Correlate_Clinical_vs_Molecular_Signatures

Bladder Urothelial Carcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

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): Correlate_Clinical_vs_Molecular_Signatures. Broad Institute of MIT and Harvard. doi:10.7908/C1NV9GD5

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 8 different clustering approaches and 12 clinical features across 128 patients, 3 significant findings detected with P value < 0.05 and Q value < 0.25.

6 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'LYMPH.NODE.METASTASIS' and 'NUMBER.OF.LYMPH.NODES'.
4 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 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 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 'LYMPH.NODE.METASTASIS'.
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.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 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, 3 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
Time to Death	logrank test	0.0233 (1.00)	0.56 (1.00)	0.245 (1.00)	0.25 (1.00)	0.503 (1.00)	0.829 (1.00)	0.593 (1.00)	0.199 (1.00)
AGE	ANOVA	0.0632 (1.00)	0.0677 (1.00)	0.473 (1.00)	0.164 (1.00)	0.733 (1.00)	0.63 (1.00)	0.233 (1.00)	0.766 (1.00)
GENDER	Fisher's exact test	0.549 (1.00)	0.0368 (1.00)	0.633 (1.00)	0.196 (1.00)	0.251 (1.00)	0.195 (1.00)	0.155 (1.00)	0.46 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA	0.698 (1.00)	0.854 (1.00)	0.465 (1.00)	0.342 (1.00)	0.877 (1.00)	0.962 (1.00)	0.73 (1.00)	0.453 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.259 (1.00)	0.785 (1.00)	0.657 (1.00)	0.677 (1.00)	0.718 (1.00)	0.566 (1.00)	0.465 (1.00)	0.345 (1.00)
STOPPEDSMOKINGYEAR	ANOVA	0.101 (1.00)	0.266 (1.00)	0.955 (1.00)	0.56 (1.00)	0.34 (1.00)	0.302 (1.00)	0.805 (1.00)	0.481 (1.00)
TOBACCOSMOKINGHISTORYINDICATOR	ANOVA	0.535 (1.00)	0.606 (1.00)	0.258 (1.00)	0.412 (1.00)	0.604 (1.00)	0.318 (1.00)	0.843 (1.00)	0.946 (1.00)
DISTANT METASTASIS	Chi-square test	0.471 (1.00)	0.525 (1.00)	0.108 (1.00)	0.154 (1.00)	0.39 (1.00)	0.168 (1.00)	0.97 (1.00)	0.771 (1.00)
LYMPH NODE METASTASIS	Chi-square test	0.00135 (0.118)	0.054 (1.00)	0.082 (1.00)	0.426 (1.00)	0.142 (1.00)	0.00134 (0.118)	0.0573 (1.00)	0.0737 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.00189 (0.162)	0.223 (1.00)	0.201 (1.00)	0.121 (1.00)	0.973 (1.00)	0.141 (1.00)	0.145 (1.00)	0.106 (1.00)
TUMOR STAGECODE	ANOVA
NEOPLASM DISEASESTAGE	Chi-square test	0.0462 (1.00)	0.0942 (1.00)	0.279 (1.00)	0.199 (1.00)	0.738 (1.00)	0.415 (1.00)	0.0443 (1.00)	0.0261 (1.00)

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

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

Cluster Labels	1	2	3	4	5	6
Number of samples	13	41	20	26	10	15

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

P value = 0.0233 (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	118	31	0.1 - 131.2 (6.9)
subtype1	13	2	2.2 - 118.9 (7.2)
subtype2	39	12	0.1 - 131.2 (6.6)
subtype3	19	4	0.4 - 100.5 (6.4)
subtype4	23	5	0.5 - 46.8 (8.9)
subtype5	10	5	3.6 - 12.9 (7.3)
subtype6	14	3	1.0 - 75.3 (4.3)

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 = 0.0632 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	124	67.1 (10.7)
subtype1	13	67.0 (11.4)
subtype2	41	66.9 (10.8)
subtype3	20	67.8 (9.0)
subtype4	25	63.5 (12.2)
subtype5	10	64.9 (7.2)
subtype6	15	74.4 (8.6)

