
subtype4	2	7	124
subtype5	0	4	30
subtype6	0	7	21

Figure S87. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.647 (Fisher's exact test), Q value = 0.81

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	26	355
subtype1	6	74
subtype2	2	30
subtype3	12	112
subtype4	5	89
subtype5	1	26
subtype6	0	24

Figure S88. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S97. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	126	205	185

'MIRSEQ CNMF' versus 'Time to Death'

P value = 8.54e-06 (logrank test), Q value = 7.6e-05

Table S98. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	514	172	0.1 - 149.2 (38.4)
subtype1	125	33	0.1 - 131.1 (45.5)
subtype2	204	52	0.2 - 149.2 (42.4)
subtype3	185	87	0.5 - 133.7 (33.5)

Figure S89. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0365 (Kruskal-Wallis (anova)), Q value = 0.076

Table S99. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	516	60.5 (12.1)
subtype1	126	58.4 (12.1)
subtype2	205	62.1 (12.3)
subtype3	185	60.2 (11.6)

Figure S90. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00013 (Fisher's exact test), Q value = 0.00053

Table S100. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	254	55	124	83
subtype1	77	11	27	11
subtype2	111	20	45	29
subtype3	66	24	52	43

Figure S91. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00044 (Fisher's exact test), Q value = 0.0017

Table S101. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	259	67	179	11
subtype1	78	11	33	4
subtype2	112	26	65	2
subtype3	69	30	81	5

Figure S92. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0021 (Fisher's exact test), Q value = 0.0066

Table S102. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	228	17
subtype1	56	3
subtype2	88	1
subtype3	84	13

Figure S93. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.00075 (Fisher's exact test), Q value = 0.0025

Table S103. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	406	78
subtype1	105	9
subtype2	168	27
subtype3	133	42

Figure S94. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00126 (Fisher's exact test), Q value = 0.004

Table S104. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	181	335
subtype1	44	82
subtype2	89	116
subtype3	48	137

Figure S95. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.108 (Kruskal-Wallis (anova)), Q value = 0.19

Table S105. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	53	85.3 (26.0)
subtype1	14	92.1 (11.9)
subtype2	23	92.6 (7.5)
subtype3	16	68.8 (41.5)

Figure S96. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.38 (Kruskal-Wallis (anova)), Q value = 0.52

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	21	28.3 (16.1)
subtype1	6	21.0 (12.8)
subtype2	6	34.5 (19.9)
subtype3	9	29.1 (15.2)

Figure S97. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.744 (Kruskal-Wallis (anova)), Q value = 0.89

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	12	1979.2 (17.8)
subtype1	4	1975.5 (15.8)
subtype2	4	1975.0 (24.1)
subtype3	4	1987.2 (14.4)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.206 (Fisher's exact test), Q value = 0.31

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	8	56	445
subtype1	1	20	104
subtype2	3	16	182
subtype3	4	20	159

Figure S99. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.188 (Fisher's exact test), Q value = 0.29

Table S109. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	345
subtype1	2	83
subtype2	12	134
subtype3	10	128

Figure S100. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S110. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	146	137	196	37

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 2.94e-05 (logrank test), Q value = 0.00016

Table S111. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	514	172	0.1 - 149.2 (38.4)
subtype1	145	49	0.1 - 131.1 (36.5)
subtype2	137	51	0.1 - 123.1 (34.4)
subtype3	195	46	0.1 - 149.2 (44.9)
subtype4	37	26	1.4 - 118.8 (40.7)

Figure S101. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.0719 (Kruskal-Wallis (anova)), Q value = 0.13

Table S112. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	516	60.5 (12.1)
subtype1	146	59.1 (12.4)
subtype2	137	59.6 (11.9)
subtype3	196	61.6 (12.0)
subtype4	37	64.2 (11.0)

Figure S102. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00012

Table S113. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	254	55	124	83
subtype1	83	10	36	17
subtype2	51	17	34	35
subtype3	111	24	42	19
subtype4	9	4	12	12

Figure S103. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.00026

Table S114. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	259	67	179	11
subtype1	84	11	46	5
subtype2	53	23	58	3
subtype3	113	28	54	1
subtype4	9	5	21	2

Figure S104. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.0275 (Fisher's exact test), Q value = 0.061

Table S115. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	228	17
subtype1	61	8
subtype2	67	7
subtype3	84	1
subtype4	16	1

Figure S105. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 4e-05 (Fisher's exact test), Q value = 2e-04

Table S116. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	406	78
subtype1	119	15
subtype2	98	32
subtype3	165	18
subtype4	24	13

Figure S106. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00074 (Fisher's exact test), Q value = 0.0025

Table S117. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	181	335
subtype1	54	92
subtype2	36	101
subtype3	85	111
subtype4	6	31

Figure S107. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.269 (Kruskal-Wallis (anova)), Q value = 0.38

Table S118. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	53	85.3 (26.0)
subtype1	17	83.5 (32.0)
subtype2	16	78.1 (31.9)
subtype3	19	92.1 (9.2)
subtype4	1	100.0 (NA)

Figure S108. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.943 (Kruskal-Wallis (anova)), Q value = 1

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	21	28.3 (16.1)
subtype1	6	25.7 (13.0)
subtype2	8	28.1 (15.7)
subtype3	7	30.9 (20.5)

Figure S109. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.59 (Kruskal-Wallis (anova)), Q value = 0.76

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

	nPatients	Mean (Std.Dev)
ALL	12	1979.2 (17.8)
subtype1	4	1975.0 (16.1)
subtype2	4	1987.8 (13.4)
subtype3	4	1975.0 (24.1)

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0862 (Fisher's exact test), Q value = 0.16

