Correlation between aggregated molecular cancer subtypes and selected clinical features

Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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/C19G5KTC

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 10 different clustering approaches and 12 clinical features across 271 patients, 49 significant findings detected with P value < 0.05 and Q value < 0.25.

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

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 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, 49 significant findings detected.


Clinical Features	Statistical Tests	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	0.00509 (0.018)	0.0328 (0.0875)	0.778 (0.826)	2.31e-05 (0.000163)	0.0606 (0.135)	0.00165 (0.00707)	0.078 (0.17)	0.179 (0.312)	0.000222 (0.00133)	9.65e-08 (1.16e-05)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.345 (0.477)	0.248 (0.381)	0.000926 (0.00427)	0.000101 (0.000674)	0.115 (0.242)	0.055 (0.124)	0.409 (0.539)	0.228 (0.36)	0.133 (0.266)	0.135 (0.266)
NEOPLASM DISEASESTAGE	Fisher's exact test	1e-05 (8e-05)	1e-05 (8e-05)	0.64 (0.725)	1e-05 (8e-05)	1e-05 (8e-05)	1e-05 (8e-05)	0.0296 (0.0825)	0.00043 (0.00215)	0.00014 (0.000884)	1e-05 (8e-05)
PATHOLOGY T STAGE	Fisher's exact test	1e-05 (8e-05)	1e-05 (8e-05)	0.164 (0.303)	1e-05 (8e-05)	1e-05 (8e-05)	1e-05 (8e-05)	0.135 (0.266)	0.00127 (0.00564)	0.00057 (0.00274)	1e-05 (8e-05)
PATHOLOGY N STAGE	Fisher's exact test	0.0344 (0.0896)	0.00026 (0.00149)	0.309 (0.455)	0.0176 (0.0529)	0.00621 (0.0209)	1e-05 (8e-05)	0.0376 (0.096)	0.0147 (0.0451)	0.00038 (0.00198)	0.0037 (0.0143)
PATHOLOGY M STAGE	Fisher's exact test	0.0091 (0.0295)	0.031 (0.0847)	0.365 (0.498)	0.0537 (0.124)	0.148 (0.282)	0.0431 (0.106)	0.553 (0.664)	0.65 (0.729)	0.021 (0.0614)	0.0128 (0.0403)
GENDER	Fisher's exact test	0.00029 (0.00158)	0.00413 (0.0151)	0.902 (0.917)	0.0257 (0.0735)	1e-05 (8e-05)	2e-05 (0.00015)	0.174 (0.312)	0.00415 (0.0151)	0.00244 (0.00976)	0.00175 (0.00724)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)	0.246 (0.381)	0.0879 (0.188)	0.152 (0.284)	0.477 (0.603)	0.00628 (0.0209)	0.138 (0.266)	0.199 (0.332)	0.778 (0.826)	0.252 (0.382)	0.182 (0.312)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.735 (0.794)	0.71 (0.782)	0.548 (0.664)	0.552 (0.664)	0.425 (0.549)	0.313 (0.455)	0.828 (0.856)	0.788 (0.83)	0.422 (0.549)	0.312 (0.455)
YEAR OF TOBACCO SMOKING ONSET	Kruskal-Wallis (anova)	0.055 (0.124)	0.611 (0.699)	0.803 (0.838)	0.0502 (0.12)	0.132 (0.266)	0.346 (0.477)	0.689 (0.766)	0.59 (0.687)	0.603 (0.695)	0.729 (0.794)
RACE	Fisher's exact test	0.214 (0.348)	0.215 (0.348)	0.582 (0.687)	0.321 (0.459)	0.182 (0.312)	0.59 (0.687)	0.341 (0.477)	0.0402 (0.101)	0.896 (0.917)	0.384 (0.518)
ETHNICITY	Fisher's exact test	0.529 (0.655)	0.179 (0.312)	0.44 (0.562)	0.314 (0.455)	1 (1.00)	0.194 (0.327)	0.493 (0.617)	0.401 (0.535)	1 (1.00)	0.218 (0.349)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	104	72	44	49

