Correlation between aggregated molecular cancer subtypes and selected clinical features

Bladder Urothelial Carcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12R3PZ7

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 10 clinical features across 158 patients, 3 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.N.STAGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 5 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NEOPLASM.DISEASESTAGE' and 'PATHOLOGY.N.STAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 3 significant findings detected.


Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE	NUMBERPACKYEARSSMOKED	NUMBER OF LYMPH NODES
Statistical Tests	logrank test	ANOVA	Chi-square test	Chi-square test	Chi-square test	Chi-square test	Fisher's exact test	ANOVA	ANOVA	ANOVA
Copy Number Ratio CNMF subtypes	0.875 (1.00)	0.135 (1.00)	0.0185 (1.00)	0.0873 (1.00)	0.00112 (0.11)	0.701 (1.00)	0.94 (1.00)	0.355 (1.00)	0.0384 (1.00)	0.189 (1.00)
METHLYATION CNMF	0.375 (1.00)	0.0829 (1.00)	0.223 (1.00)	0.362 (1.00)	0.432 (1.00)	0.19 (1.00)	0.203 (1.00)	0.0253 (1.00)	0.43 (1.00)	0.262 (1.00)
RPPA CNMF subtypes	0.344 (1.00)	0.289 (1.00)	0.331 (1.00)	0.635 (1.00)	0.486 (1.00)	0.134 (1.00)	0.64 (1.00)	0.336 (1.00)	0.111 (1.00)	0.223 (1.00)
RPPA cHierClus subtypes	0.14 (1.00)	0.294 (1.00)	0.159 (1.00)	0.156 (1.00)	0.779 (1.00)	0.189 (1.00)	0.175 (1.00)	0.441 (1.00)	0.0975 (1.00)	0.447 (1.00)
RNAseq CNMF subtypes	0.692 (1.00)	0.205 (1.00)	0.28 (1.00)	0.281 (1.00)	0.412 (1.00)	0.205 (1.00)	0.972 (1.00)	0.463 (1.00)	0.93 (1.00)	0.268 (1.00)
RNAseq cHierClus subtypes	0.68 (1.00)	0.192 (1.00)	0.000308 (0.0305)	0.00538 (0.517)	7.33e-06 (0.000733)	0.401 (1.00)	0.257 (1.00)	0.53 (1.00)	0.952 (1.00)	0.00858 (0.816)
MIRSEQ CNMF	0.514 (1.00)	0.0601 (1.00)	0.0313 (1.00)	0.104 (1.00)	0.00493 (0.478)	0.91 (1.00)	0.00992 (0.922)	0.404 (1.00)	0.651 (1.00)	0.315 (1.00)
MIRSEQ CHIERARCHICAL	0.37 (1.00)	0.17 (1.00)	0.299 (1.00)	0.353 (1.00)	0.287 (1.00)	0.933 (1.00)	0.00891 (0.837)	0.0954 (1.00)	0.66 (1.00)	0.235 (1.00)
MIRseq Mature CNMF subtypes	0.593 (1.00)	0.0551 (1.00)	0.0471 (1.00)	0.0693 (1.00)	0.131 (1.00)	0.734 (1.00)	0.0588 (1.00)	0.506 (1.00)	0.29 (1.00)	0.146 (1.00)
MIRseq Mature cHierClus subtypes	0.731 (1.00)	0.0156 (1.00)	0.0657 (1.00)	0.0888 (1.00)	0.331 (1.00)	0.918 (1.00)	0.171 (1.00)	0.276 (1.00)	0.907 (1.00)	0.418 (1.00)

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
Number of samples	48	66	38

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

P value = 0.875 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	145	42	0.1 - 131.2 (6.8)
subtype1	45	14	0.1 - 112.4 (6.7)
subtype2	63	18	0.1 - 131.2 (7.1)
subtype3	37	10	0.4 - 130.8 (6.5)

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 'AGE'

P value = 0.135 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	151	67.2 (10.9)
subtype1	47	69.2 (10.4)
subtype2	66	65.2 (11.9)
subtype3	38	68.1 (9.1)

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

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

P value = 0.0185 (Chi-square test), Q value = 1

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	2	49	49	48
subtype1	1	16	8	22
subtype2	1	21	30	12
subtype3	0	12	11	14

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 = 0.0873 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	44	69	22
subtype1	20	17	5
subtype2	16	31	13
subtype3	8	21	4

