Bladder Urothelial Carcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Bladder Urothelial Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

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

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 7 clinical features across 117 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes do not correlate to any clinical features.
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 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that 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 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.


Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	NUMBERPACKYEARSSMOKED	STOPPEDSMOKINGYEAR	TOBACCOSMOKINGHISTORYINDICATOR
Statistical Tests	logrank test	ANOVA	Fisher's exact test	ANOVA	ANOVA	ANOVA	ANOVA
CN CNMF	0.877 (1.00)	0.493 (1.00)	0.859 (1.00)	0.614 (1.00)	0.803 (1.00)	0.755 (1.00)	0.92 (1.00)
METHLYATION CNMF	0.484 (1.00)	0.0426 (1.00)	0.032 (1.00)	0.863 (1.00)	0.763 (1.00)	0.24 (1.00)	0.537 (1.00)
RPPA CNMF subtypes	0.245 (1.00)	0.473 (1.00)	0.633 (1.00)	0.465 (1.00)	0.657 (1.00)	0.955 (1.00)	0.258 (1.00)
RPPA cHierClus subtypes	0.25 (1.00)	0.164 (1.00)	0.196 (1.00)	0.342 (1.00)	0.677 (1.00)	0.56 (1.00)	0.412 (1.00)
RNAseq CNMF subtypes	0.641 (1.00)	0.269 (1.00)	0.19 (1.00)	0.412 (1.00)	0.664 (1.00)	0.643 (1.00)	0.0533 (1.00)
RNAseq cHierClus subtypes	0.209 (1.00)	0.958 (1.00)	0.283 (1.00)	0.923 (1.00)	0.688 (1.00)	0.744 (1.00)	0.46 (1.00)
MIRseq CNMF subtypes	0.478 (1.00)	0.389 (1.00)	0.25 (1.00)	0.559 (1.00)	0.816 (1.00)	0.993 (1.00)	0.463 (1.00)
MIRseq cHierClus subtypes	0.878 (1.00)	0.47 (1.00)	0.801 (1.00)	0.715 (1.00)	0.15 (1.00)	0.627 (1.00)	0.385 (1.00)

Clustering Approach #1: 'CN CNMF'

Table S1. Get Full Table Description of clustering approach #1: 'CN CNMF'

Cluster Labels	1	2	3
Number of samples	26	55	33

'CN CNMF' versus 'Time to Death'

P value = 0.877 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	108	29	0.4 - 118.9 (6.8)
subtype1	25	6	0.8 - 75.3 (5.7)
subtype2	51	16	0.5 - 46.8 (7.2)
subtype3	32	7	0.4 - 118.9 (6.3)

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

'CN CNMF' versus 'AGE'

P value = 0.493 (ANOVA), Q value = 1

Table S3. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	113	67.2 (11.1)
subtype1	25	69.6 (11.2)
subtype2	55	66.7 (12.2)
subtype3	33	66.4 (8.8)

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

'CN CNMF' versus 'GENDER'

P value = 0.859 (Fisher's exact test), Q value = 1

Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	31	83
subtype1	8	18
subtype2	14	41
subtype3	9	24

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

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.614 (ANOVA), Q value = 1

Table S5. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	35	77.7 (16.6)
subtype1	9	82.2 (13.9)
subtype2	17	75.3 (18.7)
subtype3	9	77.8 (15.6)

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

'CN CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.803 (ANOVA), Q value = 1

Table S6. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	70	34.6 (21.7)
subtype1	17	34.5 (30.2)
subtype2	29	32.8 (19.3)
subtype3	24	36.8 (18.0)

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

'CN CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.755 (ANOVA), Q value = 1

Table S7. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	57	1991.5 (17.6)
subtype1	14	1989.6 (19.6)
subtype2	26	1990.7 (18.5)
subtype3	17	1994.1 (15.2)

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

'CN CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.92 (ANOVA), Q value = 1

Table S8. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	108	2.6 (1.1)
subtype1	25	2.6 (1.1)
subtype2	52	2.6 (1.2)
subtype3	31	2.7 (1.1)

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S9. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	29	37	32	19

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.484 (logrank test), Q value = 1

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	111	30	0.4 - 118.9 (6.7)
subtype1	28	5	0.8 - 118.9 (6.3)
subtype2	36	13	0.4 - 75.3 (7.2)
subtype3	28	5	0.7 - 37.8 (6.4)
subtype4	19	7	0.5 - 46.8 (8.7)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0426 (ANOVA), Q value = 1

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	116	67.3 (11.0)
subtype1	28	67.1 (10.5)
subtype2	37	68.9 (10.2)
subtype3	32	63.1 (12.3)
subtype4	19	71.4 (9.2)