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

P value = 0.00509 (logrank test), Q value = 0.018

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	256	34	0.1 - 194.8 (21.4)
subtype1	101	13	0.1 - 129.9 (23.3)
subtype2	66	6	0.1 - 87.2 (25.1)
subtype3	41	3	2.2 - 194.8 (19.7)
subtype4	48	12	0.1 - 86.3 (13.9)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.345 (Kruskal-Wallis (anova)), Q value = 0.48

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

	nPatients	Mean (Std.Dev)
ALL	264	61.3 (12.2)
subtype1	101	62.9 (11.1)
subtype2	71	60.2 (11.9)
subtype3	44	61.8 (12.1)
subtype4	48	59.0 (14.6)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	167	21	49	14
subtype1	64	10	22	2
subtype2	53	6	6	1
subtype3	34	3	2	2
subtype4	16	2	19	9

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

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

nPatients	T1	T2	T3+T4
ALL	176	29	56
subtype1	68	8	21
subtype2	55	10	6
subtype3	35	5	4
subtype4	18	6	25

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0344 (Fisher's exact test), Q value = 0.09

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

nPatients	N0	N1	N2
ALL	45	22	4
subtype1	19	8	1
subtype2	10	1	0
subtype3	7	1	0
subtype4	9	12	3

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0091 (Fisher's exact test), Q value = 0.03

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

nPatients	0	1
ALL	90	9
subtype1	40	2
subtype2	22	1
subtype3	14	0
subtype4	14	6

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00029 (Fisher's exact test), Q value = 0.0016

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

nPatients	FEMALE	MALE
ALL	72	197
subtype1	33	71
subtype2	9	63
subtype3	8	36
subtype4	22	27

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.246 (Kruskal-Wallis (anova)), Q value = 0.38

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	60	92.0 (15.6)
subtype1	17	92.4 (14.4)
subtype2	17	94.1 (8.7)
subtype3	11	96.4 (6.7)
subtype4	15	86.0 (24.7)

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.735 (Kruskal-Wallis (anova)), Q value = 0.79

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	65	32.2 (28.5)
subtype1	20	40.3 (40.0)
subtype2	21	27.6 (15.8)
subtype3	10	24.9 (18.9)
subtype4	14	32.6 (29.2)

Figure S9. Get High-res Image Clustering Approach #1: '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 = 0.055 (Kruskal-Wallis (anova)), Q value = 0.12

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	51	1972.4 (15.9)
subtype1	19	1978.6 (16.6)
subtype2	15	1965.6 (16.0)
subtype3	7	1965.7 (11.9)
subtype4	10	1975.4 (12.8)

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.214 (Fisher's exact test), Q value = 0.35

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	5	60	187
subtype1	0	1	26	73
subtype2	1	1	17	49
subtype3	1	0	6	35
subtype4	0	3	11	30

Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.529 (Fisher's exact test), Q value = 0.65

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	220
subtype1	7	79
subtype2	2	64
subtype3	2	37
subtype4	1	40

Figure S12. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S14. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	72	67	116

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0328 (logrank test), Q value = 0.087

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	242	32	0.1 - 194.8 (20.2)
subtype1	68	8	0.1 - 100.3 (22.2)
subtype2	65	17	0.2 - 194.8 (20.0)
subtype3	109	7	0.1 - 129.9 (19.6)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.248 (Kruskal-Wallis (anova)), Q value = 0.38

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	250	61.6 (12.3)
subtype1	69	63.0 (13.0)
subtype2	66	61.9 (14.0)
subtype3	115	60.6 (10.7)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	164	18	48	13
subtype1	48	8	11	2
subtype2	21	4	29	9
subtype3	95	6	8	2

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3+T4
ALL	171	22	54
subtype1	50	5	14
subtype2	23	7	33
subtype3	98	10	7

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00026 (Fisher's exact test), Q value = 0.0015

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	43	21	4
subtype1	13	3	1
subtype2	15	18	3
subtype3	15	0	0

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.031 (Fisher's exact test), Q value = 0.085

