Correlation between aggregated molecular cancer subtypes and selected clinical features

Kidney Renal Clear Cell Carcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1XG9QH3

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, 71 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'.
6 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', and 'PATHOLOGY_M_STAGE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', and 'GENDER'.
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 5 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'.
5 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', 'GENDER', and 'RACE'.
3 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', and 'GENDER'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_M_STAGE', and 'RACE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PATHOLOGIC_STAGE', '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, 71 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	0.119 (0.215)	0.87 (0.994)	0.00614 (0.0184)	0.00588 (0.018)	0.124 (0.22)	0.136 (0.236)	0.45 (0.623)				0.0122 (0.0326)	0.321 (0.487)
mRNA cHierClus subtypes	0.0269 (0.0604)	0.168 (0.278)	0.00051 (0.00216)	0.00442 (0.0141)	0.0964 (0.183)	0.011 (0.03)	6e-05 (0.000332)				1e-05 (6.86e-05)	0.474 (0.65)
Copy Number Ratio CNMF subtypes	7.01e-05 (0.00036)	0.0913 (0.175)	0.00769 (0.0221)	0.0189 (0.0461)	0.00492 (0.0154)	0.00128 (0.00461)	0.704 (0.845)	0.671 (0.815)	0.674 (0.815)	0.806 (0.951)	0.248 (0.389)	0.863 (0.994)
METHLYATION CNMF	1.94e-05 (0.000125)	0.165 (0.277)	1e-05 (6.86e-05)	1e-05 (6.86e-05)	0.0243 (0.0564)	5e-05 (0.000288)	0.00077 (0.00292)	0.908 (1.00)	0.95 (1.00)	1 (1.00)	0.291 (0.446)	0.892 (1.00)
RPPA CNMF subtypes	1.17e-08 (5.62e-07)	0.0884 (0.174)	1e-05 (6.86e-05)	1e-05 (6.86e-05)	0.0209 (0.0502)	1e-05 (6.86e-05)	0.193 (0.312)	0.21 (0.336)	0.545 (0.713)		0.574 (0.738)	0.565 (0.733)
RPPA cHierClus subtypes	1.41e-09 (1.02e-07)	0.000705 (0.00282)	1e-05 (6.86e-05)	1e-05 (6.86e-05)	0.0474 (0.0962)	1e-05 (6.86e-05)	0.0668 (0.134)	0.0153 (0.04)	0.711 (0.846)		0.0261 (0.0596)	0.127 (0.223)
RNAseq CNMF subtypes	9.06e-10 (1.02e-07)	0.257 (0.399)	1e-05 (6.86e-05)	1e-05 (6.86e-05)	0.0442 (0.091)	5e-05 (0.000288)	0.00015 (0.000675)	0.483 (0.654)	0.635 (0.789)	0.518 (0.691)	0.00199 (0.00682)	0.103 (0.193)
RNAseq cHierClus subtypes	1.89e-08 (6.8e-07)	0.11 (0.203)	1e-05 (6.86e-05)	1e-05 (6.86e-05)	0.00054 (0.00222)	2e-05 (0.000125)	1e-05 (6.86e-05)	0.0228 (0.0538)	0.486 (0.654)	0.525 (0.694)	0.00112 (0.00414)	0.648 (0.797)
MIRSEQ CNMF	4.77e-08 (1.37e-06)	0.0366 (0.0775)	0.00167 (0.00587)	0.00015 (0.000675)	0.0288 (0.0629)	0.0276 (0.0611)	0.00317 (0.0104)	0.326 (0.489)	0.358 (0.531)	0.957 (1.00)	0.0171 (0.044)	0.857 (0.994)
MIRSEQ CHIERARCHICAL	6.47e-05 (0.000345)	0.63 (0.789)	0.00014 (0.000672)	0.00012 (0.000596)	0.011 (0.03)	0.00076 (0.00292)	0.00269 (0.00901)	0.361 (0.531)	0.943 (1.00)	0.59 (0.752)	0.85 (0.994)	0.187 (0.306)
MIRseq Mature CNMF subtypes	0.000347 (0.00152)	0.43 (0.606)	0.0414 (0.0864)	0.0177 (0.0444)	0.119 (0.215)	0.0179 (0.0444)	0.154 (0.264)	0.908 (1.00)			1e-05 (6.86e-05)	0.218 (0.345)
MIRseq Mature cHierClus subtypes	0.426 (0.606)	0.0892 (0.174)	0.031 (0.0667)	0.00743 (0.0218)	0.42 (0.605)	0.635 (0.789)	0.0085 (0.024)	0.373 (0.543)	0.437 (0.61)		1e-05 (6.86e-05)	0.162 (0.274)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	32	25	15