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 'GENDER'

P value = 0.549 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	33	92
subtype1	1	12
subtype2	12	29
subtype3	5	15
subtype4	6	20
subtype5	4	6
subtype6	5	10

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.698 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	37	78.1 (16.3)
subtype1	5	86.0 (5.5)
subtype2	8	75.0 (20.7)
subtype3	7	78.6 (18.6)
subtype4	11	78.2 (16.6)
subtype5	2	85.0 (7.1)
subtype6	4	70.0 (16.3)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.259 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	77	35.6 (21.8)
subtype1	11	26.0 (27.0)
subtype2	22	32.0 (17.8)
subtype3	14	35.6 (21.1)
subtype4	13	40.5 (14.8)
subtype5	7	49.9 (24.4)
subtype6	10	38.1 (27.8)

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

'Copy Number Ratio CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.101 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	63	1991.6 (16.9)
subtype1	8	1989.2 (24.5)
subtype2	20	1990.0 (15.7)
subtype3	10	1985.8 (19.0)
subtype4	12	2001.0 (7.6)
subtype5	4	2004.8 (9.2)
subtype6	9	1985.1 (16.0)

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.535 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	119	2.6 (1.1)
subtype1	12	3.0 (1.0)
subtype2	38	2.4 (1.1)
subtype3	19	2.7 (1.2)
subtype4	26	2.7 (1.3)
subtype5	9	2.9 (1.2)
subtype6	15	2.4 (1.1)

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

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.471 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	73	4	47
subtype1	7	1	5
subtype2	22	1	18
subtype3	9	0	10
subtype4	20	0	6
subtype5	6	1	3
subtype6	9	1	5

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00135 (Chi-square test), Q value = 0.12

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

nPatients	N0	N1	N2	N3	NX
ALL	71	14	24	4	10
subtype1	3	2	2	1	5
subtype2	31	3	5	1	1
subtype3	10	1	5	0	3
subtype4	18	2	5	0	0
subtype5	4	3	1	1	1
subtype6	5	3	6	1	0

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

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

P value = 0.00189 (ANOVA), Q value = 0.16

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

	nPatients	Mean (Std.Dev)
ALL	94	1.9 (3.8)
subtype1	9	1.8 (2.7)
subtype2	32	0.9 (1.9)
subtype3	14	0.9 (1.3)
subtype4	17	1.5 (2.3)
subtype5	8	2.0 (4.1)
subtype6	14	5.8 (7.4)

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

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

P value = 0.0462 (Chi-square test), Q value = 1

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	35	40	44
subtype1	1	4	2	6
subtype2	0	11	20	9
subtype3	0	8	4	7
subtype4	0	6	10	7
subtype5	0	3	2	5
subtype6	0	3	2	10

Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S13. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	30	45	33	20

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.56 (logrank test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	121	32	0.1 - 131.2 (6.8)
subtype1	29	5	0.8 - 118.9 (6.4)
subtype2	44	15	0.4 - 131.2 (7.7)
subtype3	28	5	0.7 - 37.8 (6.4)
subtype4	20	7	0.1 - 46.8 (8.0)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0677 (ANOVA), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	127	67.2 (10.6)
subtype1	29	67.3 (10.3)
subtype2	45	68.4 (9.6)
subtype3	33	63.3 (12.2)
subtype4	20	70.7 (9.5)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0368 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	94
subtype1	10	20
subtype2	16	29
subtype3	3	30
subtype4	5	15

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.854 (ANOVA), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	38	78.4 (16.2)
subtype1	7	82.9 (15.0)
subtype2	10	79.0 (15.2)
subtype3	16	76.2 (18.9)
subtype4	5	78.0 (13.0)