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	81	8
subtype1	29	1
subtype2	23	6
subtype3	29	1

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00413 (Fisher's exact test), Q value = 0.015

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	69	186
subtype1	19	53
subtype2	28	39
subtype3	22	94

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0879 (Kruskal-Wallis (anova)), Q value = 0.19

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	60	93.7 (9.9)
subtype1	13	87.7 (15.9)
subtype2	11	95.5 (5.2)
subtype3	36	95.3 (7.4)

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.71 (Kruskal-Wallis (anova)), Q value = 0.78

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	63	32.7 (28.8)
subtype1	19	38.5 (39.6)
subtype2	14	31.7 (29.8)
subtype3	30	29.5 (19.1)

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.611 (Kruskal-Wallis (anova)), Q value = 0.7

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	51	1972.0 (16.0)
subtype1	14	1976.4 (18.9)
subtype2	13	1971.0 (12.5)
subtype3	24	1970.0 (16.0)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'RACE'

P value = 0.215 (Fisher's exact test), Q value = 0.35

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	5	53	180
subtype1	1	3	18	46
subtype2	0	1	17	46
subtype3	1	1	18	88

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.179 (Fisher's exact test), Q value = 0.31

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	206
subtype1	1	60
subtype2	5	47
subtype3	6	99

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S27. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	71	85	48

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.778 (logrank test), Q value = 0.83

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	194	25	0.1 - 194.8 (20.1)
subtype1	68	10	0.1 - 100.3 (18.8)
subtype2	81	10	0.1 - 194.8 (22.1)
subtype3	45	5	1.1 - 123.6 (20.0)

Figure S25. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000926 (Kruskal-Wallis (anova)), Q value = 0.0043

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	200	61.1 (12.0)
subtype1	70	59.5 (12.0)
subtype2	83	59.2 (12.1)
subtype3	47	66.8 (10.1)

Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.64 (Fisher's exact test), Q value = 0.72

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	126	15	43	13
subtype1	41	7	17	3
subtype2	57	6	14	7
subtype3	28	2	12	3

Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.164 (Fisher's exact test), Q value = 0.3

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3+T4
ALL	131	22	49
subtype1	45	7	19
subtype2	57	13	15
subtype3	29	2	15

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.309 (Fisher's exact test), Q value = 0.45

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	42	19	4
subtype1	17	6	2
subtype2	17	10	0
subtype3	8	3	2

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.365 (Fisher's exact test), Q value = 0.5

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	69	8
subtype1	24	1
subtype2	26	5
subtype3	19	2

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.902 (Fisher's exact test), Q value = 0.92

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	56	148
subtype1	18	53
subtype2	24	61
subtype3	14	34

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.152 (Kruskal-Wallis (anova)), Q value = 0.28

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	43	95.6 (5.5)
subtype1	14	93.6 (6.3)
subtype2	22	95.9 (5.0)
subtype3	7	98.6 (3.8)

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.548 (Kruskal-Wallis (anova)), Q value = 0.66

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	50	31.2 (28.3)
subtype1	17	26.1 (16.3)
subtype2	23	32.3 (20.1)
subtype3	10	37.2 (53.2)

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.803 (Kruskal-Wallis (anova)), Q value = 0.84

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	42	1970.7 (15.4)
subtype1	14	1973.7 (17.3)
subtype2	20	1968.3 (13.4)
subtype3	8	1971.4 (17.5)

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.582 (Fisher's exact test), Q value = 0.69

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	44	141
subtype1	0	3	15	45
subtype2	0	2	18	63
subtype3	1	0	11	33

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.44 (Fisher's exact test), Q value = 0.56

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	168
subtype1	1	56
subtype2	1	75
subtype3	2	37

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S40. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	16	22	41	34	26	40	25

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 2.31e-05 (logrank test), Q value = 0.00016