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.032 (Fisher's exact test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	32	85
subtype1	10	19
subtype2	14	23
subtype3	3	29
subtype4	5	14

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.863 (ANOVA), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	36	78.1 (16.5)
subtype1	7	82.9 (15.0)
subtype2	8	77.5 (16.7)
subtype3	16	76.2 (18.9)
subtype4	5	78.0 (13.0)

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.763 (ANOVA), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	71	35.1 (22.1)
subtype1	18	31.4 (24.5)
subtype2	26	34.2 (20.6)
subtype3	21	38.2 (21.7)
subtype4	6	39.5 (26.1)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'STOPPEDSMOKINGYEAR'

P value = 0.24 (ANOVA), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	58	1991.8 (17.6)
subtype1	11	1996.0 (15.5)
subtype2	20	1987.7 (17.5)
subtype3	17	1997.1 (16.3)
subtype4	10	1986.2 (20.7)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.537 (ANOVA), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	111	2.6 (1.1)
subtype1	27	2.5 (1.2)
subtype2	34	2.6 (1.0)
subtype3	32	2.9 (1.3)
subtype4	18	2.5 (1.1)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S17. 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 S18. 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 - 61.9 (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 S15. 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 S19. 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 S16. 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 S20. 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 S17. 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 S21. 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 S18. 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 S22. 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 S19. 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 S23. 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 S20. 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 S24. 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 S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S25. 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 S26. 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 - 61.9 (8.3)
subtype2	17	10	0.4 - 118.9 (8.2)
subtype3	21	5	0.5 - 100.5 (7.7)

Figure S22. 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 S27. 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 S23. 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 S28. 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 S24. 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 S29. 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 S25. 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 S30. 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 S26. 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 S31. 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 S27. 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 S32. 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 S28. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S33. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	37	18	25	10

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.641 (logrank test), Q value = 1

Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	84	25	0.4 - 118.9 (7.2)
subtype1	32	9	0.5 - 100.5 (7.1)
subtype2	18	8	4.1 - 118.9 (6.7)
subtype3	25	6	0.4 - 75.3 (8.2)
subtype4	9	2	1.8 - 11.1 (5.1)

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.269 (ANOVA), Q value = 1

Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	89	66.4 (11.6)
subtype1	36	66.5 (12.2)
subtype2	18	62.7 (9.7)
subtype3	25	66.9 (12.1)
subtype4	10	71.6 (9.9)

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.19 (Fisher's exact test), Q value = 1

Table S36. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	63
subtype1	8	29
subtype2	4	14
subtype3	10	15
subtype4	5	5

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

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

Table S37. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	26	77.7 (17.7)
subtype1	12	75.8 (17.8)
subtype2	7	85.7 (7.9)
subtype3	6	73.3 (25.8)
subtype4	1	70.0 (NA)

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.664 (ANOVA), Q value = 1

Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	51	34.6 (22.7)
subtype1	15	40.8 (30.9)
subtype2	15	31.6 (19.5)
subtype3	16	33.2 (19.5)
subtype4	5	30.0 (12.2)

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.643 (ANOVA), Q value = 1

Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	42	1990.6 (18.5)
subtype1	14	1988.9 (20.8)
subtype2	9	1997.4 (17.3)
subtype3	14	1987.6 (17.6)
subtype4	5	1991.8 (18.9)

Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.0533 (ANOVA), Q value = 1

Table S40. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	84	2.5 (1.1)
subtype1	34	2.2 (1.2)
subtype2	16	3.0 (0.8)
subtype3	24	2.7 (1.0)
subtype4	10	2.1 (1.1)

Figure S35. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S41. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	25	44	21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.209 (logrank test), Q value = 1

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	84	25	0.4 - 118.9 (7.2)
subtype1	25	6	0.4 - 75.3 (8.3)
subtype2	39	10	0.5 - 100.5 (6.7)
subtype3	20	9	1.8 - 118.9 (5.9)

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.958 (ANOVA), Q value = 1

Table S43. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	89	66.4 (11.6)
subtype1	25	67.0 (12.0)
subtype2	43	66.2 (11.8)
subtype3	21	66.2 (11.1)

Figure S37. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.283 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	63
subtype1	10	15
subtype2	10	34
subtype3	7	14

Figure S38. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.923 (ANOVA), Q value = 1

Table S45. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	26	77.7 (17.7)
subtype1	7	75.7 (24.4)
subtype2	14	77.9 (17.2)
subtype3	5	80.0 (10.0)

Figure S39. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.688 (ANOVA), Q value = 1