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.785 (ANOVA), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	78	36.1 (22.1)
subtype1	19	31.8 (23.9)
subtype2	32	36.8 (21.2)
subtype3	21	38.2 (21.7)
subtype4	6	39.5 (26.1)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.266 (ANOVA), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	64	1991.8 (16.8)
subtype1	12	1995.2 (15.1)
subtype2	25	1988.9 (16.0)
subtype3	17	1997.1 (16.3)
subtype4	10	1986.2 (20.7)

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.606 (ANOVA), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	122	2.6 (1.1)
subtype1	28	2.5 (1.2)
subtype2	42	2.6 (1.0)
subtype3	33	2.8 (1.3)
subtype4	19	2.4 (1.1)

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.525 (Chi-square test), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	76	4	47
subtype1	18	2	9
subtype2	24	1	20
subtype3	20	0	13
subtype4	14	1	5

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.054 (Chi-square test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	73	14	25	4	10
subtype1	10	5	9	1	4
subtype2	31	5	5	2	2
subtype3	24	3	3	1	2
subtype4	8	1	8	0	2

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

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

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	94	1.9 (3.8)
subtype1	21	2.3 (3.7)
subtype2	35	1.1 (2.5)
subtype3	24	1.7 (4.7)
subtype4	14	3.6 (4.8)

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0942 (Chi-square test), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	36	41	45
subtype1	1	7	6	15
subtype2	0	11	20	12
subtype3	0	13	11	8
subtype4	0	5	4	10

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S25. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	12	10	14	17

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.245 (logrank test), Q value = 1

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	52	20	0.4 - 118.9 (8.3)
subtype1	12	4	5.7 - 49.9 (10.9)
subtype2	10	5	1.8 - 100.5 (5.5)
subtype3	13	2	0.5 - 19.5 (6.6)
subtype4	17	9	0.4 - 118.9 (14.9)

Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.473 (ANOVA), Q value = 1

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	52	67.3 (9.8)
subtype1	12	63.9 (9.2)
subtype2	10	69.5 (9.3)
subtype3	13	66.5 (10.2)
subtype4	17	69.1 (10.3)

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.633 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	21	32
subtype1	6	6
subtype2	5	5
subtype3	5	9
subtype4	5	12

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

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

P value = 0.465 (ANOVA), Q value = 1

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	80.0 (16.7)
subtype1	5	88.0 (4.5)
subtype2	1	70.0 (NA)
subtype3	1	60.0 (NA)
subtype4	4	77.5 (25.0)

Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.657 (ANOVA), Q value = 1

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	27	32.1 (17.5)
subtype1	9	27.6 (12.4)
subtype2	5	28.8 (18.4)
subtype3	6	35.2 (20.4)
subtype4	7	37.9 (21.6)

Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.955 (ANOVA), Q value = 1

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	21	1990.1 (16.1)
subtype1	6	1989.7 (18.5)
subtype2	2	1979.0 (11.3)
subtype3	5	1992.8 (13.9)
subtype4	8	1991.5 (17.9)

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.258 (ANOVA), Q value = 1

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	48	2.3 (1.1)
subtype1	12	2.6 (0.9)
subtype2	8	1.9 (0.8)
subtype3	14	2.1 (1.1)
subtype4	14	2.6 (1.2)

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.108 (Chi-square test), Q value = 1

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	41	2	10
subtype1	7	2	3
subtype2	10	0	0
subtype3	11	0	3
subtype4	13	0	4

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'RPPA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.082 (Chi-square test), Q value = 1

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	33	5	11	1	2
subtype1	9	1	0	0	2
subtype2	4	1	5	0	0
subtype3	10	0	3	0	0
subtype4	10	3	3	1	0

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.201 (ANOVA), Q value = 1

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	34	1.5 (3.0)
subtype1	5	0.0 (0.0)
subtype2	7	3.4 (4.1)
subtype3	9	0.9 (1.5)
subtype4	13	1.5 (3.3)

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.279 (Chi-square test), Q value = 1

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	11	19	18
subtype1	1	5	4	2
subtype2	0	2	2	6
subtype3	0	2	6	3
subtype4	0	2	7	7