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 = 0.00112 (Chi-square test), Q value = 0.11

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

nPatients	N0	N1	N2	N3
ALL	91	14	27	5
subtype1	17	8	12	1
subtype2	53	1	9	1
subtype3	21	5	6	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 = 0.701 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	90	5	56
subtype1	26	1	20
subtype2	43	2	21
subtype3	21	2	15

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 = 0.94 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	36	116
subtype1	12	36
subtype2	16	50
subtype3	8	30

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 = 0.355 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	45	76.2 (16.0)
subtype1	15	80.7 (13.3)
subtype2	19	72.6 (18.2)
subtype3	11	76.4 (15.0)

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 'NUMBERPACKYEARSSMOKED'

P value = 0.0384 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	90	35.6 (22.4)
subtype1	27	31.2 (23.4)
subtype2	36	32.0 (17.3)
subtype3	27	44.7 (25.3)

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.189 (ANOVA), Q value = 1

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	108	1.7 (3.6)
subtype1	35	1.7 (2.7)
subtype2	43	1.1 (2.1)
subtype3	30	2.7 (5.6)

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S12. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	61	63	34

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.375 (logrank test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	151	44	0.1 - 131.2 (6.7)
subtype1	58	13	0.1 - 130.8 (6.3)
subtype2	61	20	0.4 - 131.2 (7.8)
subtype3	32	11	0.1 - 46.8 (5.7)

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0829 (ANOVA), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	157	67.4 (10.8)
subtype1	60	66.6 (10.7)
subtype2	63	69.6 (9.6)
subtype3	34	64.7 (12.7)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.223 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	50	51	51
subtype1	2	20	16	21
subtype2	0	18	27	17
subtype3	0	12	8	13

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.362 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	44	71	25
subtype1	22	25	8
subtype2	14	33	10
subtype3	8	13	7

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.432 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	94	14	30	5
subtype1	34	7	11	2
subtype2	43	6	9	2
subtype3	17	1	10	1

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.19 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	93	5	59
subtype1	33	2	25
subtype2	35	1	27
subtype3	25	2	7

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.203 (Fisher's exact test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	38	120
subtype1	12	49
subtype2	20	43
subtype3	6	28

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0253 (ANOVA), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	47	77.0 (16.1)
subtype1	22	80.5 (13.3)
subtype2	12	81.7 (15.3)
subtype3	13	66.9 (18.0)

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.43 (ANOVA), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	93	35.6 (22.6)
subtype1	39	36.5 (25.8)
subtype2	41	37.2 (19.9)
subtype3	13	28.0 (21.0)

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

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

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	110	1.7 (3.6)
subtype1	43	1.8 (4.1)
subtype2	48	1.2 (2.7)
subtype3	19	2.8 (4.3)

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S23. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	36	28	35	4	20

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.344 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	116	41	0.1 - 131.2 (7.8)
subtype1	31	6	0.1 - 130.8 (6.6)
subtype2	27	11	1.8 - 131.2 (6.9)
subtype3	34	15	0.4 - 123.8 (8.2)
subtype4	4	2	3.1 - 11.9 (8.1)
subtype5	20	7	2.1 - 61.9 (9.3)

Figure S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.289 (ANOVA), Q value = 1

Table S25. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	122	67.1 (10.6)
subtype1	35	65.3 (10.8)
subtype2	28	69.8 (9.4)
subtype3	35	68.5 (10.7)
subtype4	4	62.2 (12.0)
subtype5	20	65.0 (11.0)

Figure S22. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.331 (Chi-square test), Q value = 1

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	35	40	43
subtype1	1	11	9	12
subtype2	0	5	8	15
subtype3	0	8	15	11
subtype4	0	1	2	1
subtype5	0	10	6	4

Figure S23. 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 = 0.635 (Chi-square test), Q value = 1

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	27	60	19
subtype1	10	15	4
subtype2	6	14	8
subtype3	8	20	5
subtype4	0	2	1
subtype5	3	9	1

Figure S24. 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 = 0.486 (Chi-square test), Q value = 1

Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	69	12	24	5
subtype1	19	1	9	2
subtype2	13	3	9	2
subtype3	23	5	4	1
subtype4	3	1	0	0
subtype5	11	2	2	0

Figure S25. 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 = 0.134 (Chi-square test), Q value = 1