Table S46. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	51	34.6 (22.7)
subtype1	16	33.5 (19.5)
subtype2	20	38.0 (30.3)
subtype3	15	31.4 (13.0)

Figure S40. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.744 (ANOVA), Q value = 1

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	42	1990.6 (18.5)
subtype1	14	1987.6 (17.6)
subtype2	18	1991.6 (20.0)
subtype3	10	1993.2 (18.4)

Figure S41. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.46 (ANOVA), Q value = 1

Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	84	2.5 (1.1)
subtype1	24	2.7 (1.0)
subtype2	41	2.3 (1.2)
subtype3	19	2.6 (1.0)

Figure S42. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S49. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	24	57	29

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.478 (logrank test), Q value = 1

Table S50. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	104	30	0.4 - 118.9 (7.0)
subtype1	20	3	0.8 - 118.9 (6.4)
subtype2	55	20	0.4 - 61.9 (7.0)
subtype3	29	7	0.5 - 100.5 (8.1)

Figure S43. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.389 (ANOVA), Q value = 1

Table S51. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	109	67.3 (11.0)
subtype1	23	68.4 (12.1)
subtype2	57	65.9 (10.9)
subtype3	29	69.1 (10.2)

Figure S44. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.25 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	31	79
subtype1	4	20
subtype2	16	41
subtype3	11	18

Figure S45. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.559 (ANOVA), Q value = 1

Table S53. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	31	76.8 (17.4)
subtype1	6	83.3 (10.3)
subtype2	20	74.5 (19.9)
subtype3	5	78.0 (13.0)

Figure S46. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.816 (ANOVA), Q value = 1

Table S54. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	65	35.6 (22.5)
subtype1	14	32.1 (27.0)
subtype2	35	36.3 (19.3)
subtype3	16	36.9 (26.1)

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

'MIRseq CNMF subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.993 (ANOVA), Q value = 1

Table S55. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	54	1991.4 (17.8)
subtype1	13	1990.9 (23.0)
subtype2	24	1991.7 (16.3)
subtype3	17	1991.4 (16.5)

Figure S48. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'MIRseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

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

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

	nPatients	Mean (Std.Dev)
ALL	104	2.6 (1.1)
subtype1	23	2.6 (1.3)
subtype2	54	2.5 (1.0)
subtype3	27	2.8 (1.2)

Figure S49. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S57. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	23	75	12

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.878 (logrank test), Q value = 1

Table S58. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	104	30	0.4 - 118.9 (7.0)
subtype1	19	4	1.5 - 118.9 (6.7)
subtype2	73	22	0.4 - 100.5 (7.3)
subtype3	12	4	0.8 - 46.8 (5.6)

Figure S50. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.47 (ANOVA), Q value = 1

Table S59. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	109	67.3 (11.0)
subtype1	22	69.5 (12.4)
subtype2	75	66.4 (11.0)
subtype3	12	68.5 (7.6)

Figure S51. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.801 (Fisher's exact test), Q value = 1

Table S60. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	31	79
subtype1	5	18
subtype2	23	52
subtype3	3	9

Figure S52. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.715 (ANOVA), Q value = 1

Table S61. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	31	76.8 (17.4)
subtype1	4	80.0 (11.5)
subtype2	24	75.4 (18.9)
subtype3	3	83.3 (11.5)

Figure S53. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.15 (ANOVA), Q value = 1

Table S62. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	65	35.6 (22.5)
subtype1	13	28.5 (24.2)
subtype2	43	35.2 (19.8)
subtype3	9	47.4 (29.8)

Figure S54. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBERPACKYEARSSMOKED'

'MIRseq cHierClus subtypes' versus 'STOPPEDSMOKINGYEAR'

P value = 0.627 (ANOVA), Q value = 1

Table S63. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

	nPatients	Mean (Std.Dev)
ALL	54	1991.4 (17.8)
subtype1	12	1987.2 (22.3)
subtype2	33	1992.2 (17.2)
subtype3	9	1994.2 (13.8)

Figure S55. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'STOPPEDSMOKINGYEAR'

'MIRseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.385 (ANOVA), Q value = 1

Table S64. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

	nPatients	Mean (Std.Dev)
ALL	104	2.6 (1.1)
subtype1	22	2.5 (1.3)
subtype2	70	2.6 (1.1)
subtype3	12	3.0 (1.0)

Figure S56. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

Methods & Data

Input

Cluster data file = BLCA-TP.mergedcluster.txt
Clinical data file = BLCA-TP.clin.merged.picked.txt
Number of patients = 117
Number of clustering approaches = 8
Number of selected clinical features = 7
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

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] 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)

[6] 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