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	194	25	0.1 - 194.8 (20.1)
subtype1	15	0	0.1 - 87.2 (13.4)
subtype2	21	1	2.0 - 100.3 (21.7)
subtype3	39	4	0.1 - 129.9 (24.0)
subtype4	32	1	0.2 - 194.8 (21.1)
subtype5	26	11	0.2 - 86.3 (13.5)
subtype6	37	3	1.1 - 123.6 (22.3)
subtype7	24	5	0.1 - 92.6 (20.3)

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000101 (Kruskal-Wallis (anova)), Q value = 0.00067

Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	200	61.1 (12.0)
subtype1	15	67.3 (11.1)
subtype2	21	59.6 (11.1)
subtype3	40	59.5 (11.2)
subtype4	33	60.0 (10.3)
subtype5	26	54.2 (13.4)
subtype6	40	68.0 (9.8)
subtype7	25	58.6 (12.8)

Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	126	15	43	13
subtype1	14	2	0	0
subtype2	16	1	4	0
subtype3	35	1	2	2
subtype4	19	3	10	2
subtype5	9	1	8	8
subtype6	25	3	9	0
subtype7	8	4	10	1

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3+T4
ALL	131	22	49
subtype1	14	2	0
subtype2	17	1	4
subtype3	35	5	1
subtype4	20	4	10
subtype5	9	3	14
subtype6	26	3	9
subtype7	10	4	11

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0176 (Fisher's exact test), Q value = 0.053

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	42	19	4
subtype1	2	0	0
subtype2	5	1	0
subtype3	5	0	0
subtype4	11	4	0
subtype5	4	10	3
subtype6	7	1	1
subtype7	8	3	0

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0537 (Fisher's exact test), Q value = 0.12

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	69	8
subtype1	5	0
subtype2	5	0
subtype3	13	1
subtype4	12	1
subtype5	8	5
subtype6	14	0
subtype7	12	1

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0257 (Fisher's exact test), Q value = 0.074

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	56	148
subtype1	4	12
subtype2	4	18
subtype3	8	33
subtype4	13	21
subtype5	14	12
subtype6	9	31
subtype7	4	21

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.477 (Kruskal-Wallis (anova)), Q value = 0.6

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	43	95.6 (5.5)
subtype1	2	90.0 (0.0)
subtype2	6	93.3 (5.2)
subtype3	13	96.9 (4.8)
subtype4	7	94.3 (5.3)
subtype5	5	98.0 (4.5)
subtype6	5	98.0 (4.5)
subtype7	5	94.0 (8.9)

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.552 (Kruskal-Wallis (anova)), Q value = 0.66

Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	50	31.2 (28.3)
subtype1	3	21.3 (5.5)
subtype2	5	22.8 (10.4)
subtype3	13	36.4 (21.4)
subtype4	8	23.9 (20.1)
subtype5	5	36.6 (15.1)
subtype6	10	36.2 (53.7)
subtype7	6	28.7 (18.9)

Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0502 (Kruskal-Wallis (anova)), Q value = 0.12

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	42	1970.7 (15.4)
subtype1	2	1971.0 (15.6)
subtype2	4	1993.8 (7.5)
subtype3	12	1963.7 (14.7)
subtype4	7	1974.9 (13.5)
subtype5	5	1971.0 (16.3)
subtype6	9	1968.1 (14.7)
subtype7	3	1965.7 (4.0)

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.321 (Fisher's exact test), Q value = 0.46

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	44	141
subtype1	0	0	4	9
subtype2	0	0	5	16
subtype3	0	0	6	33
subtype4	0	1	8	24
subtype5	0	1	8	15
subtype6	1	0	10	28
subtype7	0	3	3	16

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.314 (Fisher's exact test), Q value = 0.45

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	168
subtype1	0	13
subtype2	0	19
subtype3	0	36
subtype4	2	28
subtype5	1	20
subtype6	0	33
subtype7	1	19

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	32	94	58	86

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0606 (logrank test), Q value = 0.13

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	257	34	0.1 - 194.8 (21.4)
subtype1	29	5	3.8 - 129.9 (30.5)
subtype2	89	7	0.1 - 87.2 (19.2)
subtype3	55	4	0.5 - 100.3 (25.6)
subtype4	84	18	0.1 - 194.8 (19.0)