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S37. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	14	17	22

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.25 (logrank test), Q value = 1

Table S38. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	52	20	0.4 - 118.9 (8.3)
subtype1	14	5	1.9 - 49.9 (8.3)
subtype2	17	10	0.4 - 118.9 (8.2)
subtype3	21	5	0.5 - 100.5 (7.7)

Figure S34. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.164 (ANOVA), Q value = 1

Table S39. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	52	67.3 (9.8)
subtype1	14	63.5 (9.6)
subtype2	17	70.2 (9.0)
subtype3	21	67.5 (10.2)

Figure S35. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.196 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	21	32
subtype1	8	6
subtype2	7	10
subtype3	6	16

Figure S36. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.342 (ANOVA), Q value = 1

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	80.0 (16.7)
subtype1	4	90.0 (0.0)
subtype2	4	72.5 (23.6)
subtype3	3	76.7 (15.3)

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

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.677 (ANOVA), Q value = 1

Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	27	32.1 (17.5)
subtype1	9	30.4 (15.6)
subtype2	8	29.1 (18.5)
subtype3	10	36.1 (19.4)

Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.56 (ANOVA), Q value = 1

Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	21	1990.1 (16.1)
subtype1	4	1997.8 (13.0)
subtype2	9	1987.0 (16.8)
subtype3	8	1989.8 (17.2)

Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.412 (ANOVA), Q value = 1

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	48	2.3 (1.1)
subtype1	13	2.3 (0.9)
subtype2	14	2.6 (1.1)
subtype3	21	2.1 (1.2)

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.154 (Chi-square test), Q value = 1

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	41	2	10
subtype1	9	2	3
subtype2	13	0	4
subtype3	19	0	3

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'RPPA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.426 (Chi-square test), Q value = 1

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	33	5	11	1	2
subtype1	11	1	1	0	1
subtype2	8	3	5	1	0
subtype3	14	1	5	0	1

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.121 (ANOVA), Q value = 1

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	34	1.5 (3.0)
subtype1	7	0.6 (1.5)
subtype2	13	2.8 (4.3)
subtype3	14	0.8 (1.3)

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.199 (Chi-square test), Q value = 1

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	11	19	18
subtype1	0	6	5	3
subtype2	0	2	5	9
subtype3	1	3	9	6

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S49. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	49	31	35

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.503 (logrank test), Q value = 1

Table S50. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	108	30	0.1 - 131.2 (7.0)
subtype1	44	9	0.1 - 100.5 (6.5)
subtype2	30	11	1.3 - 131.2 (6.3)
subtype3	34	10	0.4 - 75.3 (8.1)

Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.733 (ANOVA), Q value = 1

Table S51. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	114	66.8 (10.8)
subtype1	48	65.9 (11.7)
subtype2	31	67.8 (9.1)
subtype3	35	67.2 (11.3)

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.251 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	32	83
subtype1	10	39
subtype2	9	22
subtype3	13	22

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.877 (ANOVA), Q value = 1

Table S53. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	34	77.6 (16.9)
subtype1	19	78.4 (16.8)
subtype2	5	74.0 (11.4)
subtype3	10	78.0 (20.4)

Figure S48. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.718 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	68	35.3 (22.7)
subtype1	24	38.1 (28.2)
subtype2	21	32.5 (18.5)
subtype3	23	35.1 (20.1)

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

'RNAseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.34 (ANOVA), Q value = 1

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	58	1991.6 (17.3)
subtype1	20	1994.7 (19.5)
subtype2	18	1993.2 (15.8)
subtype3	20	1987.0 (15.9)

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.604 (ANOVA), Q value = 1

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	109	2.6 (1.1)
subtype1	46	2.5 (1.3)
subtype2	30	2.7 (1.1)
subtype3	33	2.7 (1.0)

Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.39 (Chi-square test), Q value = 1

Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	69	3	42
subtype1	32	1	15
subtype2	16	2	13
subtype3	21	0	14

Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.142 (Chi-square test), Q value = 1

