Correlation between aggregated molecular cancer subtypes and selected clinical features

Pan-kidney cohort (KICH+KIRC+KIRP) (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/C1BR8RD8

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 14 clinical features across 893 patients, 91 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', and 'RACE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 5 subtypes that correlate to 'PATHOLOGY_T_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 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_M_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'YEAR_OF_TOBACCO_SMOKING_ONSET', 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', 'HISTOLOGICAL_TYPE', and 'RACE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'RACE'.
Consensus hierarchical clustering analysis on RPPA data identified 7 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', and 'RACE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', and 'RACE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'HISTOLOGICAL_TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 91 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.215 (0.308)	0.0983 (0.162)	0.00139 (0.0033)	3.47e-05 (0.000101)	1.45e-09 (4.88e-08)	6.83e-12 (3.82e-10)	0 (0)	1.01e-14 (8.49e-13)	5.34e-08 (1.5e-06)	0.000157 (0.000405)	1.61e-07 (3.86e-06)	1.84e-10 (7.74e-09)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.222 (0.316)	0.214 (0.308)	0.227 (0.321)	2.12e-07 (4.45e-06)	0.0502 (0.0917)	0.00353 (0.0077)	0.00018 (0.000452)	1.25e-05 (3.89e-05)	0.319 (0.409)	1.56e-05 (4.76e-05)	0.00156 (0.00359)	0.0168 (0.034)
PATHOLOGIC STAGE	Fisher's exact test	0.033 (0.063)	0.0637 (0.113)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)
PATHOLOGY T STAGE	Fisher's exact test	0.0117 (0.0243)	0.0225 (0.0445)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)
PATHOLOGY N STAGE	Fisher's exact test	0.0768 (0.132)	0.325 (0.411)	0.191 (0.284)	0.00014 (0.000367)	0.322 (0.41)	0.00024 (0.000593)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	0.00068 (0.00163)	1e-05 (3.17e-05)
PATHOLOGY M STAGE	Fisher's exact test	0.183 (0.278)	0.244 (0.336)	0.00214 (0.00479)	0.0059 (0.0125)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	0.00018 (0.000452)	1e-05 (3.17e-05)	0.03 (0.058)	0.0905 (0.152)	0.0403 (0.0761)	0.00595 (0.0125)
GENDER	Fisher's exact test	0.738 (0.805)	0.00014 (0.000367)	3e-05 (8.84e-05)	5e-05 (0.000142)	0.394 (0.487)	0.0247 (0.0483)	0.00011 (0.000298)	1e-05 (3.17e-05)	0.141 (0.219)	0.316 (0.409)	0.00048 (0.00117)	0.00164 (0.00372)
RADIATION THERAPY	Fisher's exact test			0.187 (0.281)	0.465 (0.55)	0.381 (0.475)	0.769 (0.828)	0.458 (0.546)	0.266 (0.361)	0.194 (0.285)	0.581 (0.657)	0.298 (0.391)	0.505 (0.585)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)			0.121 (0.194)	0.671 (0.747)	0.471 (0.553)	0.0416 (0.0777)	0.27 (0.362)	0.00151 (0.00353)	0.258 (0.353)	0.0817 (0.139)	0.0434 (0.0801)	0.161 (0.249)
HISTOLOGICAL TYPE	Fisher's exact test	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	8e-05 (0.000224)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)			0.0722 (0.125)	0.86 (0.915)	0.545 (0.623)	0.968 (1.00)	0.928 (0.98)	0.502 (0.585)	0.717 (0.787)	0.406 (0.498)	0.532 (0.612)	0.214 (0.308)
YEAR OF TOBACCO SMOKING ONSET	Kruskal-Wallis (anova)			0.00589 (0.0125)	0.1 (0.164)	0.745 (0.807)	0.453 (0.546)	0.586 (0.657)	0.271 (0.362)	0.132 (0.208)	0.0941 (0.156)	0.129 (0.205)	0.282 (0.373)
RACE	Fisher's exact test	1e-05 (3.17e-05)	1e-05 (3.17e-05)	0.0001 (0.000275)	0.00226 (0.005)	0.0201 (0.0403)	0.014 (0.0287)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	1e-05 (3.17e-05)	2e-05 (6e-05)	0.814 (0.871)	0.0618 (0.112)
ETHNICITY	Fisher's exact test	0.109 (0.176)	0.0668 (0.117)	0.317 (0.409)	0.242 (0.336)	0.587 (0.657)	0.244 (0.336)	0.348 (0.437)	0.0637 (0.113)	0.456 (0.546)	0.419 (0.51)	0.69 (0.762)	0.183 (0.278)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	34	24	13	17

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.215 (logrank test), Q value = 0.31

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	19	0.5 - 117.8 (37.3)
subtype1	34	5	1.4 - 115.0 (37.1)
subtype2	24	8	0.5 - 114.4 (46.1)
subtype3	13	1	12.1 - 117.8 (40.0)
subtype4	17	5	0.5 - 76.6 (28.2)

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 = 0.222 (Kruskal-Wallis (anova)), Q value = 0.32

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

	nPatients	Mean (Std.Dev)
ALL	87	60.1 (12.2)
subtype1	33	60.2 (13.8)
subtype2	24	59.2 (10.8)
subtype3	13	65.6 (10.5)
subtype4	17	56.9 (11.2)

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 = 0.033 (Fisher's exact test), Q value = 0.063