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.115 (Kruskal-Wallis (anova)), Q value = 0.24

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

	nPatients	Mean (Std.Dev)
ALL	265	61.3 (12.2)
subtype1	31	57.9 (13.9)
subtype2	93	61.6 (10.8)
subtype3	57	64.5 (11.5)
subtype4	84	60.0 (13.2)

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	168	21	49	14
subtype1	26	2	0	2
subtype2	74	5	7	2
subtype3	37	8	9	0
subtype4	31	6	33	10

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

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

nPatients	T1	T2	T3+T4
ALL	177	29	56
subtype1	27	2	2
subtype2	76	11	6
subtype3	39	7	9
subtype4	35	9	39

Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00621 (Fisher's exact test), Q value = 0.021

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

nPatients	N0	N1	N2
ALL	45	22	4
subtype1	4	1	1
subtype2	12	1	0
subtype3	13	2	0
subtype4	16	18	3

Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.148 (Fisher's exact test), Q value = 0.28

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

nPatients	0	1
ALL	91	9
subtype1	13	1
subtype2	27	1
subtype3	17	0
subtype4	34	7

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

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

nPatients	FEMALE	MALE
ALL	72	198
subtype1	4	28
subtype2	18	76
subtype3	9	49
subtype4	41	45

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

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00628 (Kruskal-Wallis (anova)), Q value = 0.021

Table S61. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	61	92.1 (15.5)
subtype1	6	90.0 (0.0)
subtype2	27	97.0 (6.7)
subtype3	12	92.5 (8.7)
subtype4	16	84.4 (26.8)

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

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.425 (Kruskal-Wallis (anova)), Q value = 0.55

Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	66	31.9 (28.4)
subtype1	4	41.5 (29.8)
subtype2	27	26.4 (16.9)
subtype3	16	26.8 (18.4)
subtype4	19	41.9 (43.2)

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

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.132 (Kruskal-Wallis (anova)), Q value = 0.27

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	52	1972.2 (15.9)
subtype1	2	1957.0 (1.4)
subtype2	23	1968.7 (14.9)
subtype3	11	1972.5 (17.6)
subtype4	16	1978.8 (15.0)

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.182 (Fisher's exact test), Q value = 0.31

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	5	60	188
subtype1	0	1	8	20
subtype2	1	0	19	68
subtype3	1	0	10	45
subtype4	0	4	23	55

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

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

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	221
subtype1	1	24
subtype2	5	82
subtype3	2	49
subtype4	4	66

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S66. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	107	119	44

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00165 (logrank test), Q value = 0.0071

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	257	34	0.1 - 194.8 (21.4)
subtype1	103	14	0.1 - 194.8 (22.3)
subtype2	113	8	0.1 - 129.9 (22.1)
subtype3	41	12	0.2 - 92.6 (18.6)

Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.055 (Kruskal-Wallis (anova)), Q value = 0.12

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	265	61.3 (12.2)
subtype1	104	63.6 (12.0)
subtype2	118	59.7 (11.2)
subtype3	43	60.1 (14.7)

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	168	21	49	14
subtype1	53	10	32	4
subtype2	94	8	8	2
subtype3	21	3	9	8

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3+T4
ALL	177	29	56
subtype1	56	11	33
subtype2	98	14	7
subtype3	23	4	16

Figure S64. Get High-res Image Clustering Approach #6: '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 = 8e-05

Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	45	22	4
subtype1	23	12	1
subtype2	19	0	0
subtype3	3	10	3

Figure S65. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0431 (Fisher's exact test), Q value = 0.11

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	91	9
subtype1	38	3
subtype2	36	1
subtype3	17	5

Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 2e-05 (Fisher's exact test), Q value = 0.00015

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	72	198
subtype1	27	80
subtype2	21	98
subtype3	24	20

Figure S67. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.138 (Kruskal-Wallis (anova)), Q value = 0.27

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	61	92.1 (15.5)
subtype1	22	89.1 (21.1)
subtype2	35	95.4 (7.0)
subtype3	4	80.0 (27.1)

Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.313 (Kruskal-Wallis (anova)), Q value = 0.45

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	66	31.9 (28.4)
subtype1	32	33.8 (36.0)
subtype2	27	28.4 (19.9)
subtype3	7	36.9 (14.0)

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

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.346 (Kruskal-Wallis (anova)), Q value = 0.48

Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	52	1972.2 (15.9)
subtype1	25	1974.0 (16.0)
subtype2	22	1968.6 (15.6)
subtype3	5	1978.4 (16.3)

Figure S70. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.59 (Fisher's exact test), Q value = 0.69

Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	5	60	188
subtype1	1	2	23	78
subtype2	1	1	25	84
subtype3	0	2	12	26

Figure S71. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.194 (Fisher's exact test), Q value = 0.33

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	221
subtype1	4	88
subtype2	4	103
subtype3	4	30

Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S79. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	80	100	91

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.078 (logrank test), Q value = 0.17

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	258	34	0.1 - 194.8 (21.4)
subtype1	77	8	0.4 - 100.3 (21.6)
subtype2	93	8	0.1 - 123.6 (19.6)
subtype3	88	18	0.1 - 194.8 (22.1)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.409 (Kruskal-Wallis (anova)), Q value = 0.54

Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	266	61.4 (12.2)
subtype1	79	62.5 (11.8)
subtype2	97	62.1 (10.9)
subtype3	90	59.7 (13.8)

Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0296 (Fisher's exact test), Q value = 0.083

Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	169	21	49	14
subtype1	45	11	18	1
subtype2	69	5	14	4
subtype3	55	5	17	9

Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.135 (Fisher's exact test), Q value = 0.27

Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3+T4
ALL	178	29	56
subtype1	47	13	17
subtype2	73	10	16
subtype3	58	6	23

Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0376 (Fisher's exact test), Q value = 0.096

Table S84. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	45	22	4
subtype1	19	4	1
subtype2	13	4	0
subtype3	13	14	3

Figure S77. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.553 (Fisher's exact test), Q value = 0.66

Table S85. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	91	9
subtype1	28	1
subtype2	27	3
subtype3	36	5

Figure S78. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.174 (Fisher's exact test), Q value = 0.31

Table S86. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	73	198
subtype1	16	64
subtype2	27	73
subtype3	30	61

Figure S79. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.199 (Kruskal-Wallis (anova)), Q value = 0.33

Table S87. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	61	92.1 (15.5)
subtype1	14	92.1 (8.0)
subtype2	22	95.9 (7.3)
subtype3	25	88.8 (22.2)

Figure S80. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.828 (Kruskal-Wallis (anova)), Q value = 0.86

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	67	31.5 (28.3)
subtype1	25	30.9 (25.3)
subtype2	20	36.0 (38.7)
subtype3	22	28.0 (20.1)

Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.689 (Kruskal-Wallis (anova)), Q value = 0.77

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	53	1972.0 (15.7)
subtype1	17	1969.4 (15.0)
subtype2	19	1975.3 (16.7)
subtype3	17	1971.0 (15.6)

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.341 (Fisher's exact test), Q value = 0.48

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	5	60	189
subtype1	0	1	23	53
subtype2	1	2	15	75
subtype3	1	2	22	61

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.493 (Fisher's exact test), Q value = 0.62

Table S91. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	222
subtype1	3	68
subtype2	3	83
subtype3	6	71

Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S92. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	50	61	77	83

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.179 (logrank test), Q value = 0.31

Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	258	34	0.1 - 194.8 (21.4)
subtype1	49	9	0.4 - 100.3 (26.4)
subtype2	55	3	0.5 - 123.6 (19.3)
subtype3	74	7	0.1 - 99.8 (18.6)
subtype4	80	15	0.1 - 194.8 (22.6)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.228 (Kruskal-Wallis (anova)), Q value = 0.36

Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	266	61.4 (12.2)
subtype1	49	63.6 (12.1)
subtype2	60	62.4 (12.4)
subtype3	76	62.1 (10.6)
subtype4	81	58.7 (13.3)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00043 (Fisher's exact test), Q value = 0.0021

Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	169	21	49	14
subtype1	22	10	14	2
subtype2	42	2	7	1
subtype3	58	5	9	2
subtype4	47	4	19	9

Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00127 (Fisher's exact test), Q value = 0.0056

Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3+T4
ALL	178	29	56
subtype1	24	8	15
subtype2	44	6	8
subtype3	61	9	7
subtype4	49	6	26

Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.0147 (Fisher's exact test), Q value = 0.045

Table S97. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	45	22	4
subtype1	12	6	1
subtype2	9	0	0
subtype3	12	2	0
subtype4	12	14	3

Figure S89. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.65 (Fisher's exact test), Q value = 0.73

Table S98. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	91	9
subtype1	21	2
subtype2	16	1
subtype3	23	1
subtype4	31	5

Figure S90. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00415 (Fisher's exact test), Q value = 0.015

Table S99. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	73	198
subtype1	7	43
subtype2	15	46
subtype3	17	60
subtype4	34	49

Figure S91. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.778 (Kruskal-Wallis (anova)), Q value = 0.83

Table S100. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	61	92.1 (15.5)
subtype1	7	81.4 (36.7)
subtype2	17	94.7 (6.2)
subtype3	19	94.7 (8.4)
subtype4	18	91.1 (14.1)

Figure S92. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.788 (Kruskal-Wallis (anova)), Q value = 0.83

Table S101. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	67	31.5 (28.3)
subtype1	16	36.1 (28.7)
subtype2	17	27.5 (17.3)
subtype3	14	27.3 (18.5)
subtype4	20	34.1 (40.0)

Figure S93. Get High-res Image Clustering Approach #8: '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 = 0.69

Table S102. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	53	1972.0 (15.7)
subtype1	10	1974.0 (12.8)
subtype2	13	1967.1 (14.1)
subtype3	14	1971.0 (15.5)
subtype4	16	1975.7 (18.8)

Figure S94. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0402 (Fisher's exact test), Q value = 0.1

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	5	60	189
subtype1	0	1	9	39
subtype2	1	0	21	35
subtype3	1	1	10	59
subtype4	0	3	20	56

Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.401 (Fisher's exact test), Q value = 0.54

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	222
subtype1	1	44
subtype2	3	49
subtype3	2	66
subtype4	6	63

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S105. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	63	79	66

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

P value = 0.000222 (logrank test), Q value = 0.0013

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	199	30	0.1 - 194.8 (21.9)
subtype1	61	6	0.4 - 100.3 (20.0)
subtype2	74	3	0.1 - 123.6 (24.9)
subtype3	64	21	0.1 - 194.8 (20.8)

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

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.133 (Kruskal-Wallis (anova)), Q value = 0.27

Table S107. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	204	60.9 (12.4)
subtype1	60	62.8 (10.9)
subtype2	79	59.0 (11.3)
subtype3	65	61.3 (14.6)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00014 (Fisher's exact test), Q value = 0.00088

Table S108. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	128	15	41	13
subtype1	37	5	13	1
subtype2	62	4	10	1
subtype3	29	6	18	11

Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00057 (Fisher's exact test), Q value = 0.0027

Table S109. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3+T4
ALL	134	18	49
subtype1	39	7	14
subtype2	64	6	9
subtype3	31	5	26

Figure S100. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00038 (Fisher's exact test), Q value = 0.002

Table S110. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	37	21	4
subtype1	17	2	1
subtype2	12	3	0
subtype3	8	16	3

Figure S101. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.021 (Fisher's exact test), Q value = 0.061

Table S111. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	73	8
subtype1	23	1
subtype2	24	0
subtype3	26	7

Figure S102. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.00244 (Fisher's exact test), Q value = 0.0098

Table S112. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	58	150
subtype1	17	46
subtype2	13	66
subtype3	28	38

Figure S103. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.252 (Kruskal-Wallis (anova)), Q value = 0.38

Table S113. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	52	91.7 (16.3)
subtype1	12	92.5 (7.5)
subtype2	24	95.8 (5.8)
subtype3	16	85.0 (27.1)

