Kidney Renal Clear Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 7 different clustering approaches and 9 clinical features across 499 patients, 33 significant findings detected with P value < 0.05.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.T' and 'TUMOR.STAGE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.T' and 'TUMOR.STAGE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
CNMF clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 9 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 33 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRseq CNMF subtypes	MIRseq cHierClus subtypes
Time to Death	logrank test	0.0868	0.189	9.14e-08	2.59e-07	1.03e-09	3.31e-07	1.62e-08
AGE	ANOVA	0.795	0.607	0.0456	0.323	0.335	0.0787	0.892
GENDER	Fisher's exact test	0.634	0.82	0.0262	1.17e-05	0.00361	0.0142	0.000362
KARNOFSKY PERFORMANCE SCORE	ANOVA			0.647	0.709	0.342	0.18	0.827
PATHOLOGY T	Chi-square test	0.00911	0.00779	8.27e-11	1.37e-05	1.66e-10	0.00171	3.55e-06
PATHOLOGY N	Fisher's exact test	0.0704	0.124	0.0718	0.00986	0.00446	0.072	0.0257
PATHOLOGICSPREAD(M)	Fisher's exact test	0.12	0.0988	9.62e-05	0.000475	4.85e-05	0.00738	0.000451
TUMOR STAGE	Chi-square test	0.0134	0.0102	2.15e-10	2.6e-06	7.47e-11	0.00357	1.61e-05
NEOADJUVANT THERAPY	Fisher's exact test	0.194	0.208	0.782	1	0.654	0.52	0.133

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	34	24	14

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0868 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	71	13	0.5 - 101.1 (32.6)
subtype1	33	4	0.5 - 101.1 (31.0)
subtype2	24	8	0.5 - 93.3 (36.7)
subtype3	14	1	1.3 - 84.4 (25.0)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.795 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	71	60.5 (12.4)
subtype1	33	60.2 (13.8)
subtype2	24	59.9 (11.1)
subtype3	14	62.6 (11.3)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.634 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	29	43
subtype1	15	19
subtype2	10	14
subtype3	4	10

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00911 (Chi-square test)

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3
ALL	41	14	17
subtype1	23	4	7
subtype2	10	4	10
subtype3	8	6	0

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0704 (Fisher's exact test)

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	0	1
ALL	35	3
subtype1	18	0
subtype2	10	3
subtype3	7	0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'mRNA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.12 (Fisher's exact test)

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	67	5
subtype1	33	1
subtype2	20	4
subtype3	14	0

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0134 (Chi-square test)

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	40	15	12	5
subtype1	23	5	5	1
subtype2	9	4	7	4
subtype3	8	6	0	0

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.194 (Fisher's exact test)

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	1	71
subtype1	0	34
subtype2	0	24
subtype3	1	13

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	15	23	34

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.189 (logrank test)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	71	13	0.5 - 101.1 (32.6)
subtype1	15	2	1.3 - 84.4 (24.2)
subtype2	23	7	0.5 - 93.3 (36.8)
subtype3	33	4	0.5 - 101.1 (31.0)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.607 (ANOVA)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	71	60.5 (12.4)
subtype1	15	63.2 (11.2)
subtype2	23	59.1 (10.7)
subtype3	33	60.4 (14.0)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.82 (Fisher's exact test)

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	29	43
subtype1	5	10
subtype2	9	14
subtype3	15	19

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.00779 (Chi-square test)

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3
ALL	41	14	17
subtype1	9	6	0
subtype2	9	4	10
subtype3	23	4	7

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.124 (Fisher's exact test)

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	0	1
ALL	35	3
subtype1	7	0
subtype2	11	3
subtype3	17	0

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'mRNA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0988 (Fisher's exact test)

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	67	5
subtype1	15	0
subtype2	19	4
subtype3	33	1

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0102 (Chi-square test)

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	40	15	12	5
subtype1	9	6	0	0
subtype2	8	4	7	4
subtype3	23	5	5	1

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.208 (Fisher's exact test)

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	1	71
subtype1	1	14
subtype2	0	23
subtype3	0	34

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S19. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	79	84	60	60

'METHLYATION CNMF' versus 'Time to Death'

P value = 9.14e-08 (logrank test)

Table S20. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	281	95	0.1 - 109.9 (28.5)
subtype1	78	41	0.2 - 84.7 (27.7)
subtype2	84	18	0.1 - 109.6 (34.9)
subtype3	59	9	0.3 - 91.4 (29.9)
subtype4	60	27	0.1 - 109.9 (19.1)

Figure S17. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0456 (ANOVA)