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	70	5	47
subtype1	24	2	9
subtype2	10	2	16
subtype3	24	0	11
subtype4	3	0	1
subtype5	9	1	10

Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.64 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	32	91
subtype1	7	29
subtype2	10	18
subtype3	8	27
subtype4	1	3
subtype5	6	14

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

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

P value = 0.336 (ANOVA), Q value = 1

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	35	79.1 (15.8)
subtype1	12	75.0 (17.8)
subtype2	2	80.0 (14.1)
subtype3	10	77.0 (20.0)
subtype4	1	90.0 (NA)
subtype5	10	85.0 (7.1)

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.111 (ANOVA), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	77	35.7 (21.4)
subtype1	23	30.5 (20.7)
subtype2	17	45.8 (22.4)
subtype3	22	36.8 (20.3)
subtype4	2	35.5 (6.4)
subtype5	13	30.1 (22.2)

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.223 (ANOVA), Q value = 1

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	86	1.9 (3.9)
subtype1	21	1.9 (3.1)
subtype2	25	3.3 (5.5)
subtype3	26	0.9 (2.4)
subtype4	3	0.3 (0.6)
subtype5	11	1.4 (3.6)

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S34. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	41	18	22	42

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.14 (logrank test), Q value = 1

Table S35. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	116	41	0.1 - 131.2 (7.8)
subtype1	35	8	0.1 - 112.4 (6.6)
subtype2	18	4	2.1 - 61.9 (8.1)
subtype3	22	12	0.4 - 130.8 (13.5)
subtype4	41	17	1.8 - 131.2 (7.7)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.294 (ANOVA), Q value = 1

Table S36. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	122	67.1 (10.6)
subtype1	40	65.1 (10.9)
subtype2	18	65.4 (9.3)
subtype3	22	68.7 (11.9)
subtype4	42	69.0 (10.0)

Figure S32. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.159 (Chi-square test), Q value = 1

Table S37. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	35	40	43
subtype1	1	14	12	11
subtype2	0	9	5	4
subtype3	0	4	10	7
subtype4	0	8	13	21

Figure S33. 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 = 0.156 (Chi-square test), Q value = 1

Table S38. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	27	60	19
subtype1	14	15	6
subtype2	2	6	1
subtype3	3	16	2
subtype4	8	23	10

Figure S34. 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 = 0.779 (Chi-square test), Q value = 1

Table S39. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	69	12	24	5
subtype1	23	4	7	1
subtype2	9	0	2	1
subtype3	15	3	4	0
subtype4	22	5	11	3

Figure S35. 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 = 0.189 (Chi-square test), Q value = 1

Table S40. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	70	5	47
subtype1	29	1	10
subtype2	8	2	8
subtype3	12	0	10
subtype4	21	2	19

Figure S36. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.175 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	32	91
subtype1	6	35
subtype2	5	13
subtype3	6	16
subtype4	15	27

Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.441 (ANOVA), Q value = 1

Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	35	79.1 (15.8)
subtype1	13	76.9 (17.5)
subtype2	10	86.0 (7.0)
subtype3	7	77.1 (18.0)
subtype4	5	74.0 (20.7)

Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0975 (ANOVA), Q value = 1

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	77	35.7 (21.4)
subtype1	26	31.0 (19.0)
subtype2	12	29.5 (21.8)
subtype3	14	34.7 (19.0)
subtype4	25	44.2 (23.3)

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.447 (ANOVA), Q value = 1

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	86	1.9 (3.9)
subtype1	24	1.5 (2.7)
subtype2	8	1.8 (4.2)
subtype3	18	1.0 (2.0)
subtype4	36	2.7 (5.0)

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S45. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	25	45	26	58

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.692 (logrank test), Q value = 1

Table S46. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	147	43	0.1 - 131.2 (6.8)
subtype1	25	10	0.5 - 130.8 (6.7)
subtype2	44	14	0.4 - 87.3 (7.7)
subtype3	25	7	1.3 - 131.2 (10.6)
subtype4	53	12	0.1 - 112.4 (4.9)

Figure S41. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.205 (ANOVA), Q value = 1

Table S47. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	153	67.2 (10.9)
subtype1	25	65.4 (9.8)
subtype2	45	68.2 (10.7)
subtype3	26	70.4 (8.6)
subtype4	57	65.6 (12.1)

Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.28 (Chi-square test), Q value = 1