Figure S104. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.422 (Kruskal-Wallis (anova)), Q value = 0.55

Table S114. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	53	33.5 (30.3)
subtype1	15	41.9 (48.0)
subtype2	23	26.9 (17.4)
subtype3	15	35.3 (21.7)

Figure S105. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.603 (Kruskal-Wallis (anova)), Q value = 0.7

Table S115. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	43	1971.0 (16.8)
subtype1	15	1971.6 (17.7)
subtype2	20	1968.8 (17.0)
subtype3	8	1975.4 (15.5)

Figure S106. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.896 (Fisher's exact test), Q value = 0.92

Table S116. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	5	39	153
subtype1	1	1	10	49
subtype2	1	2	14	58
subtype3	0	2	15	46

Figure S107. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

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

Table S117. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	168
subtype1	3	51
subtype2	4	67
subtype3	3	50

Figure S108. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S118. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	92	71	15	30

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

P value = 9.65e-08 (logrank test), Q value = 1.2e-05

Table S119. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	199	30	0.1 - 194.8 (21.9)
subtype1	87	4	0.5 - 123.6 (25.7)
subtype2	70	20	0.1 - 194.8 (21.5)
subtype3	13	6	0.2 - 75.4 (10.7)
subtype4	29	0	0.1 - 87.2 (17.8)

Figure S109. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.135 (Kruskal-Wallis (anova)), Q value = 0.27

Table S120. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	204	60.9 (12.4)
subtype1	90	60.7 (11.2)
subtype2	70	63.4 (12.2)
subtype3	14	53.7 (18.3)
subtype4	30	58.6 (11.9)

Figure S110. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S121. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	128	15	41	13
subtype1	62	9	14	1
subtype2	36	3	20	9
subtype3	3	3	6	3
subtype4	27	0	1	0

Figure S111. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 8e-05

Table S122. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3+T4
ALL	134	18	49
subtype1	65	11	14
subtype2	37	3	26
subtype3	4	3	8
subtype4	28	1	1

Figure S112. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0037 (Fisher's exact test), Q value = 0.014

Table S123. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	37	21	4
subtype1	15	3	0
subtype2	17	11	4
subtype3	1	7	0
subtype4	4	0	0

Figure S113. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0128 (Fisher's exact test), Q value = 0.04

Table S124. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	73	8
subtype1	28	0
subtype2	29	5
subtype3	6	3
subtype4	10	0

Figure S114. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.00175 (Fisher's exact test), Q value = 0.0072

Table S125. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	58	150
subtype1	17	75
subtype2	23	48
subtype3	10	5
subtype4	8	22

Figure S115. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.182 (Kruskal-Wallis (anova)), Q value = 0.31

Table S126. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	52	91.7 (16.3)
subtype1	23	94.8 (6.7)
subtype2	20	88.5 (21.8)
subtype3	1	40.0 (NA)
subtype4	8	97.5 (4.6)

Figure S116. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.312 (Kruskal-Wallis (anova)), Q value = 0.45

Table S127. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	53	33.5 (30.3)
subtype1	19	31.9 (26.2)
subtype2	18	42.4 (41.2)
subtype4	16	25.4 (16.0)

Figure S117. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.729 (Kruskal-Wallis (anova)), Q value = 0.79

Table S128. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	43	1971.0 (16.8)
subtype1	16	1969.9 (16.0)
subtype2	14	1974.0 (18.0)
subtype4	13	1969.2 (17.3)

Figure S118. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.384 (Fisher's exact test), Q value = 0.52

Table S129. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	5	39	153
subtype1	2	1	14	72
subtype2	0	3	16	50
subtype3	0	1	4	8
subtype4	0	0	5	23

Figure S119. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

Table S130. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	168
subtype1	2	77
subtype2	4	54
subtype3	1	11
subtype4	3	26

Figure S120. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KIRP-TP/15098835/KIRP-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIRP-TP/15081779/KIRP-TP.merged_data.txt
Number of patients = 271
Number of clustering approaches = 10
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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