Table S21. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	283	61.5 (12.0)
subtype1	79	64.2 (10.7)
subtype2	84	60.8 (11.7)
subtype3	60	58.6 (13.7)
subtype4	60	61.7 (11.7)

Figure S18. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0262 (Fisher's exact test)

Table S22. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	96	187
subtype1	19	60
subtype2	32	52
subtype3	28	32
subtype4	17	43

Figure S19. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.647 (ANOVA)

Table S23. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	28	92.5 (8.0)
subtype1	5	90.0 (7.1)
subtype2	10	91.0 (9.9)
subtype3	9	94.4 (5.3)
subtype4	4	95.0 (10.0)

Figure S20. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 8.27e-11 (Chi-square test)

Table S24. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	132	36	107	8
subtype1	14	14	48	3
subtype2	52	14	18	0
subtype3	44	4	11	1
subtype4	22	4	30	4

Figure S21. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.0718 (Fisher's exact test)

Table S25. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	0	1
ALL	127	9
subtype1	35	3
subtype2	38	1
subtype3	27	0
subtype4	27	5

Figure S22. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 9.62e-05 (Fisher's exact test)

Table S26. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	232	51
subtype1	53	26
subtype2	74	10
subtype3	57	3
subtype4	48	12

Figure S23. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 2.15e-10 (Chi-square test)

Table S27. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	130	24	73	56
subtype1	14	9	28	28
subtype2	52	9	13	10
subtype3	44	3	10	3
subtype4	20	3	22	15

Figure S24. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.782 (Fisher's exact test)

Table S28. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	4	279
subtype1	2	77
subtype2	1	83
subtype3	1	59
subtype4	0	60

Figure S25. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S29. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	198	100	171

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 2.59e-07 (logrank test)

Table S30. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	467	154	0.1 - 111.0 (34.3)
subtype1	198	45	0.1 - 111.0 (37.9)
subtype2	99	29	0.1 - 93.3 (31.3)
subtype3	170	80	0.1 - 90.3 (29.8)

Figure S26. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.323 (ANOVA)

Table S31. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	468	60.6 (12.2)
subtype1	197	61.3 (12.2)
subtype2	100	59.1 (12.5)
subtype3	171	60.7 (12.0)

Figure S27. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 1.17e-05 (Fisher's exact test)

Table S32. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	162	307
subtype1	92	106
subtype2	30	70
subtype3	40	131

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

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

P value = 0.709 (ANOVA)

Table S33. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	31	91.0 (18.7)
subtype1	14	92.1 (8.9)
subtype2	7	94.3 (7.9)
subtype3	10	87.0 (31.3)

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 1.37e-05 (Chi-square test)

Table S34. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	229	58	171	11
subtype1	114	23	59	2
subtype2	60	10	27	3
subtype3	55	25	85	6

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.00986 (Fisher's exact test)

Table S35. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	0	1
ALL	224	17
subtype1	96	2
subtype2	49	3
subtype3	79	12

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000475 (Fisher's exact test)

Table S36. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	393	76
subtype1	175	23
subtype2	90	10
subtype3	128	43

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 2.6e-06 (Chi-square test)

Table S37. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	225	47	117	80
subtype1	114	19	41	24
subtype2	59	9	20	12
subtype3	52	19	56	44

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S38. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	5	464
subtype1	2	196
subtype2	1	99
subtype3	2	169

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S39. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	52	221	196

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1.03e-09 (logrank test)

Table S40. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	467	154	0.1 - 111.0 (34.3)
subtype1	51	10	0.2 - 92.0 (24.2)
subtype2	220	49	0.1 - 111.0 (38.5)
subtype3	196	95	0.1 - 93.3 (28.9)

Figure S35. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.335 (ANOVA)

Table S41. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	468	60.6 (12.2)
subtype1	52	58.5 (12.9)
subtype2	220	60.5 (12.4)
subtype3	196	61.3 (11.7)

Figure S36. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00361 (Fisher's exact test)

Table S42. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	162	307
subtype1	17	35
subtype2	93	128
subtype3	52	144

Figure S37. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.342 (ANOVA)

Table S43. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	31	91.0 (18.7)
subtype1	9	95.6 (10.1)
subtype2	13	93.1 (6.3)
subtype3	9	83.3 (32.4)

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 1.66e-10 (Chi-square test)

Table S44. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	229	58	171	11
subtype1	40	5	6	1
subtype2	128	28	64	1
subtype3	61	25	101	9

Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00446 (Fisher's exact test)

Table S45. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	0	1
ALL	224	17
subtype1	26	1
subtype2	104	2
subtype3	94	14