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	45	16	15	6
subtype1	23	4	6	1
subtype2	8	4	8	4
subtype3	8	5	0	0
subtype4	6	3	1	1

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 = 0.0117 (Fisher's exact test), Q value = 0.024

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

nPatients	T1	T2	T3
ALL	48	21	19
subtype1	23	4	7
subtype2	9	5	10
subtype3	8	5	0
subtype4	8	7	2

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 = 0.0768 (Fisher's exact test), Q value = 0.13

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

nPatients	N0	N1
ALL	37	4
subtype1	18	0
subtype2	11	3
subtype3	5	0
subtype4	3	1

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 = 0.183 (Fisher's exact test), Q value = 0.28

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

nPatients	0	1
ALL	77	6
subtype1	33	1
subtype2	20	4
subtype3	13	0
subtype4	11	1

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 = 0.738 (Fisher's exact test), Q value = 0.81

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

nPatients	FEMALE	MALE
ALL	33	55
subtype1	15	19
subtype2	9	15
subtype3	4	9
subtype4	5	12

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	72	16
subtype1	34	0
subtype2	23	1
subtype3	12	1
subtype4	3	14

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	71
subtype1	0	0	32
subtype2	0	1	21
subtype3	0	2	11
subtype4	1	9	7

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.109 (Fisher's exact test), Q value = 0.18

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	58
subtype1	5	19
subtype2	2	11
subtype3	0	12
subtype4	0	16

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S12. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	21	24	18	13	12

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0983 (logrank test), Q value = 0.16

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	19	0.5 - 117.8 (37.3)
subtype1	21	2	1.4 - 115.0 (32.0)
subtype2	24	8	0.5 - 114.4 (46.1)
subtype3	18	6	0.5 - 76.6 (27.3)
subtype4	13	3	11.1 - 106.2 (48.4)
subtype5	12	0	12.1 - 117.8 (40.4)

Figure S11. 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 = 0.214 (Kruskal-Wallis (anova)), Q value = 0.31

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

	nPatients	Mean (Std.Dev)
ALL	87	60.1 (12.2)
subtype1	20	57.6 (13.7)
subtype2	24	59.2 (10.8)
subtype3	18	57.0 (10.9)
subtype4	13	64.1 (13.5)
subtype5	12	66.2 (10.8)

Figure S12. 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 = 0.0637 (Fisher's exact test), Q value = 0.11

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	45	16	15	6
subtype1	16	2	3	0
subtype2	8	4	8	4
subtype3	7	3	1	1
subtype4	7	2	3	1
subtype5	7	5	0	0

Figure S13. 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 = 0.0225 (Fisher's exact test), Q value = 0.044

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	48	21	19
subtype1	16	2	3
subtype2	9	5	10
subtype3	9	7	2
subtype4	7	2	4
subtype5	7	5	0

Figure S14. 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 = 0.325 (Fisher's exact test), Q value = 0.41

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1
ALL	37	4
subtype1	9	0
subtype2	11	3
subtype3	4	1
subtype4	9	0
subtype5	4	0

Figure S15. 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 = 0.244 (Fisher's exact test), Q value = 0.34

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	77	6
subtype1	21	0
subtype2	20	4
subtype3	12	1
subtype4	12	1
subtype5	12	0

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00014 (Fisher's exact test), Q value = 0.00037

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	55
subtype1	3	18
subtype2	9	15
subtype3	5	13
subtype4	12	1
subtype5	4	8

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	72	16
subtype1	21	0
subtype2	23	1
subtype3	4	14
subtype4	13	0
subtype5	11	1

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	71
subtype1	0	0	19
subtype2	0	1	21
subtype3	1	10	7
subtype4	0	0	13
subtype5	0	1	11

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0668 (Fisher's exact test), Q value = 0.12

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	58
subtype1	2	12
subtype2	2	11
subtype3	0	16
subtype4	3	7
subtype5	0	12

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

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

Table S23. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	281	310	290

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

P value = 0.00139 (logrank test), Q value = 0.0033

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	873	228	0.1 - 194.8 (34.5)
subtype1	276	48	0.1 - 194.8 (29.2)
subtype2	308	80	0.1 - 149.2 (34.9)
subtype3	289	100	0.1 - 141.7 (38.6)

Figure S21. 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 = 0.227 (Kruskal-Wallis (anova)), Q value = 0.32

Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	875	60.1 (12.5)
subtype1	279	59.7 (13.2)
subtype2	308	59.6 (12.0)
subtype3	288	61.2 (12.3)

Figure S22. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	455	104	191	104
subtype1	158	39	46	16
subtype2	181	32	59	34
subtype3	116	33	86	54

Figure S23. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S27. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	479	127	258	15
subtype1	172	48	53	6
subtype2	187	40	80	3
subtype3	120	39	125	6

Figure S24. 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 = 0.191 (Fisher's exact test), Q value = 0.28

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	322	44	7
subtype1	78	14	3
subtype2	117	9	2
subtype3	127	21	2

Figure S25. 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 = 0.00214 (Fisher's exact test), Q value = 0.0048

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	545	89
subtype1	116	8
subtype2	222	32
subtype3	207	49

Figure S26. 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 = 3e-05 (Fisher's exact test), Q value = 8.8e-05

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	292	589
subtype1	70	211
subtype2	132	178
subtype3	90	200

Figure S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.187 (Fisher's exact test), Q value = 0.28

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	400	3
subtype1	203	1
subtype2	114	0
subtype3	83	2

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.121 (Kruskal-Wallis (anova)), Q value = 0.19