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	67	11	22	4	9
subtype1	25	4	13	0	6
subtype2	19	2	7	2	1
subtype3	23	5	2	2	2

Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.973 (ANOVA), Q value = 1

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	86	1.9 (3.9)
subtype1	31	2.0 (3.1)
subtype2	29	2.0 (4.0)
subtype3	26	1.8 (4.8)

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.738 (Chi-square test), Q value = 1

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	34	37	39
subtype1	1	15	13	17
subtype2	0	7	13	11
subtype3	0	12	11	11

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S61. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	37	17	61

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.829 (logrank test), Q value = 1

Table S62. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	108	30	0.1 - 131.2 (7.0)
subtype1	36	9	0.4 - 75.3 (7.9)
subtype2	16	7	4.5 - 131.2 (7.0)
subtype3	56	14	0.1 - 100.5 (6.3)

Figure S56. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.63 (ANOVA), Q value = 1

Table S63. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	114	66.8 (10.8)
subtype1	37	67.6 (11.2)
subtype2	17	64.6 (8.8)
subtype3	60	67.0 (11.2)

Figure S57. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.195 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	32	83
subtype1	14	23
subtype2	5	12
subtype3	13	48

Figure S58. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.962 (ANOVA), Q value = 1

Table S65. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	34	77.6 (16.9)
subtype1	10	78.0 (20.4)
subtype2	3	80.0 (10.0)
subtype3	21	77.1 (16.5)

Figure S59. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.566 (ANOVA), Q value = 1

Table S66. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	68	35.3 (22.7)
subtype1	24	31.7 (17.5)
subtype2	13	35.0 (20.7)
subtype3	31	38.3 (26.9)

Figure S60. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.302 (ANOVA), Q value = 1

Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	58	1991.6 (17.3)
subtype1	22	1987.2 (16.3)
subtype2	10	1996.3 (15.9)
subtype3	26	1993.4 (18.3)

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

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.318 (ANOVA), Q value = 1

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	109	2.6 (1.1)
subtype1	36	2.7 (1.1)
subtype2	15	2.9 (0.9)
subtype3	58	2.5 (1.2)

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.168 (Chi-square test), Q value = 1

Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	69	3	42
subtype1	21	0	16
subtype2	8	0	9
subtype3	40	3	17

Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00134 (Chi-square test), Q value = 0.12

Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	67	11	22	4	9
subtype1	25	6	1	2	2
subtype2	13	0	1	2	1
subtype3	29	5	20	0	6

Figure S64. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.141 (ANOVA), Q value = 1

Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	86	1.9 (3.9)
subtype1	29	1.6 (4.5)
subtype2	15	0.5 (1.0)
subtype3	42	2.7 (4.0)

Figure S65. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.415 (Chi-square test), Q value = 1

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	34	37	39
subtype1	0	11	14	11
subtype2	0	6	8	3
subtype3	1	17	15	25

Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S73. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	27	60	35

'MIRSEQ CNMF' versus 'Time to Death'

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

Table S74. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	115	30	0.1 - 131.2 (6.9)
subtype1	23	3	0.8 - 118.9 (5.3)
subtype2	57	18	0.4 - 49.9 (6.9)
subtype3	35	9	0.1 - 131.2 (8.3)

Figure S67. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.233 (ANOVA), Q value = 1

Table S75. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	121	67.0 (10.9)
subtype1	26	69.1 (12.2)
subtype2	60	65.3 (10.6)
subtype3	35	68.3 (10.1)

Figure S68. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.155 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	89
subtype1	4	23
subtype2	16	44
subtype3	13	22

Figure S69. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.73 (ANOVA), Q value = 1

Table S77. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	38	78.4 (16.2)
subtype1	7	82.9 (9.5)
subtype2	25	77.6 (18.5)
subtype3	6	76.7 (12.1)

Figure S70. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.465 (ANOVA), Q value = 1

Table S78. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	73	35.6 (22.1)
subtype1	16	29.6 (23.2)
subtype2	38	37.7 (20.2)
subtype3	19	36.2 (25.1)