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 4.85e-05 (Fisher's exact test)

Table S46. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	393	76
subtype1	49	3
subtype2	197	24
subtype3	147	49

Figure S41. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 7.47e-11 (Chi-square test)

Table S47. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	225	47	117	80
subtype1	40	5	4	3
subtype2	127	22	47	25
subtype3	58	20	66	52

Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.654 (Fisher's exact test)

Table S48. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	5	464
subtype1	1	51
subtype2	2	219
subtype3	2	194

Figure S43. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S49. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	172	84	64	143

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 3.31e-07 (logrank test)

Table S50. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	461	149	0.1 - 111.0 (33.5)
subtype1	172	37	0.1 - 111.0 (36.6)
subtype2	84	22	0.1 - 109.9 (37.1)
subtype3	64	22	0.1 - 91.4 (31.4)
subtype4	141	68	0.2 - 93.3 (29.1)

Figure S44. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0787 (ANOVA)

Table S51. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	463	60.7 (12.2)
subtype1	172	62.3 (12.3)
subtype2	84	58.1 (11.5)
subtype3	64	60.7 (12.7)
subtype4	143	60.4 (11.9)

Figure S45. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0142 (Fisher's exact test)

Table S52. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	159	304
subtype1	74	98
subtype2	29	55
subtype3	18	46
subtype4	38	105

Figure S46. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.18 (ANOVA)

Table S53. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	35	88.0 (23.2)
subtype1	14	92.1 (8.9)
subtype2	6	95.0 (8.4)
subtype3	6	93.3 (5.2)
subtype4	9	73.3 (42.1)

Figure S47. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00171 (Chi-square test)

Table S54. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	225	59	169	10
subtype1	95	22	53	2
subtype2	50	5	25	4
subtype3	31	7	26	0
subtype4	49	25	65	4

Figure S48. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.072 (Fisher's exact test)

Table S55. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	0	1
ALL	214	16
subtype1	78	2
subtype2	41	2
subtype3	26	2
subtype4	69	10

Figure S49. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.00738 (Fisher's exact test)

Table S56. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	391	72
subtype1	154	18
subtype2	75	9
subtype3	53	11
subtype4	109	34

Figure S50. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.00357 (Chi-square test)

Table S57. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	221	47	119	76
subtype1	95	17	40	20
subtype2	49	6	19	10
subtype3	29	5	20	10
subtype4	48	19	40	36

Figure S51. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.52 (Fisher's exact test)

Table S58. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	5	458
subtype1	1	171
subtype2	1	83
subtype3	0	64
subtype4	3	140

Figure S52. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S59. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	99	123	241

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 1.62e-08 (logrank test)

Table S60. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	461	149	0.1 - 111.0 (33.5)
subtype1	99	15	0.4 - 109.6 (48.4)
subtype2	123	57	0.1 - 109.9 (28.8)
subtype3	239	77	0.1 - 111.0 (31.1)

Figure S53. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.892 (ANOVA)

Table S61. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	463	60.7 (12.2)
subtype1	99	60.3 (12.2)
subtype2	123	61.1 (12.8)
subtype3	241	60.8 (11.9)

Figure S54. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.000362 (Fisher's exact test)

Table S62. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	159	304
subtype1	51	48
subtype2	36	87
subtype3	72	169

Figure S55. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.827 (ANOVA)

Table S63. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	35	88.0 (23.2)
subtype1	5	94.0 (5.5)
subtype2	11	86.4 (29.4)
subtype3	19	87.4 (22.8)

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 3.55e-06 (Chi-square test)

Table S64. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	225	59	169	10
subtype1	68	10	21	0
subtype2	47	11	58	7
subtype3	110	38	90	3

Figure S57. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0257 (Fisher's exact test)

Table S65. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	0	1
ALL	214	16
subtype1	43	0
subtype2	54	8
subtype3	117	8

Figure S58. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000451 (Fisher's exact test)

Table S66. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	391	72
subtype1	95	4
subtype2	99	24
subtype3	197	44

Figure S59. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 1.61e-05 (Chi-square test)

Table S67. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	221	47	119	76
subtype1	68	9	18	4
subtype2	46	9	42	26
subtype3	107	29	59	46

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.133 (Fisher's exact test)

Table S68. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	5	458
subtype1	0	99
subtype2	0	123
subtype3	5	236

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Methods & Data

Input

Cluster data file = KIRC.mergedcluster.txt
Clinical data file = KIRC.clin.merged.picked.txt
Number of patients = 499
Number of clustering approaches = 7
Number of selected clinical features = 9
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

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

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

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