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	142	87.6 (22.8)
subtype1	64	91.7 (14.3)
subtype2	36	92.5 (10.5)
subtype3	42	77.1 (34.9)

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	528	287
subtype1	46	29	206
subtype2	6	250	54
subtype3	14	249	27

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0722 (Kruskal-Wallis (anova)), Q value = 0.13

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	106	30.8 (24.9)
subtype1	60	30.8 (18.7)
subtype2	26	38.6 (38.5)
subtype3	20	20.6 (13.7)

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.00589 (Kruskal-Wallis (anova)), Q value = 0.012

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	74	1973.7 (16.0)
subtype1	41	1968.4 (13.9)
subtype2	20	1981.1 (17.8)
subtype3	13	1979.2 (13.7)

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 1e-04 (Fisher's exact test), Q value = 0.00028

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	121	719
subtype1	2	8	45	212
subtype2	0	2	55	250
subtype3	0	5	21	257

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.317 (Fisher's exact test), Q value = 0.41

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	42	621
subtype1	10	216
subtype2	16	215
subtype3	16	190

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S38. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	129	305	225

'METHLYATION CNMF' versus 'Time to Death'

P value = 3.47e-05 (logrank test), Q value = 1e-04

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	651	154	0.1 - 194.8 (30.2)
subtype1	126	30	0.2 - 152.0 (42.6)
subtype2	304	99	0.1 - 149.2 (35.6)
subtype3	221	25	0.1 - 194.8 (25.1)

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 2.12e-07 (Kruskal-Wallis (anova)), Q value = 4.4e-06

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	654	60.5 (12.6)
subtype1	128	54.3 (14.1)
subtype2	303	61.7 (11.8)
subtype3	223	62.4 (11.5)

Figure S36. 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 = 3.2e-05

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	345	75	138	79
subtype1	55	30	27	15
subtype2	140	28	78	56
subtype3	150	17	33	8

Figure S37. 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 = 3.2e-05

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

nPatients	T1	T2	T3	T4
ALL	366	92	187	12
subtype1	56	31	36	6
subtype2	145	39	116	5
subtype3	165	22	35	1

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00014 (Fisher's exact test), Q value = 0.00037

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	220	34	7
subtype1	55	13	3
subtype2	126	9	0
subtype3	39	12	4

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0059 (Fisher's exact test), Q value = 0.012

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	352	63
subtype1	67	9
subtype2	221	51
subtype3	64	3

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 5e-05 (Fisher's exact test), Q value = 0.00014

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	214	445
subtype1	60	69
subtype2	102	203
subtype3	52	173

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.465 (Fisher's exact test), Q value = 0.55

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	377	3
subtype1	98	0
subtype2	110	2
subtype3	169	1

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.671 (Kruskal-Wallis (anova)), Q value = 0.75

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	133	89.8 (17.7)
subtype1	25	90.8 (15.3)
subtype2	39	89.2 (17.4)
subtype3	69	89.9 (18.9)

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	319	274
subtype1	66	27	36
subtype2	0	288	17
subtype3	0	4	221

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.86 (Kruskal-Wallis (anova)), Q value = 0.91

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	102	31.3 (25.2)
subtype1	17	27.4 (19.7)
subtype2	24	35.1 (36.1)
subtype3	61	30.8 (21.4)

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.1 (Kruskal-Wallis (anova)), Q value = 0.16

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	72	1973.0 (16.0)
subtype1	13	1976.8 (14.1)
subtype2	15	1979.3 (20.1)
subtype3	44	1969.8 (14.4)

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00226 (Fisher's exact test), Q value = 0.005

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	8	107	522
subtype1	0	5	25	95
subtype2	0	2	38	260
subtype3	2	1	44	167

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.242 (Fisher's exact test), Q value = 0.34

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	26	518
subtype1	7	81
subtype2	9	244
subtype3	10	193

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S53. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	185	184	189	197

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 1.45e-09 (logrank test), Q value = 4.9e-08

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	751	206	0.1 - 194.8 (36.4)
subtype1	185	72	0.1 - 152.0 (35.0)
subtype2	183	36	0.2 - 194.8 (37.1)
subtype3	187	22	0.9 - 137.1 (37.0)
subtype4	196	76	0.1 - 130.7 (35.2)

Figure S49. 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 = 0.0502 (Kruskal-Wallis (anova)), Q value = 0.092

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	750	60.0 (12.6)
subtype1	185	58.3 (12.1)
subtype2	182	59.3 (13.4)
subtype3	188	61.6 (12.7)
subtype4	195	60.8 (11.9)

Figure S50. 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 = 3.2e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	383	90	173	101
subtype1	75	22	50	37
subtype2	103	22	41	16
subtype3	128	20	33	6
subtype4	77	26	49	42

Figure S51. 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 = 3.2e-05

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	395	109	236	15
subtype1	78	25	76	6
subtype2	104	27	51	2
subtype3	131	20	38	0
subtype4	82	37	71	7

Figure S52. 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 = 0.322 (Fisher's exact test), Q value = 0.41

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

nPatients	N0	N1	N2
ALL	299	39	6
subtype1	84	17	3
subtype2	65	5	1
subtype3	67	5	1
subtype4	83	12	1

Figure S53. 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 = 3.2e-05

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

nPatients	0	1
ALL	486	86
subtype1	116	33
subtype2	120	12
subtype3	122	5
subtype4	128	36

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.394 (Fisher's exact test), Q value = 0.49