Figure S71. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.805 (ANOVA), Q value = 1

Table S79. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	62	1991.9 (17.1)
subtype1	14	1990.9 (21.8)
subtype2	29	1993.4 (14.7)
subtype3	19	1990.3 (17.5)

Figure S72. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.843 (ANOVA), Q value = 1

Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	116	2.6 (1.2)
subtype1	26	2.6 (1.3)
subtype2	58	2.6 (1.1)
subtype3	32	2.7 (1.2)

Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.97 (Chi-square test), Q value = 1

Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	72	3	46
subtype1	15	1	10
subtype2	37	1	22
subtype3	20	1	14

Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0573 (Chi-square test), Q value = 1

Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	71	12	23	4	10
subtype1	12	5	5	2	2
subtype2	41	5	6	2	6
subtype3	18	2	12	0	2

Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.145 (ANOVA), Q value = 1

Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	91	1.9 (3.8)
subtype1	21	2.7 (5.4)
subtype2	42	1.0 (2.4)
subtype3	28	2.6 (4.2)

Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0443 (Chi-square test), Q value = 1

Table S84. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	36	39	41
subtype1	1	6	5	13
subtype2	0	21	24	13
subtype3	0	9	10	15

Figure S77. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S85. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	17	88	17

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.199 (logrank test), Q value = 1

Table S86. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	115	30	0.1 - 131.2 (6.9)
subtype1	13	1	1.5 - 118.9 (6.7)
subtype2	85	23	0.1 - 131.2 (7.0)
subtype3	17	6	0.8 - 46.8 (5.1)

Figure S78. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.766 (ANOVA), Q value = 1

Table S87. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	121	67.0 (10.9)
subtype1	16	67.2 (13.6)
subtype2	88	66.6 (10.8)
subtype3	17	68.7 (8.3)

Figure S79. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.46 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	89
subtype1	3	14
subtype2	27	61
subtype3	3	14

Figure S80. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

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

P value = 0.453 (ANOVA), Q value = 1

Table S89. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	38	78.4 (16.2)
subtype1	5	82.0 (11.0)
subtype2	31	77.4 (17.3)
subtype3	2	85.0 (7.1)

Figure S81. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.345 (ANOVA), Q value = 1

Table S90. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	73	35.6 (22.1)
subtype1	10	32.4 (26.1)
subtype2	52	34.3 (20.5)
subtype3	11	44.5 (26.0)

Figure S82. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'STOPPEDSMOKINGYEAR'

P value = 0.481 (ANOVA), Q value = 1

Table S91. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	62	1991.9 (17.1)
subtype1	9	1996.6 (18.3)
subtype2	43	1990.1 (18.0)
subtype3	10	1995.2 (11.4)

Figure S83. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.946 (ANOVA), Q value = 1

Table S92. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	116	2.6 (1.2)
subtype1	16	2.6 (1.3)
subtype2	83	2.6 (1.1)
subtype3	17	2.7 (1.2)

Figure S84. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.771 (Chi-square test), Q value = 1

Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	72	3	46
subtype1	9	1	7
subtype2	52	2	34
subtype3	11	0	5

Figure S85. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0737 (Chi-square test), Q value = 1

Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	NX
ALL	71	12	23	4	10
subtype1	8	4	2	1	2
subtype2	57	5	15	2	8
subtype3	6	3	6	1	0

Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

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

P value = 0.106 (ANOVA), Q value = 1

Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	91	1.9 (3.8)
subtype1	12	1.6 (3.4)
subtype2	64	1.5 (3.1)
subtype3	15	3.8 (6.1)

Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0261 (Chi-square test), Q value = 1

Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	1	36	39	41
subtype1	1	3	4	7
subtype2	0	29	32	24
subtype3	0	4	3	10

Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = BLCA-TP.mergedcluster.txt
Clinical data file = BLCA-TP.clin.merged.picked.txt
Number of patients = 128
Number of clustering approaches = 8
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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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)

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