Table S121. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	8	56	445
subtype1	4	22	120
subtype2	2	15	117
subtype3	2	19	171
subtype4	0	0	37

Figure S111. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.0167 (Fisher's exact test), Q value = 0.04

Table S122. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	345
subtype1	2	95
subtype2	4	99
subtype3	14	125
subtype4	4	26

Figure S112. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S123. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	38	22	49	35

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

P value = 0.00455 (logrank test), Q value = 0.013

Table S124. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	142	44	0.1 - 149.2 (35.5)
subtype1	36	9	0.1 - 116.8 (21.3)
subtype2	22	4	6.9 - 131.1 (47.3)
subtype3	49	13	2.1 - 149.2 (46.0)
subtype4	35	18	0.5 - 115.7 (31.1)

Figure S113. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.675 (Kruskal-Wallis (anova)), Q value = 0.83

Table S125. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	144	60.5 (11.7)
subtype1	38	60.0 (11.0)
subtype2	22	58.0 (12.0)
subtype3	49	61.7 (12.3)
subtype4	35	61.2 (11.3)

Figure S114. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.252 (Fisher's exact test), Q value = 0.37

Table S126. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	75	17	26	26
subtype1	24	5	4	5
subtype2	15	1	4	2
subtype3	22	7	12	8
subtype4	14	4	6	11

Figure S115. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0823 (Fisher's exact test), Q value = 0.15

Table S127. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	77	19	43	5
subtype1	24	6	7	1
subtype2	16	1	4	1
subtype3	22	8	19	0
subtype4	15	4	13	3

Figure S116. 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 = 0.199 (Fisher's exact test), Q value = 0.3

Table S128. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	56	5
subtype1	7	1
subtype2	9	1
subtype3	23	0
subtype4	17	3

Figure S117. 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 = 0.114 (Fisher's exact test), Q value = 0.19

Table S129. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	98	23
subtype1	14	4
subtype2	20	1
subtype3	41	8
subtype4	23	10

Figure S118. 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 = 0.0312 (Fisher's exact test), Q value = 0.068

Table S130. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	50	94
subtype1	18	20
subtype2	6	16
subtype3	20	29
subtype4	6	29

Figure S119. 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 = 0.951 (Kruskal-Wallis (anova)), Q value = 1

Table S131. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	21	91.4 (10.6)
subtype1	8	90.0 (13.1)
subtype3	9	92.2 (9.7)
subtype4	4	92.5 (9.6)

Figure S120. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S132. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	3	30	108
subtype1	0	24	14
subtype2	1	2	18
subtype3	0	0	48
subtype4	2	4	28

Figure S121. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.267 (Fisher's exact test), Q value = 0.38

Table S133. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	6	104
subtype1	0	32
subtype2	1	13
subtype3	4	37
subtype4	1	22

Figure S122. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S134. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	40	56	48

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

P value = 0.632 (logrank test), Q value = 0.8

Table S135. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	142	44	0.1 - 149.2 (35.5)
subtype1	38	9	0.1 - 116.8 (23.9)
subtype2	56	21	0.5 - 133.7 (38.9)
subtype3	48	14	2.1 - 149.2 (40.8)

Figure S123. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0622 (Kruskal-Wallis (anova)), Q value = 0.12

Table S136. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	144	60.5 (11.7)
subtype1	40	60.3 (10.9)
subtype2	56	58.1 (11.7)
subtype3	48	63.6 (11.8)

Figure S124. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.179 (Fisher's exact test), Q value = 0.28

Table S137. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	75	17	26	26
subtype1	27	4	4	5
subtype2	27	4	12	13
subtype3	21	9	10	8

Figure S125. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0182 (Fisher's exact test), Q value = 0.043

Table S138. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	77	19	43	5
subtype1	28	5	6	1
subtype2	27	4	21	4
subtype3	22	10	16	0

Figure S126. 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 = 0.171 (Fisher's exact test), Q value = 0.27

Table S139. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	56	5
subtype1	8	1
subtype2	26	4
subtype3	22	0

Figure S127. 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 = 0.866 (Fisher's exact test), Q value = 0.99

Table S140. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	98	23
subtype1	17	4
subtype2	41	11
subtype3	40	8

Figure S128. 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 = 0.00242 (Fisher's exact test), Q value = 0.0074

Table S141. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	50	94
subtype1	18	22
subtype2	10	46
subtype3	22	26

Figure S129. 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 = 0.82 (Kruskal-Wallis (anova)), Q value = 0.97

Table S142. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	21	91.4 (10.6)
subtype1	8	90.0 (13.1)
subtype2	5	94.0 (8.9)
subtype3	8	91.2 (9.9)

Figure S130. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 1 (Kruskal-Wallis (anova)), Q value = 1

Table S143. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	26.4 (18.0)
subtype1	5	28.0 (22.7)
subtype2	4	21.0 (13.3)
subtype3	1	40.0 (NA)

Figure S131. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.86 (Kruskal-Wallis (anova)), Q value = 0.99

Table S144. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	7	1980.6 (20.8)
subtype1	4	1982.5 (25.0)
subtype2	3	1978.0 (18.4)

Figure S132. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.6e-05

Table S145. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	3	30	108
subtype1	0	23	17
subtype2	3	6	46
subtype3	0	1	45

Figure S133. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.277 (Fisher's exact test), Q value = 0.39

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	6	104
subtype1	0	34
subtype2	3	35
subtype3	3	35

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

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KIRC-TP/20125734/KIRC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIRC-TP/19775258/KIRC-TP.merged_data.txt
Number of patients = 537
Number of clustering approaches = 12
Number of selected clinical features = 12
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

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

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] 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)

[5] 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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