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	243	512
subtype1	64	121
subtype2	63	121
subtype3	62	127
subtype4	54	143

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.381 (Fisher's exact test), Q value = 0.47

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	324	3
subtype1	76	1
subtype2	85	2
subtype3	97	0
subtype4	66	0

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

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.471 (Kruskal-Wallis (anova)), Q value = 0.55

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

	nPatients	Mean (Std.Dev)
ALL	117	89.7 (19.8)
subtype1	29	86.9 (26.2)
subtype2	34	91.2 (15.7)
subtype3	26	95.0 (6.5)
subtype4	28	86.1 (24.2)

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 8e-05 (Fisher's exact test), Q value = 0.00022

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

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	63	478	214
subtype1	17	122	46
subtype2	17	112	55
subtype3	21	96	72
subtype4	8	148	41

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

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.545 (Kruskal-Wallis (anova)), Q value = 0.62

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

	nPatients	Mean (Std.Dev)
ALL	84	29.8 (24.4)
subtype1	18	28.7 (16.3)
subtype2	21	25.9 (20.0)
subtype3	24	31.6 (35.5)
subtype4	21	32.6 (19.4)

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

'RPPA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.745 (Kruskal-Wallis (anova)), Q value = 0.81

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	60	1971.7 (15.2)
subtype1	14	1969.1 (12.2)
subtype2	15	1973.5 (17.2)
subtype3	16	1970.1 (14.8)
subtype4	15	1974.0 (17.1)

Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0201 (Fisher's exact test), Q value = 0.04

Table S66. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	15	93	625
subtype1	0	9	24	146
subtype2	0	1	23	153
subtype3	1	2	30	153
subtype4	0	3	16	173

Figure S61. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.587 (Fisher's exact test), Q value = 0.66

Table S67. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	522
subtype1	7	125
subtype2	12	135
subtype3	6	125
subtype4	7	137

Figure S62. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S68. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	200	65	116	149	59	71	95

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 6.83e-12 (logrank test), Q value = 3.8e-10

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	751	206	0.1 - 194.8 (36.4)
subtype1	199	47	0.1 - 194.8 (35.9)
subtype2	64	3	1.1 - 137.1 (35.0)
subtype3	116	56	0.1 - 117.8 (33.1)
subtype4	148	29	0.1 - 129.9 (46.2)
subtype5	58	23	1.9 - 118.8 (36.4)
subtype6	71	8	0.6 - 152.0 (22.3)
subtype7	95	40	0.2 - 133.7 (30.6)

Figure S63. 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 = 0.00353 (Kruskal-Wallis (anova)), Q value = 0.0077

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	750	60.0 (12.6)
subtype1	198	60.0 (11.9)
subtype2	65	62.4 (13.2)
subtype3	116	59.7 (12.6)
subtype4	147	61.0 (12.6)
subtype5	58	64.2 (12.3)
subtype6	71	58.1 (12.1)
subtype7	95	56.1 (13.2)

Figure S64. 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 = 3.2e-05

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	383	90	173	101
subtype1	102	27	41	27
subtype2	38	9	15	1
subtype3	32	17	36	29
subtype4	101	17	24	7
subtype5	19	7	22	11
subtype6	44	8	14	4
subtype7	47	5	21	22

Figure S65. 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 = 3.2e-05

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	395	109	236	15
subtype1	103	37	59	1
subtype2	40	9	16	0
subtype3	34	19	52	11
subtype4	102	18	28	1
subtype5	23	9	27	0
subtype6	45	9	17	0
subtype7	48	8	37	2

Figure S66. 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 = 0.00024 (Fisher's exact test), Q value = 0.00059

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	299	39	6
subtype1	87	4	0
subtype2	19	3	1
subtype3	60	12	0
subtype4	46	1	1
subtype5	27	4	1
subtype6	25	2	0
subtype7	35	13	3

Figure S67. 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 = 3.2e-05

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	486	86
subtype1	125	24
subtype2	28	0
subtype3	71	25
subtype4	121	5
subtype5	40	9
subtype6	41	4
subtype7	60	19

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0247 (Fisher's exact test), Q value = 0.048

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	243	512
subtype1	59	141
subtype2	20	45
subtype3	27	89
subtype4	63	86
subtype5	15	44
subtype6	23	48
subtype7	36	59

Figure S69. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.769 (Fisher's exact test), Q value = 0.83

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	324	3
subtype1	102	2
subtype2	47	0
subtype3	44	0
subtype4	42	0
subtype5	16	0
subtype6	38	0
subtype7	35	1

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0416 (Kruskal-Wallis (anova)), Q value = 0.078

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	117	89.7 (19.8)
subtype1	33	88.5 (21.1)
subtype2	14	96.4 (6.3)
subtype3	15	96.0 (7.4)
subtype4	20	93.5 (7.5)
subtype5	7	74.3 (33.1)
subtype6	13	93.1 (7.5)
subtype7	15	79.3 (34.7)

Figure S71. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	63	478	214
subtype1	19	122	59
subtype2	18	1	46
subtype3	19	73	24
subtype4	4	120	25
subtype5	1	49	9
subtype6	1	44	26
subtype7	1	69	25

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

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

Table S79. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	84	29.8 (24.4)
subtype1	24	26.5 (21.1)
subtype2	14	38.2 (45.4)
subtype3	11	29.2 (14.4)
subtype4	9	29.7 (22.1)
subtype5	9	30.9 (17.8)
subtype6	8	25.6 (11.5)
subtype7	9	29.1 (14.6)