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.119 (logrank test), Q value = 0.21

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 (39.1)
subtype1	32	5	1.4 - 115.0 (38.7)
subtype2	25	9	0.5 - 114.4 (39.2)
subtype3	15	1	10.5 - 117.8 (40.0)

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 = 0.87 (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	31	61.2 (13.6)
subtype2	25	59.2 (11.5)
subtype3	15	61.5 (11.7)

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 = 0.00614 (Fisher's exact test), Q value = 0.018

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	22	3	6	1
subtype2	9	4	8	4
subtype3	9	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 = 0.00588 (Fisher's exact test), Q value = 0.018

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

nPatients	T1	T2	T3
ALL	41	14	17
subtype1	22	3	7
subtype2	10	5	10
subtype3	9	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 = 0.124 (Fisher's exact test), Q value = 0.22

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

nPatients	0	1
ALL	35	3
subtype1	17	0
subtype2	11	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 = 0.136 (Fisher's exact test), Q value = 0.24

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

nPatients	0	1
ALL	67	5
subtype1	31	1
subtype2	21	4
subtype3	15	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 = 0.45 (Fisher's exact test), Q value = 0.62

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

nPatients	FEMALE	MALE
ALL	29	43
subtype1	15	17
subtype2	10	15
subtype3	4	11

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.0122 (Fisher's exact test), Q value = 0.033

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	30
subtype2	0	2	21
subtype3	1	3	11

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

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.321 (Fisher's exact test), Q value = 0.49

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	4	19
subtype2	3	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 = 0.0269 (logrank test), Q value = 0.06

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 (39.1)
subtype1	12	1	18.4 - 53.4 (33.9)
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 (45.5)

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 = 0.28

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 = 0.00051 (Fisher's exact test), Q value = 0.0022

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 = 0.00442 (Fisher's exact test), Q value = 0.014

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 = 0.0964 (Fisher's exact test), Q value = 0.18

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 = 0.011 (Fisher's exact test), Q value = 0.03

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 = 0.00033

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 = 1e-05 (Fisher's exact test), Q value = 6.9e-05

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 = 0.65

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	4	5	6
Number of samples	61	80	156	82	80	69

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

P value = 7.01e-05 (logrank test), Q value = 0.00036

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 (38.8)
subtype1	61	14	2.0 - 122.8 (50.0)
subtype2	79	28	0.1 - 133.7 (34.0)
subtype3	156	72	0.4 - 129.4 (36.9)
subtype4	82	16	0.1 - 133.9 (44.7)
subtype5	79	28	0.9 - 149.2 (36.9)
subtype6	69	17	0.1 - 117.8 (38.3)

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 = 0.0913 (Kruskal-Wallis (anova)), Q value = 0.18

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	61	61.0 (12.8)
subtype2	80	61.5 (11.0)
subtype3	155	62.1 (12.3)
subtype4	82	58.9 (12.3)
subtype5	80	59.7 (11.2)
subtype6	69	58.3 (12.8)

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 = 0.00769 (Fisher's exact test), Q value = 0.022

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	262	57	124	83
subtype1	29	7	12	13
subtype2	38	8	23	11
subtype3	63	18	41	32
subtype4	47	12	17	6
subtype5	38	5	19	18
subtype6	47	7	12	3

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 = 0.0189 (Fisher's exact test), Q value = 0.046

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	29	11	21	0
subtype2	38	8	31	3
subtype3	67	22	63	4
subtype4	48	13	21	0
subtype5	39	7	30	4
subtype6	47	8	14	0

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 = 0.00492 (Fisher's exact test), Q value = 0.015