Table S48. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	50	49	49
subtype1	1	7	9	8
subtype2	0	13	18	13
subtype3	0	5	9	12
subtype4	1	25	13	16

Figure S43. 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 = 0.281 (Chi-square test), Q value = 1

Table S49. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	44	69	23
subtype1	5	10	4
subtype2	11	22	8
subtype3	5	16	5
subtype4	23	21	6

Figure S44. 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 = 0.412 (Chi-square test), Q value = 1

Table S50. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	92	14	28	5
subtype1	12	2	4	1
subtype2	30	6	3	2
subtype3	14	2	9	1
subtype4	36	4	12	1

Figure S45. 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 = 0.205 (Chi-square test), Q value = 1

Table S51. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	92	5	56
subtype1	12	2	10
subtype2	25	0	20
subtype3	15	2	9
subtype4	40	1	17

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.972 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	117
subtype1	6	19
subtype2	12	33
subtype3	6	20
subtype4	13	45

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.463 (ANOVA), Q value = 1

Table S53. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	46	76.5 (15.9)
subtype1	8	83.8 (9.2)
subtype2	11	78.2 (19.4)
subtype3	4	75.0 (12.9)
subtype4	23	73.5 (16.4)

Figure S48. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.93 (ANOVA), Q value = 1

Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	90	36.0 (22.8)
subtype1	20	34.1 (20.8)
subtype2	29	34.8 (19.5)
subtype3	13	37.5 (20.0)
subtype4	28	37.8 (28.7)

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.268 (ANOVA), Q value = 1

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	107	1.7 (3.6)
subtype1	17	0.7 (1.0)
subtype2	34	1.4 (4.2)
subtype3	23	2.9 (4.5)
subtype4	33	1.8 (3.1)

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S56. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	30	59	65

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.68 (logrank test), Q value = 1

Table S57. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	147	43	0.1 - 131.2 (6.8)
subtype1	30	10	0.5 - 130.8 (7.3)
subtype2	54	11	0.1 - 50.3 (5.3)
subtype3	63	22	0.4 - 131.2 (7.8)

Figure S51. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.192 (ANOVA), Q value = 1

Table S58. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	153	67.2 (10.9)
subtype1	30	69.8 (9.7)
subtype2	58	65.4 (11.4)
subtype3	65	67.5 (10.8)

Figure S52. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000308 (Chi-square test), Q value = 0.03

Table S59. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	50	49	49
subtype1	0	5	5	19
subtype2	2	26	16	13
subtype3	0	19	28	17

Figure S53. 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 = 0.00538 (Chi-square test), Q value = 0.52

Table S60. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	44	69	23
subtype1	4	16	8
subtype2	25	19	5
subtype3	15	34	10

Figure S54. 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 = 7.33e-06 (Chi-square test), Q value = 0.00073

Table S61. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	92	14	28	5
subtype1	11	1	17	1
subtype2	36	5	7	1
subtype3	45	8	4	3

Figure S55. 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 = 0.401 (Chi-square test), Q value = 1

Table S62. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	92	5	56
subtype1	17	2	11
subtype2	39	2	17
subtype3	36	1	28

Figure S56. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.257 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	117
subtype1	8	22
subtype2	10	49
subtype3	19	46

Figure S57. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.53 (ANOVA), Q value = 1

Table S64. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	46	76.5 (15.9)
subtype1	6	81.7 (13.3)
subtype2	24	74.2 (16.4)
subtype3	16	78.1 (16.4)

Figure S58. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.952 (ANOVA), Q value = 1

Table S65. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	90	36.0 (22.8)
subtype1	14	37.6 (27.2)
subtype2	33	35.3 (26.0)
subtype3	43	35.9 (18.8)

Figure S59. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00858 (ANOVA), Q value = 0.82

Table S66. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	107	1.7 (3.6)
subtype1	24	3.7 (4.2)
subtype2	32	1.4 (2.9)
subtype3	51	1.0 (3.5)

Figure S60. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S67. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	38	47	70

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.514 (logrank test), Q value = 1

Table S68. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	44	0.1 - 131.2 (6.8)
subtype1	34	8	0.1 - 130.8 (4.2)
subtype2	47	19	0.4 - 131.2 (8.2)
subtype3	67	17	0.1 - 61.9 (7.0)

Figure S61. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0601 (ANOVA), Q value = 1

Table S69. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	154	67.2 (10.8)
subtype1	37	69.5 (11.6)
subtype2	47	68.7 (9.9)
subtype3	70	65.0 (10.7)