Figure S73. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.453 (Kruskal-Wallis (anova)), Q value = 0.55

Table S80. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	60	1971.7 (15.2)
subtype1	18	1967.4 (15.9)
subtype2	11	1968.2 (14.0)
subtype3	5	1969.4 (9.2)
subtype4	4	1987.0 (15.2)
subtype5	7	1972.4 (13.9)
subtype6	6	1975.8 (17.4)
subtype7	9	1975.6 (16.2)

Figure S74. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.014 (Fisher's exact test), Q value = 0.029

Table S81. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	15	93	625
subtype1	0	1	18	175
subtype2	1	0	14	48
subtype3	0	5	12	96
subtype4	0	1	20	124
subtype5	0	2	5	51
subtype6	0	1	12	57
subtype7	0	5	12	74

Figure S75. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.244 (Fisher's exact test), Q value = 0.34

Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	522
subtype1	10	151
subtype2	5	49
subtype3	2	75
subtype4	8	90
subtype5	3	38
subtype6	0	50
subtype7	4	69

Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S83. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	121	196	300	271

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	880	228	0.1 - 194.8 (34.9)
subtype1	118	18	0.2 - 152.0 (50.5)
subtype2	196	98	0.1 - 133.7 (29.1)
subtype3	299	73	0.1 - 149.2 (45.1)
subtype4	267	39	0.1 - 194.8 (25.2)

Figure S77. 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 = 0.00018 (Kruskal-Wallis (anova)), Q value = 0.00045

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	882	60.2 (12.5)
subtype1	120	54.9 (14.3)
subtype2	196	60.5 (12.0)
subtype3	299	60.6 (12.5)
subtype4	267	61.8 (11.4)

Figure S78. 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 = 3.2e-05

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	461	104	190	105
subtype1	57	33	19	10
subtype2	58	20	69	49
subtype3	174	31	60	34
subtype4	172	20	42	12

Figure S79. 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 = 3.2e-05

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	486	127	258	15
subtype1	58	33	28	2
subtype2	62	25	99	10
subtype3	176	38	84	2
subtype4	190	31	47	1

Figure S80. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S88. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	329	43	7
subtype1	60	5	3
subtype2	86	20	1
subtype3	131	1	0
subtype4	52	17	3

Figure S81. 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 = 0.00018 (Fisher's exact test), Q value = 0.00045

Table S89. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	550	90
subtype1	72	7
subtype2	140	44
subtype3	248	32
subtype4	90	7

Figure S82. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00011 (Fisher's exact test), Q value = 3e-04

Table S90. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	291	597
subtype1	48	73
subtype2	51	145
subtype3	122	178
subtype4	70	201

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.458 (Fisher's exact test), Q value = 0.55

Table S91. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	406	3
subtype1	87	0
subtype2	36	0
subtype3	89	2
subtype4	194	1

Figure S84. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.27 (Kruskal-Wallis (anova)), Q value = 0.36

Table S92. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	142	88.3 (21.6)
subtype1	21	94.3 (11.2)
subtype2	20	76.5 (35.9)
subtype3	28	91.8 (9.4)
subtype4	73	88.5 (21.3)

Figure S85. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S93. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	533	289
subtype1	64	39	18
subtype2	2	182	12
subtype3	0	297	3
subtype4	0	15	256

Figure S86. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.928 (Kruskal-Wallis (anova)), Q value = 0.98

Table S94. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	107	30.6 (24.8)
subtype1	14	38.6 (46.8)
subtype2	8	29.1 (17.1)
subtype3	11	26.3 (18.4)
subtype4	74	29.9 (20.3)

Figure S87. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.586 (Kruskal-Wallis (anova)), Q value = 0.66

Table S95. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	75	1973.6 (15.9)
subtype1	11	1972.7 (16.3)
subtype2	6	1980.0 (14.7)
subtype3	6	1978.5 (21.9)
subtype4	52	1972.4 (15.5)

Figure S88. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S96. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	121	726
subtype1	0	3	20	95
subtype2	0	4	13	176
subtype3	0	2	28	265
subtype4	2	6	60	190

Figure S89. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.348 (Fisher's exact test), Q value = 0.44

Table S97. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	42	628
subtype1	4	79
subtype2	7	125
subtype3	19	195
subtype4	12	229

Figure S90. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S98. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	90	145	210	160	111	172

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1.01e-14 (logrank test), Q value = 8.5e-13

Table S99. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	880	228	0.1 - 194.8 (34.9)
subtype1	89	12	2.5 - 152.0 (54.7)
subtype2	143	42	0.2 - 194.8 (19.7)
subtype3	210	48	0.1 - 131.2 (37.9)
subtype4	159	79	0.1 - 133.7 (36.2)
subtype5	110	30	0.1 - 149.2 (49.0)
subtype6	169	17	0.1 - 129.9 (26.0)

Figure S91. 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 = 1.25e-05 (Kruskal-Wallis (anova)), Q value = 3.9e-05

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	882	60.2 (12.5)
subtype1	90	54.0 (14.2)
subtype2	143	61.6 (12.9)
subtype3	209	58.9 (12.8)
subtype4	159	61.6 (11.3)
subtype5	111	62.9 (12.0)
subtype6	170	60.8 (11.3)

Figure S92. 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 = 3.2e-05

Table S101. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	461	104	190	105
subtype1	38	30	15	5
subtype2	64	8	44	22
subtype3	123	21	39	26
subtype4	39	20	60	41
subtype5	72	9	22	8
subtype6	125	16	10	3