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

nPatients	0	1
ALL	234	17
subtype1	31	0
subtype2	38	2
subtype3	64	12
subtype4	36	0
subtype5	36	3
subtype6	29	0

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 = 0.00128 (Fisher's exact test), Q value = 0.0046

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

nPatients	0	1
ALL	418	78
subtype1	44	13
subtype2	65	10
subtype3	118	31
subtype4	70	6
subtype5	60	16
subtype6	61	2

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 = 0.704 (Fisher's exact test), Q value = 0.85

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

nPatients	FEMALE	MALE
ALL	189	339
subtype1	21	40
subtype2	27	53
subtype3	50	106
subtype4	31	51
subtype5	34	46
subtype6	26	43

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 = 0.671 (Kruskal-Wallis (anova)), Q value = 0.82

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

	nPatients	Mean (Std.Dev)
ALL	54	85.6 (25.8)
subtype1	4	97.5 (5.0)
subtype2	3	93.3 (5.8)
subtype3	23	75.7 (36.8)
subtype4	7	90.0 (11.5)
subtype5	8	92.5 (7.1)
subtype6	9	93.3 (5.0)

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 = 0.674 (Kruskal-Wallis (anova)), Q value = 0.82

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	1	20.0 (NA)
subtype2	5	32.0 (16.4)
subtype3	8	24.5 (14.5)
subtype4	1	33.0 (NA)
subtype6	6	31.0 (21.6)

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 = 0.806 (Kruskal-Wallis (anova)), Q value = 0.95

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)
subtype2	1	1970.0 (NA)
subtype3	5	1983.4 (16.6)
subtype4	1	1968.0 (NA)
subtype5	1	1996.0 (NA)
subtype6	4	1975.0 (24.1)

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 = 0.248 (Fisher's exact test), Q value = 0.39

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	0	7	53
subtype2	3	11	65
subtype3	2	11	140
subtype4	1	10	71
subtype5	1	5	73
subtype6	1	12	55

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 = 0.863 (Fisher's exact test), Q value = 0.99

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	3	35
subtype2	5	49
subtype3	9	106
subtype4	3	56
subtype5	4	51
subtype6	2	53

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	4
Number of samples	69	70	97	83

'METHLYATION CNMF' versus 'Time to Death'

P value = 1.94e-05 (logrank test), Q value = 0.00013

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 (35.9)
subtype1	69	10	0.4 - 133.9 (44.5)
subtype2	69	26	0.5 - 130.7 (23.0)
subtype3	97	50	0.6 - 133.7 (31.3)
subtype4	82	19	0.1 - 149.2 (40.9)

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 = 0.165 (Kruskal-Wallis (anova)), Q value = 0.28

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	69	58.9 (13.8)
subtype2	70	62.0 (11.5)
subtype3	97	63.0 (10.6)
subtype4	83	61.0 (11.5)

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 = 6.9e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	155	31	73	59
subtype1	51	6	10	2
subtype2	30	3	24	13
subtype3	20	11	32	33
subtype4	54	11	7	11

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 = 6.9e-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	51	6	11	1
subtype2	32	4	30	4
subtype3	22	16	56	3
subtype4	54	15	14	0

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 = 0.0243 (Fisher's exact test), Q value = 0.056

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

nPatients	0	1
ALL	133	8
subtype1	30	0
subtype2	30	5
subtype3	40	3
subtype4	33	0

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 = 5e-05 (Fisher's exact test), Q value = 0.00029

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

nPatients	0	1
ALL	234	53
subtype1	63	2
subtype2	50	11
subtype3	62	30
subtype4	59	10

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00077 (Fisher's exact test), Q value = 0.0029

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

nPatients	FEMALE	MALE
ALL	114	205
subtype1	30	39
subtype2	28	42
subtype3	19	78
subtype4	37	46

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.908 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	45	89.6 (16.2)
subtype1	13	93.1 (4.8)
subtype2	9	92.2 (9.7)
subtype3	8	81.2 (33.6)
subtype4	15	89.3 (11.6)

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 = 0.95 (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	3	28.7 (18.0)
subtype2	8	27.1 (17.2)
subtype3	5	31.4 (10.7)
subtype4	4	30.0 (24.4)