Figure S62. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0313 (Chi-square test), Q value = 1

Table S70. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	50	50	49
subtype1	1	8	13	15
subtype2	0	10	15	20
subtype3	1	32	22	14

Figure S63. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.104 (Chi-square test), Q value = 1

Table S71. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	44	70	23
subtype1	12	16	7
subtype2	8	26	10
subtype3	24	28	6

Figure S64. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.00493 (Chi-square test), Q value = 0.48

Table S72. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	93	14	28	5
subtype1	21	6	6	2
subtype2	26	2	17	0
subtype3	46	6	5	3

Figure S65. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.91 (Chi-square test), Q value = 1

Table S73. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	93	5	56
subtype1	21	1	15
subtype2	27	2	18
subtype3	45	2	23

Figure S66. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00992 (Fisher's exact test), Q value = 0.92

Table S74. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	118
subtype1	4	34
subtype2	18	29
subtype3	15	55

Figure S67. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.404 (ANOVA), Q value = 1

Table S75. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	46	76.5 (15.9)
subtype1	8	82.5 (8.9)
subtype2	7	71.4 (17.7)
subtype3	31	76.1 (16.9)

Figure S68. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.651 (ANOVA), Q value = 1

Table S76. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	91	36.0 (22.6)
subtype1	22	36.9 (27.9)
subtype2	24	39.1 (24.7)
subtype3	45	33.9 (18.6)

Figure S69. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.315 (ANOVA), Q value = 1

Table S77. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	108	1.7 (3.6)
subtype1	29	2.0 (4.7)
subtype2	38	2.2 (3.7)
subtype3	41	1.0 (2.5)

Figure S70. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S78. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	35	113	7

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.37 (logrank test), Q value = 1

Table S79. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	44	0.1 - 131.2 (6.8)
subtype1	31	8	0.1 - 130.8 (4.1)
subtype2	110	34	0.1 - 131.2 (7.8)
subtype3	7	2	1.4 - 10.0 (5.5)

Figure S71. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.17 (ANOVA), Q value = 1

Table S80. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	154	67.2 (10.8)
subtype1	34	69.7 (11.9)
subtype2	113	66.2 (10.6)
subtype3	7	71.0 (7.0)

Figure S72. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.299 (Chi-square test), Q value = 1

Table S81. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	50	50	49
subtype1	1	7	11	15
subtype2	1	42	35	32
subtype3	0	1	4	2

Figure S73. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.353 (Chi-square test), Q value = 1

Table S82. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	44	70	23
subtype1	11	14	7
subtype2	32	50	16
subtype3	1	6	0

Figure S74. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.287 (Chi-square test), Q value = 1

Table S83. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	93	14	28	5
subtype1	17	6	7	2
subtype2	70	7	21	3
subtype3	6	1	0	0

Figure S75. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.933 (Chi-square test), Q value = 1

Table S84. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	93	5	56
subtype1	19	1	14
subtype2	70	4	39
subtype3	4	0	3

Figure S76. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00891 (Fisher's exact test), Q value = 0.84

Table S85. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	118
subtype1	3	32
subtype2	34	79
subtype3	0	7

Figure S77. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0954 (ANOVA), Q value = 1

Table S86. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	46	76.5 (15.9)
subtype1	8	82.5 (8.9)
subtype2	37	75.1 (17.1)
subtype3	1	80.0 (NA)

Figure S78. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.66 (ANOVA), Q value = 1

Table S87. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	91	36.0 (22.6)
subtype1	20	39.0 (28.2)
subtype2	65	35.7 (21.0)
subtype3	6	29.7 (22.1)

Figure S79. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.235 (ANOVA), Q value = 1

Table S88. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	108	1.7 (3.6)
subtype1	26	2.6 (5.0)
subtype2	75	1.6 (3.2)
subtype3	7	0.1 (0.4)

Figure S80. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S89. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	31	64	60

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

P value = 0.593 (logrank test), Q value = 1

Table S90. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	44	0.1 - 131.2 (6.8)
subtype1	27	8	0.1 - 130.8 (4.4)
subtype2	64	22	0.4 - 131.2 (8.0)
subtype3	57	14	0.1 - 50.3 (6.4)

Figure S81. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0551 (ANOVA), Q value = 1