Figure S93. 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 = 3.2e-05

Table S102. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	486	127	258	15
subtype1	39	30	20	1
subtype2	70	14	54	7
subtype3	125	27	56	2
subtype4	43	24	88	5
subtype5	72	10	29	0
subtype6	137	22	11	0

Figure S94. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S103. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	329	43	7
subtype1	51	2	2
subtype2	35	28	4
subtype3	77	2	0
subtype4	77	11	0
subtype5	60	0	0
subtype6	29	0	1

Figure S95. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S104. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	550	90
subtype1	55	3
subtype2	64	14
subtype3	177	24
subtype4	109	40
subtype5	93	8
subtype6	52	1

Figure S96. 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 = 3.2e-05

Table S105. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	291	597
subtype1	34	56
subtype2	59	86
subtype3	12	198
subtype4	48	112
subtype5	108	3
subtype6	30	142

Figure S97. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.266 (Fisher's exact test), Q value = 0.36

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

nPatients	NO	YES
ALL	406	3
subtype1	77	0
subtype2	79	0
subtype3	54	2
subtype4	31	0
subtype5	35	0
subtype6	130	1

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

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00151 (Kruskal-Wallis (anova)), Q value = 0.0035

Table S107. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	142	88.3 (21.6)
subtype1	15	97.3 (4.6)
subtype2	28	76.4 (32.1)
subtype3	19	95.3 (7.7)
subtype4	15	72.7 (38.4)
subtype5	13	89.2 (10.4)
subtype6	52	93.8 (8.0)

Figure S99. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S108. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	533	289
subtype1	64	19	7
subtype2	2	34	109
subtype3	0	209	1
subtype4	0	156	4
subtype5	0	111	0
subtype6	0	4	168

Figure S100. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.502 (Kruskal-Wallis (anova)), Q value = 0.59

Table S109. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	107	30.6 (24.8)
subtype1	12	27.2 (22.1)
subtype2	28	37.0 (36.7)
subtype3	6	34.7 (20.6)
subtype4	11	27.5 (16.7)
subtype5	3	13.0 (6.1)
subtype6	47	29.1 (18.6)

Figure S101. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.271 (Kruskal-Wallis (anova)), Q value = 0.36

Table S110. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	75	1973.6 (15.9)
subtype1	9	1976.0 (15.9)
subtype2	23	1974.2 (14.9)
subtype3	5	1976.0 (23.5)
subtype4	7	1983.7 (16.0)
subtype6	31	1969.7 (15.1)

Figure S102. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S111. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	121	726
subtype1	0	2	10	76
subtype2	0	5	38	97
subtype3	0	3	15	187
subtype4	0	2	12	145
subtype5	0	1	12	97
subtype6	2	2	34	124

Figure S103. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0637 (Fisher's exact test), Q value = 0.11

Table S112. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	42	628
subtype1	4	53
subtype2	8	113
subtype3	9	134
subtype4	5	111
subtype5	11	67
subtype6	5	150

Figure S104. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	331	195	346

'MIRSEQ CNMF' versus 'Time to Death'

P value = 5.34e-08 (logrank test), Q value = 1.5e-06

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	864	225	0.1 - 194.8 (34.4)
subtype1	325	45	0.1 - 194.8 (29.4)
subtype2	194	70	0.1 - 131.1 (29.0)
subtype3	345	110	0.2 - 149.2 (42.4)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.319 (Kruskal-Wallis (anova)), Q value = 0.41

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

	nPatients	Mean (Std.Dev)
ALL	867	60.2 (12.5)
subtype1	327	59.7 (13.0)
subtype2	195	59.3 (12.4)
subtype3	345	61.1 (12.0)

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

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	448	102	190	104
subtype1	185	48	56	16
subtype2	90	17	52	34
subtype3	173	37	82	54

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

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S117. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	473	125	257	15
subtype1	204	59	62	4
subtype2	92	19	75	9
subtype3	177	47	120	2

Figure S108. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S118. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	317	44	7
subtype1	89	22	3
subtype2	76	16	4
subtype3	152	6	0

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

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.03 (Fisher's exact test), Q value = 0.058

Table S119. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	534	89
subtype1	130	11
subtype2	124	26
subtype3	280	52

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.141 (Fisher's exact test), Q value = 0.22

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

nPatients	FEMALE	MALE
ALL	285	587
subtype1	95	236
subtype2	69	126
subtype3	121	225

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.194 (Fisher's exact test), Q value = 0.29

Table S121. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	402	3
subtype1	249	1
subtype2	65	0
subtype3	88	2

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

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.258 (Kruskal-Wallis (anova)), Q value = 0.35

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

	nPatients	Mean (Std.Dev)
ALL	143	87.7 (22.8)
subtype1	79	90.8 (18.0)
subtype2	28	79.6 (32.1)
subtype3	36	87.2 (22.6)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S123. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	516	290
subtype1	59	25	247
subtype2	6	148	41
subtype3	1	343	2

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

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.717 (Kruskal-Wallis (anova)), Q value = 0.79

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

	nPatients	Mean (Std.Dev)
ALL	108	30.4 (24.8)
subtype1	77	30.2 (27.0)
subtype2	20	31.8 (18.7)
subtype3	11	28.9 (19.0)

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

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.132 (Kruskal-Wallis (anova)), Q value = 0.21