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

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	2	1983.5 (24.7)
subtype2	4	1981.8 (16.6)
subtype3	3	1977.7 (18.5)
subtype4	2	1966.5 (29.0)

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 = 0.291 (Fisher's exact test), Q value = 0.45

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	13	56
subtype2	0	13	56
subtype3	1	9	85
subtype4	0	14	69

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.892 (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	1	57
subtype2	2	58
subtype3	4	83
subtype4	3	62

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 = 1.17e-08 (logrank test), Q value = 5.6e-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 (38.6)
subtype1	89	26	0.5 - 122.6 (37.2)
subtype2	87	22	0.2 - 133.9 (46.0)
subtype3	91	58	0.5 - 130.7 (31.3)
subtype4	84	23	0.5 - 149.2 (38.2)
subtype5	94	34	0.1 - 133.7 (38.4)
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 = 0.17

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 = 6.9e-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	234	48	113	81
subtype1	56	7	18	8
subtype2	50	12	17	8
subtype3	17	9	29	35
subtype4	35	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 = 6.9e-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 = 0.0209 (Fisher's exact test), Q value = 0.05

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 = 6.9e-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 = 0.193 (Fisher's exact test), Q value = 0.31

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 = 0.21 (Kruskal-Wallis (anova)), Q value = 0.34

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

	nPatients	Mean (Std.Dev)
ALL	48	88.5 (20.6)
subtype1	4	97.5 (5.0)
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 = 0.71

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 = 0.74

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 = 0.565 (Fisher's exact test), Q value = 0.73

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 = 1.41e-09 (logrank test), Q value = 1e-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 (38.6)
subtype1	137	40	0.5 - 133.9 (38.3)
subtype2	114	44	0.2 - 126.3 (41.9)
subtype3	85	51	0.1 - 130.7 (25.9)
subtype4	142	31	0.8 - 149.2 (45.3)

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 = 0.0028

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 = 6.9e-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	234	48	113	81
subtype1	85	10	22	19
subtype2	46	16	30	22
subtype3	25	6	24	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 = 6.9e-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 = 0.0474 (Fisher's exact test), Q value = 0.096

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 = 1e-05 (Fisher's exact test), Q value = 6.9e-05

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 = 0.0668 (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 = 0.0153 (Kruskal-Wallis (anova)), Q value = 0.04

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

	nPatients	Mean (Std.Dev)
ALL	48	88.5 (20.6)
subtype1	14	87.1 (25.5)
subtype2	13	79.2 (26.0)
subtype3	7	95.7 (7.9)
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 = 0.85

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 = 0.06

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 = 0.127 (Fisher's exact test), Q value = 0.22

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	4	5
Number of samples	85	172	82	154	40

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 9.06e-10 (logrank test), Q value = 1e-07

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 (39.5)
subtype1	84	22	0.1 - 130.7 (40.4)
subtype2	171	30	0.1 - 149.2 (48.4)
subtype3	82	25	0.1 - 131.1 (37.1)
subtype4	154	76	0.5 - 133.7 (36.1)
subtype5	40	22	0.6 - 116.8 (30.6)

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 = 0.257 (Kruskal-Wallis (anova)), Q value = 0.4

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	85	59.0 (12.1)
subtype2	171	61.1 (12.4)
subtype3	82	58.7 (12.5)
subtype4	154	61.8 (11.9)
subtype5	40	61.1 (10.5)

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 = 6.9e-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	267	57	123	84
subtype1	57	10	9	9
subtype2	108	19	32	13
subtype3	41	8	20	12
subtype4	51	15	48	39
subtype5	10	5	14	11

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 = 6.9e-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	57	11	16	1
subtype2	108	21	42	1
subtype3	43	8	29	2
subtype4	55	22	71	6
subtype5	10	7	22	1

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 = 0.0442 (Fisher's exact test), Q value = 0.091

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

nPatients	0	1
ALL	240	16
subtype1	36	4
subtype2	81	1
subtype3	37	2
subtype4	71	9
subtype5	15	0

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 = 5e-05 (Fisher's exact test), Q value = 0.00029

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

nPatients	0	1
ALL	422	79
subtype1	70	8
subtype2	149	12
subtype3	67	11
subtype4	111	38
subtype5	25	10