Table S91. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	154	67.2 (10.8)
subtype1	30	69.5 (12.3)
subtype2	64	68.6 (10.2)
subtype3	60	64.6 (10.4)

Figure S82. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0471 (Chi-square test), Q value = 1

Table S92. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	50	50	49
subtype1	1	6	10	13
subtype2	0	16	22	24
subtype3	1	28	18	12

Figure S83. 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 = 0.0693 (Chi-square test), Q value = 1

Table S93. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	44	70	23
subtype1	9	13	7
subtype2	13	37	10
subtype3	22	20	6

Figure S84. 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 = 0.131 (Chi-square test), Q value = 1

Table S94. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	93	14	28	5
subtype1	15	5	6	2
subtype2	40	4	17	1
subtype3	38	5	5	2

Figure S85. 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 = 0.734 (Chi-square test), Q value = 1

Table S95. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	93	5	56
subtype1	18	1	11
subtype2	35	2	27
subtype3	40	2	18

Figure S86. 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 = 0.0588 (Fisher's exact test), Q value = 1

Table S96. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	118
subtype1	3	28
subtype2	20	44
subtype3	14	46

Figure S87. 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 = 0.506 (ANOVA), Q value = 1

Table S97. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	46	76.5 (15.9)
subtype1	7	82.9 (9.5)
subtype2	12	74.2 (18.8)
subtype3	27	75.9 (16.0)

Figure S88. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.29 (ANOVA), Q value = 1

Table S98. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	91	36.0 (22.6)
subtype1	17	38.6 (29.6)
subtype2	36	39.4 (22.9)
subtype3	38	31.6 (18.4)

Figure S89. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.146 (ANOVA), Q value = 1

Table S99. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	108	1.7 (3.6)
subtype1	23	2.5 (5.2)
subtype2	53	2.0 (3.6)
subtype3	32	0.7 (1.7)

Figure S90. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S100. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	49	81	25

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

P value = 0.731 (logrank test), Q value = 1

Table S101. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	148	44	0.1 - 131.2 (6.8)
subtype1	45	11	0.1 - 50.3 (6.2)
subtype2	81	28	0.4 - 131.2 (7.8)
subtype3	22	5	0.1 - 130.8 (4.8)

Figure S91. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0156 (ANOVA), Q value = 1

Table S102. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	154	67.2 (10.8)
subtype1	49	63.6 (11.1)
subtype2	81	68.7 (10.1)
subtype3	24	69.5 (11.2)

Figure S92. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0657 (Chi-square test), Q value = 1

Table S103. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	50	50	49
subtype1	1	23	14	10
subtype2	0	22	29	28
subtype3	1	5	7	11

Figure S93. 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 = 0.0888 (Chi-square test), Q value = 1

Table S104. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	44	70	23
subtype1	19	18	4
subtype2	17	43	14
subtype3	8	9	5

Figure S94. 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 = 0.331 (Chi-square test), Q value = 1

Table S105. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	93	14	28	5
subtype1	31	4	4	2
subtype2	51	6	18	2
subtype3	11	4	6	1

Figure S95. 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 = 0.918 (Chi-square test), Q value = 1

Table S106. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	93	5	56
subtype1	31	2	16
subtype2	47	2	32
subtype3	15	1	8

Figure S96. 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 = 0.171 (Fisher's exact test), Q value = 1

Table S107. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	118
subtype1	10	39
subtype2	24	57
subtype3	3	22

Figure S97. 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 = 0.276 (ANOVA), Q value = 1

Table S108. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	46	76.5 (15.9)
subtype1	23	73.5 (16.4)
subtype2	17	77.6 (16.8)
subtype3	6	85.0 (8.4)

Figure S98. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.907 (ANOVA), Q value = 1

Table S109. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	91	36.0 (22.6)
subtype1	30	36.2 (23.4)
subtype2	47	36.6 (22.5)
subtype3	14	33.6 (22.8)

Figure S99. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.418 (ANOVA), Q value = 1

Table S110. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	108	1.7 (3.6)
subtype1	25	0.9 (1.9)
subtype2	65	2.0 (4.2)
subtype3	18	1.9 (3.2)

Figure S100. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Cluster data file = BLCA-TP.mergedcluster.txt
Clinical data file = BLCA-TP.clin.merged.picked.txt
Number of patients = 158
Number of clustering approaches = 10
Number of selected clinical features = 10
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

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

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

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

[6] 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)

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

Made with Nozzle