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

	nPatients	Mean (Std.Dev)
ALL	76	1973.4 (15.9)
subtype1	55	1971.2 (15.1)
subtype2	15	1979.4 (14.2)
subtype3	6	1979.5 (23.4)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S126. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	121	710
subtype1	2	8	59	247
subtype2	0	3	40	149
subtype3	0	4	22	314

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.456 (Fisher's exact test), Q value = 0.55

Table S127. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	40	619
subtype1	14	258
subtype2	7	130
subtype3	19	231

Figure S118. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S128. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	78	308	486

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.000157 (logrank test), Q value = 4e-04

Table S129. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	864	225	0.1 - 194.8 (34.4)
subtype1	77	12	2.5 - 152.0 (66.5)
subtype2	303	56	0.1 - 194.8 (24.0)
subtype3	484	157	0.1 - 149.2 (38.8)

Figure S119. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 1.56e-05 (Kruskal-Wallis (anova)), Q value = 4.8e-05

Table S130. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	867	60.2 (12.5)
subtype1	78	53.3 (14.1)
subtype2	304	61.0 (12.4)
subtype3	485	60.7 (12.0)

Figure S120. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S131. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	448	102	190	104
subtype1	28	29	15	5
subtype2	183	23	52	24
subtype3	237	50	123	75

Figure S121. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S132. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	473	125	257	15
subtype1	29	29	19	1
subtype2	201	34	63	8
subtype3	243	62	175	6

Figure S122. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S133. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	317	44	7
subtype1	49	2	2
subtype2	56	29	5
subtype3	212	13	0

Figure S123. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.0905 (Fisher's exact test), Q value = 0.15

Table S134. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	534	89
subtype1	50	3
subtype2	103	14
subtype3	381	72

Figure S124. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.316 (Fisher's exact test), Q value = 0.41

Table S135. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	285	587
subtype1	28	50
subtype2	91	217
subtype3	166	320

Figure S125. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.581 (Fisher's exact test), Q value = 0.66

Table S136. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	402	3
subtype1	67	0
subtype2	206	1
subtype3	129	2

Figure S126. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0817 (Kruskal-Wallis (anova)), Q value = 0.14

Table S137. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	143	87.7 (22.8)
subtype1	10	98.0 (4.2)
subtype2	79	88.2 (21.0)
subtype3	54	85.0 (26.6)

Figure S127. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

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

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	516	290
subtype1	60	16	2
subtype2	6	26	276
subtype3	0	474	12

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.406 (Kruskal-Wallis (anova)), Q value = 0.5

Table S139. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	108	30.4 (24.8)
subtype1	10	25.6 (23.0)
subtype2	77	32.2 (26.7)
subtype3	21	26.0 (17.2)

Figure S129. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0941 (Kruskal-Wallis (anova)), Q value = 0.16

Table S140. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	76	1973.4 (15.9)
subtype1	7	1975.0 (16.2)
subtype2	56	1971.4 (15.0)
subtype3	13	1981.4 (17.9)

Figure S130. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 2e-05 (Fisher's exact test), Q value = 6e-05

Table S141. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	121	710
subtype1	0	2	6	68
subtype2	2	7	67	217
subtype3	0	6	48	425

Figure S131. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.419 (Fisher's exact test), Q value = 0.51

Table S142. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	40	619
subtype1	4	42
subtype2	13	253
subtype3	23	324

Figure S132. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S143. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	155	71	62	127

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

P value = 1.61e-07 (logrank test), Q value = 3.9e-06

Table S144. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	409	89	0.1 - 194.8 (29.3)
subtype1	153	15	0.1 - 194.8 (25.3)
subtype2	69	10	1.2 - 152.0 (41.3)
subtype3	61	24	0.2 - 129.9 (19.6)
subtype4	126	40	0.1 - 149.2 (35.9)

Figure S133. 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 = 0.00156 (Kruskal-Wallis (anova)), Q value = 0.0036

Table S145. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	411	59.7 (12.6)
subtype1	154	61.5 (11.7)
subtype2	69	53.8 (14.2)
subtype3	62	61.1 (12.6)
subtype4	126	60.1 (11.9)

Figure S134. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S146. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	221	49	81	46
subtype1	103	12	20	5
subtype2	29	17	18	6
subtype3	25	4	16	15
subtype4	64	16	27	20

Figure S135. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S147. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	237	56	111	9
subtype1	115	15	22	1
subtype2	29	17	24	1
subtype3	26	6	25	5
subtype4	67	18	40	2

Figure S136. 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 = 0.00068 (Fisher's exact test), Q value = 0.0016

Table S148. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	125	31	7
subtype1	27	8	1
subtype2	33	6	2
subtype3	15	12	4
subtype4	50	5	0

Figure S137. 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 = 0.0403 (Fisher's exact test), Q value = 0.076

Table S149. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	197	33
subtype1	45	2
subtype2	37	4
subtype3	26	8
subtype4	89	19

Figure S138. 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 = 0.00048 (Fisher's exact test), Q value = 0.0012

Table S150. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	133	282
subtype1	33	122
subtype2	34	37
subtype3	24	38
subtype4	42	85

Figure S139. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.298 (Fisher's exact test), Q value = 0.39

Table S151. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	250	1
subtype1	116	0
subtype2	60	0
subtype3	30	0
subtype4	44	1

Figure S140. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0434 (Kruskal-Wallis (anova)), Q value = 0.08

Table S152. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	94	89.9 (18.6)
subtype1	49	94.5 (7.1)
subtype2	13	90.8 (21.8)
subtype3	15	72.7 (34.5)
subtype4	17	91.2 (11.1)