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 = 0.00015 (Fisher's exact test), Q value = 0.00067

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

nPatients	FEMALE	MALE
ALL	188	345
subtype1	31	54
subtype2	84	88
subtype3	22	60
subtype4	39	115
subtype5	12	28

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 = 0.483 (Kruskal-Wallis (anova)), Q value = 0.65

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

	nPatients	Mean (Std.Dev)
ALL	53	87.2 (23.2)
subtype1	13	94.6 (6.6)
subtype2	17	91.2 (7.8)
subtype3	3	96.7 (5.8)
subtype4	16	75.6 (38.3)
subtype5	4	85.0 (17.3)

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 = 0.635 (Kruskal-Wallis (anova)), Q value = 0.79

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	4	32.8 (15.4)
subtype2	6	31.7 (22.4)
subtype3	3	18.0 (10.6)
subtype4	7	26.7 (14.5)
subtype5	1	33.0 (NA)

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 = 0.518 (Kruskal-Wallis (anova)), Q value = 0.69

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	3	1991.3 (4.5)
subtype2	3	1971.0 (27.8)
subtype3	1	1999.0 (NA)
subtype4	4	1974.2 (14.7)
subtype5	1	1968.0 (NA)

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 = 0.00199 (Fisher's exact test), Q value = 0.0068

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	2	20	62
subtype2	1	16	152
subtype3	3	6	73
subtype4	2	9	140
subtype5	0	5	35

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 = 0.19

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	2	69
subtype2	14	108
subtype3	1	51
subtype4	7	101
subtype5	2	26

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 = 1.89e-08 (logrank test), Q value = 6.8e-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 (39.5)
subtype1	118	34	0.1 - 126.0 (35.7)
subtype2	34	6	0.5 - 129.4 (37.4)
subtype3	183	41	0.1 - 149.2 (48.1)
subtype4	133	73	0.5 - 133.7 (33.5)
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 = 0.2

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 = 6.9e-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	267	57	123	84
subtype1	61	12	24	20
subtype2	26	4	2	3
subtype3	117	19	35	13
subtype4	37	13	46	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 = 6.9e-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 = 0.00054 (Fisher's exact test), Q value = 0.0022

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 = 2e-05 (Fisher's exact test), Q value = 0.00013

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 = 6.9e-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 = 0.0228 (Kruskal-Wallis (anova)), Q value = 0.054

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

	nPatients	Mean (Std.Dev)
ALL	53	87.2 (23.2)
subtype1	11	98.2 (4.0)
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 = 0.69

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 = 0.00112 (Fisher's exact test), Q value = 0.0041

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 = 0.648 (Fisher's exact test), Q value = 0.8

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	4	5
Number of samples	91	58	187	121	59

'MIRSEQ CNMF' versus 'Time to Death'

P value = 4.77e-08 (logrank test), Q value = 1.4e-06

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 (39.1)
subtype1	90	20	0.1 - 131.1 (48.7)
subtype2	58	23	1.7 - 123.1 (39.8)
subtype3	186	42	0.2 - 149.2 (45.3)
subtype4	121	63	0.5 - 118.8 (31.8)
subtype5	59	24	0.4 - 133.7 (29.0)

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 = 0.0366 (Kruskal-Wallis (anova)), Q value = 0.078

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	91	58.6 (11.6)
subtype2	58	57.6 (11.2)
subtype3	187	62.1 (12.4)
subtype4	121	61.5 (12.1)
subtype5	59	59.8 (12.1)

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 = 0.00167 (Fisher's exact test), Q value = 0.0059

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	253	55	123	83
subtype1	59	4	18	9
subtype2	23	9	13	13
subtype3	105	19	39	24
subtype4	42	15	36	27
subtype5	24	8	17	10

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 = 0.00015 (Fisher's exact test), Q value = 0.00067

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	61	4	23	3
subtype2	24	11	22	1
subtype3	106	25	55	1
subtype4	43	19	54	5
subtype5	25	8	25	1

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 = 0.0288 (Fisher's exact test), Q value = 0.063

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

nPatients	0	1
ALL	228	17
subtype1	39	3
subtype2	27	3
subtype3	79	1
subtype4	57	9
subtype5	26	1