Figure S141. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S153. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	48	144	223
subtype1	0	1	154
subtype2	46	5	20
subtype3	2	17	43
subtype4	0	121	6

Figure S142. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.532 (Kruskal-Wallis (anova)), Q value = 0.61

Table S154. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	79	31.6 (27.2)
subtype1	46	31.2 (20.8)
subtype2	14	24.7 (20.7)
subtype3	11	33.9 (22.7)
subtype4	8	42.9 (60.4)

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

'MIRseq Mature CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.129 (Kruskal-Wallis (anova)), Q value = 0.21

Table S155. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	59	1972.3 (17.0)
subtype1	34	1967.6 (14.4)
subtype2	12	1979.8 (16.9)
subtype3	7	1977.0 (16.3)
subtype4	6	1978.2 (25.9)

Figure S144. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.814 (Fisher's exact test), Q value = 0.87

Table S156. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	9	71	318
subtype1	2	2	25	120
subtype2	0	2	12	54
subtype3	0	2	14	44
subtype4	0	3	20	100

Figure S145. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.69 (Fisher's exact test), Q value = 0.76

Table S157. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	20	307
subtype1	7	133
subtype2	4	37
subtype3	3	46
subtype4	6	91

Figure S146. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S158. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	48	99	97	125	46

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

P value = 1.84e-10 (logrank test), Q value = 7.7e-09

Table S159. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	409	89	0.1 - 194.8 (29.3)
subtype1	48	4	0.1 - 194.8 (21.5)
subtype2	97	6	0.5 - 123.6 (27.1)
subtype3	95	36	0.2 - 100.9 (20.1)
subtype4	124	36	0.1 - 149.2 (37.0)
subtype5	45	7	2.5 - 152.0 (66.5)

Figure S147. 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 = 0.0168 (Kruskal-Wallis (anova)), Q value = 0.034

Table S160. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	411	59.7 (12.6)
subtype1	48	60.9 (11.9)
subtype2	98	61.1 (11.2)
subtype3	95	60.5 (13.7)
subtype4	124	60.1 (11.7)
subtype5	46	53.1 (14.6)

Figure S148. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S161. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	221	49	81	46
subtype1	40	3	2	1
subtype2	65	8	13	2
subtype3	38	7	30	18
subtype4	65	14	24	21
subtype5	13	17	12	4

Figure S149. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S162. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	237	56	111	9
subtype1	41	2	2	1
subtype2	74	11	14	0
subtype3	41	10	40	6
subtype4	68	16	39	2
subtype5	13	17	16	0

Figure S150. 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 = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S163. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	125	31	7
subtype1	9	1	0
subtype2	17	4	0
subtype3	19	21	5
subtype4	51	3	0
subtype5	29	2	2

Figure S151. 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 = 0.00595 (Fisher's exact test), Q value = 0.012

Table S164. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	197	33
subtype1	15	0
subtype2	29	0
subtype3	39	11
subtype4	86	20
subtype5	28	2

Figure S152. 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 = 0.00164 (Fisher's exact test), Q value = 0.0037

Table S165. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	133	282
subtype1	13	35
subtype2	18	81
subtype3	41	56
subtype4	41	84
subtype5	20	26

Figure S153. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.505 (Fisher's exact test), Q value = 0.59

Table S166. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	250	1
subtype1	36	0
subtype2	73	0
subtype3	51	0
subtype4	46	1
subtype5	44	0

Figure S154. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.161 (Kruskal-Wallis (anova)), Q value = 0.25

Table S167. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	94	89.9 (18.6)
subtype1	16	95.6 (6.3)
subtype2	31	93.5 (8.0)
subtype3	26	79.6 (30.8)
subtype4	16	91.2 (11.5)
subtype5	5	98.0 (4.5)

Figure S155. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.2e-05

Table S168. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	48	144	223
subtype1	0	0	48
subtype2	0	1	98
subtype3	6	19	72
subtype4	0	121	4
subtype5	42	3	1

Figure S156. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.214 (Kruskal-Wallis (anova)), Q value = 0.31

Table S169. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	79	31.6 (27.2)
subtype1	20	30.1 (21.1)
subtype2	23	34.0 (23.9)
subtype3	19	38.1 (38.7)
subtype4	8	21.0 (19.2)
subtype5	9	24.6 (24.2)

Figure S157. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.282 (Kruskal-Wallis (anova)), Q value = 0.37

Table S170. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	59	1972.3 (17.0)
subtype1	14	1969.6 (15.2)
subtype2	16	1966.6 (15.1)
subtype3	18	1975.4 (16.7)
subtype4	5	1983.6 (24.9)
subtype5	6	1974.7 (17.7)

Figure S158. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0618 (Fisher's exact test), Q value = 0.11

Table S171. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	9	71	318
subtype1	0	1	7	38
subtype2	2	1	16	76
subtype3	0	3	25	64
subtype4	0	3	21	99
subtype5	0	1	2	41

Figure S159. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.183 (Fisher's exact test), Q value = 0.28

Table S172. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	20	307
subtype1	3	42
subtype2	3	85
subtype3	4	72
subtype4	6	90
subtype5	4	18

Figure S160. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KIPAN-TP/20125873/KIPAN-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIPAN-TP/19775252/KIPAN-TP.merged_data.txt
Number of patients = 893
Number of clustering approaches = 12
Number of selected clinical features = 14
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

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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