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 = 0.0276 (Fisher's exact test), Q value = 0.061

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

nPatients	0	1
ALL	406	78
subtype1	73	7
subtype2	39	12
subtype3	155	22
subtype4	94	27
subtype5	45	10

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00317 (Fisher's exact test), Q value = 0.01

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

nPatients	FEMALE	MALE
ALL	181	335
subtype1	35	56
subtype2	16	42
subtype3	83	104
subtype4	34	87
subtype5	13	46

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

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.326 (Kruskal-Wallis (anova)), Q value = 0.49

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

	nPatients	Mean (Std.Dev)
ALL	54	85.6 (25.8)
subtype1	8	86.2 (13.0)
subtype2	4	72.5 (48.6)
subtype3	24	92.9 (7.5)
subtype4	11	67.3 (43.8)
subtype5	7	95.7 (5.3)

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 = 0.358 (Kruskal-Wallis (anova)), Q value = 0.53

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	5	18.6 (12.7)
subtype2	7	30.7 (15.1)
subtype3	5	32.2 (21.3)
subtype4	2	25.0 (21.2)
subtype5	2	38.0 (11.3)

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 = 0.957 (Kruskal-Wallis (anova)), Q value = 1

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	3	1978.0 (18.4)
subtype2	3	1985.0 (14.9)
subtype3	3	1978.0 (28.6)
subtype4	2	1981.0 (21.2)
subtype5	1	1966.0 (NA)

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 = 0.0171 (Fisher's exact test), Q value = 0.044

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	18	72
subtype2	1	9	46
subtype3	2	16	166
subtype4	4	6	110
subtype5	0	7	51

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.857 (Fisher's exact test), Q value = 0.99

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	345
subtype1	3	57
subtype2	3	36
subtype3	11	126
subtype4	4	84
subtype5	3	42

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
Number of samples	175	210	131

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 6.47e-05 (logrank test), Q value = 0.00035

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 (39.1)
subtype1	174	75	0.1 - 131.1 (37.2)
subtype2	209	48	0.1 - 149.2 (45.1)
subtype3	131	49	0.1 - 123.1 (35.3)

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 = 0.63 (Kruskal-Wallis (anova)), Q value = 0.79

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	175	60.2 (12.3)
subtype2	210	61.3 (12.3)
subtype3	131	59.9 (11.5)

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 = 0.00014 (Fisher's exact test), Q value = 0.00067

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	253	55	123	83
subtype1	80	14	48	31
subtype2	125	24	42	19
subtype3	48	17	33	33

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 = 0.00012 (Fisher's exact test), Q value = 6e-04

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	82	17	69	7
subtype2	127	28	54	1
subtype3	50	22	56	3

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 = 0.011 (Fisher's exact test), Q value = 0.03

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

nPatients	0	1
ALL	228	17
subtype1	77	10
subtype2	88	1
subtype3	63	6

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 = 0.00076 (Fisher's exact test), Q value = 0.0029

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

nPatients	0	1
ALL	406	78
subtype1	136	29
subtype2	177	18
subtype3	93	31

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00269 (Fisher's exact test), Q value = 0.009

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

nPatients	FEMALE	MALE
ALL	181	335
subtype1	52	123
subtype2	92	118
subtype3	37	94

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

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.361 (Kruskal-Wallis (anova)), Q value = 0.53

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

	nPatients	Mean (Std.Dev)
ALL	54	85.6 (25.8)
subtype1	18	83.9 (31.3)
subtype2	21	92.4 (8.9)
subtype3	15	78.0 (33.0)

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	7	30.9 (20.5)
subtype3	8	28.1 (15.7)

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 = 0.75

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	1975.0 (24.1)
subtype3	4	1987.8 (13.4)

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 = 0.85 (Fisher's exact test), Q value = 0.99

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	20	150
subtype2	2	21	183
subtype3	2	15	112

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.187 (Fisher's exact test), Q value = 0.31

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	345
subtype1	6	117
subtype2	14	133
subtype3	4	95

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	39	22	45	38

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

P value = 0.000347 (logrank test), Q value = 0.0015

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 (35.9)
subtype1	37	8	0.1 - 116.8 (22.6)
subtype2	22	4	6.9 - 131.1 (41.6)
subtype3	45	11	2.1 - 149.2 (53.6)
subtype4	38	21	0.5 - 115.7 (28.9)

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 = 0.43 (Kruskal-Wallis (anova)), Q value = 0.61

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	39	59.6 (10.8)
subtype2	22	57.4 (12.2)
subtype3	45	62.2 (12.4)
subtype4	38	61.3 (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 = 0.0414 (Fisher's exact test), Q value = 0.086

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	74	17	26	26
subtype1	25	5	4	4
subtype2	15	1	4	2
subtype3	21	7	11	6
subtype4	13	4	7	14

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 = 0.0177 (Fisher's exact test), Q value = 0.044

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	26	6	7	0
subtype2	16	1	4	1
subtype3	21	7	17	0
subtype4	14	5	15	4

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 = 0.119 (Fisher's exact test), Q value = 0.21

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

nPatients	0	1
ALL	56	5
subtype1	8	0
subtype2	9	1
subtype3	22	0
subtype4	17	4

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 = 0.0179 (Fisher's exact test), Q value = 0.044

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

nPatients	0	1
ALL	98	23
subtype1	16	3
subtype2	20	1
subtype3	39	6
subtype4	23	13

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 = 0.154 (Fisher's exact test), Q value = 0.26

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

nPatients	FEMALE	MALE
ALL	50	94
subtype1	17	22
subtype2	7	15
subtype3	18	27
subtype4	8	30

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 = 0.908 (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)
subtype2	1	90.0 (NA)
subtype3	8	92.5 (10.4)
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 = 6.9e-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	15
subtype2	1	2	18
subtype3	0	0	45
subtype4	2	4	30

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 = 0.218 (Fisher's exact test), Q value = 0.35

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	33
subtype2	1	12
subtype3	4	35
subtype4	1	24

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	4
Number of samples	41	52	28	23

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

P value = 0.426 (logrank test), Q value = 0.61

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 (35.9)
subtype1	39	9	0.1 - 116.8 (24.8)
subtype2	52	21	0.5 - 133.7 (37.8)
subtype3	28	8	10.2 - 129.7 (37.8)
subtype4	23	6	2.1 - 149.2 (58.9)

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 = 0.0892 (Kruskal-Wallis (anova)), Q value = 0.17

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	41	60.8 (11.2)
subtype2	52	57.5 (11.5)
subtype3	28	62.4 (13.0)
subtype4	23	64.7 (9.8)

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 = 0.031 (Fisher's exact test), Q value = 0.067

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	74	17	26	26
subtype1	28	4	4	5
subtype2	24	3	11	13
subtype3	10	8	4	6
subtype4	12	2	7	2

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 = 0.00743 (Fisher's exact test), Q value = 0.022

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	29	5	6	1
subtype2	25	3	20	4
subtype3	11	9	8	0
subtype4	12	2	9	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 = 0.42 (Fisher's exact test), Q value = 0.61

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

nPatients	0	1
ALL	56	5
subtype1	9	1
subtype2	24	4
subtype3	13	0
subtype4	10	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 = 0.635 (Fisher's exact test), Q value = 0.79

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	39	11
subtype3	22	6
subtype4	20	2

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 = 0.0085 (Fisher's exact test), Q value = 0.024

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

nPatients	FEMALE	MALE
ALL	50	94
subtype1	19	22
subtype2	9	43
subtype3	12	16
subtype4	10	13

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 = 0.373 (Kruskal-Wallis (anova)), Q value = 0.54

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	9	88.9 (12.7)
subtype2	4	97.5 (5.0)
subtype3	4	87.5 (12.6)
subtype4	4	95.0 (5.8)

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 = 0.437 (Kruskal-Wallis (anova)), Q value = 0.61

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	6	30.0 (20.9)
subtype2	3	14.7 (5.0)
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 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 6.9e-05

Table S144. 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	24	17
subtype2	3	4	44
subtype3	0	1	26
subtype4	0	1	21

Figure S132. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.162 (Fisher's exact test), Q value = 0.27

Table S145. 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	35
subtype2	2	32
subtype3	2	19
subtype4	2	18

Figure S133. 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/22555002/KIRC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIRC-TP/22506714/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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