Kidney Renal Papillary 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 8 clinical features across 95 patients, 16 significant findings detected with P value < 0.05.

CNMF clustering analysis on array-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGY.T' and 'TUMOR.STAGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death', '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', 'PATHOLOGY.T', 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 8 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 16 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	0.666	0.0699	0.0814	0.0626	0.0122	0.00206	0.0135
AGE	ANOVA	0.182	0.948	0.00131	0.434	0.246	0.257	0.789
GENDER	Fisher's exact test	0.585	1	0.379	0.0644	0.0625	0.918	0.597
KARNOFSKY PERFORMANCE SCORE	ANOVA			0.241		0.481
PATHOLOGY T	Chi-square test	0.0623	0.216	4.2e-07	0.00023	0.37	0.0389	0.0252
PATHOLOGY N	Fisher's exact test			0.222	0.934	0.0693	0.0601	0.119
PATHOLOGICSPREAD(M)	Fisher's exact test	1	1	0.0368	0.108	0.0402	0.0161	0.143
TUMOR STAGE	Chi-square test	0.292		8.48e-06	0.000784	0.00334	0.00425	0.0222

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2
Number of samples	7	9

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.666 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	16	2	0.5 - 58.5 (7.8)
subtype1	7	1	0.5 - 53.8 (5.9)
subtype2	9	1	1.1 - 58.5 (10.8)

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 = 0.182 (t-test)

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

	nPatients	Mean (Std.Dev)
ALL	16	57.9 (11.5)
subtype1	7	53.6 (10.3)
subtype2	9	61.3 (11.7)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.585 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	4	12
subtype1	1	6
subtype2	3	6

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 = 0.0623 (Chi-square test)

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

nPatients	T1	T2	T3+T4
ALL	7	7	2
subtype1	1	4	2
subtype2	6	3	0

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

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

P value = 1 (Fisher's exact test)

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	9	1
subtype1	4	1
subtype2	5	0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.292 (Chi-square test)

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	5	2	1	1
subtype1	1	1	1	1
subtype2	4	1	0	0

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	4	7	5

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0699 (logrank test)

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	16	2	0.5 - 58.5 (7.8)
subtype1	4	1	10.8 - 58.5 (40.4)
subtype2	7	1	0.5 - 25.1 (4.4)
subtype3	5	0	0.7 - 53.8 (4.1)

Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.948 (ANOVA)

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	16	57.9 (11.5)
subtype1	4	57.0 (5.0)
subtype2	7	57.4 (13.0)
subtype3	5	59.4 (14.8)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	4	12
subtype1	1	3
subtype2	2	5
subtype3	1	4

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.216 (Chi-square test)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	7	7	2
subtype1	3	1	0
subtype2	1	4	2
subtype3	3	2	0

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

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

P value = 1 (Fisher's exact test)

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	9	1
subtype1	2	0
subtype2	4	1
subtype3	3	0

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #3: 'METHLYATION CNMF'

Table S14. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	36	19	24

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0814 (logrank test)

Table S15. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	72	12	0.0 - 182.7 (20.9)
subtype1	32	2	0.0 - 129.9 (15.1)
subtype2	17	4	0.7 - 80.8 (26.3)
subtype3	23	6	0.9 - 182.7 (20.1)

Figure S12. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00131 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	76	60.5 (12.0)
subtype1	35	55.9 (9.4)
subtype2	18	67.9 (8.7)
subtype3	23	61.8 (14.5)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.379 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	24	55
subtype1	8	28
subtype2	7	12
subtype3	9	15

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

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

P value = 0.241 (ANOVA)

Table S18. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	19	92.1 (13.6)
subtype1	14	95.7 (5.1)
subtype2	4	77.5 (25.0)
subtype3	1	100.0 (NA)

Figure S15. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 4.2e-07 (Chi-square test)

Table S19. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	47	3	29
subtype1	30	2	4
subtype2	14	0	5
subtype3	3	1	20

Figure S16. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.222 (Chi-square test)

Table S20. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	19	8	4
subtype1	7	0	0
subtype2	3	2	1
subtype3	9	6	3

Figure S17. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0368 (Chi-square test)

Table S21. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	45	4	27
subtype1	16	0	17
subtype2	12	1	6
subtype3	17	3	4

Figure S18. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 8.48e-06 (Chi-square test)

Table S22. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	44	3	21	8
subtype1	28	1	4	0
subtype2	13	1	3	2
subtype3	3	1	14	6

Figure S19. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S23. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	22	18	12	11

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0626 (logrank test)

Table S24. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	13	0.5 - 123.6 (15.5)
subtype1	22	7	0.9 - 93.3 (16.2)
subtype2	18	2	0.5 - 63.7 (12.6)
subtype3	12	2	7.0 - 123.6 (29.1)
subtype4	11	2	3.8 - 80.8 (15.5)

Figure S20. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.434 (ANOVA)

Table S25. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	63	60.3 (12.5)
subtype1	22	59.2 (14.2)
subtype2	18	58.7 (11.7)
subtype3	12	65.8 (11.2)
subtype4	11	59.3 (11.6)

Figure S21. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0644 (Fisher's exact test)

Table S26. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	20	43
subtype1	11	11
subtype2	6	12
subtype3	1	11
subtype4	2	9

Figure S22. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00023 (Chi-square test)

Table S27. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	32	8	23
subtype1	6	1	15
subtype2	10	5	3
subtype3	5	2	5
subtype4	11	0	0

Figure S23. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.934 (Chi-square test)

Table S28. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	14	9	3
subtype1	7	6	2
subtype2	2	1	0
subtype3	4	2	1
subtype4	1	0	0

Figure S24. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.108 (Chi-square test)

Table S29. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	42	5	9
subtype1	14	5	3
subtype2	10	0	2
subtype3	11	0	1
subtype4	7	0	3

Figure S25. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.000784 (Chi-square test)

Table S30. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	29	3	16	7
subtype1	5	1	8	7
subtype2	9	0	3	0
subtype3	5	2	5	0
subtype4	10	0	0	0

Figure S26. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S31. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	16	25	22

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0122 (logrank test)

Table S32. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	13	0.5 - 123.6 (15.5)
subtype1	16	6	2.8 - 80.8 (10.9)
subtype2	25	3	0.5 - 123.6 (13.6)
subtype3	22	4	6.4 - 93.3 (25.2)

Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.246 (ANOVA)

Table S33. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	63	60.3 (12.5)
subtype1	16	59.1 (13.4)
subtype2	25	58.0 (11.3)
subtype3	22	63.9 (13.1)

Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0625 (Fisher's exact test)

Table S34. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	20	43
subtype1	9	7
subtype2	6	19
subtype3	5	17

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.481 (ANOVA)

Table S35. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	8	83.8 (34.2)
subtype1	3	93.3 (5.8)
subtype2	5	78.0 (43.8)

Figure S30. 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 = 0.37 (Chi-square test)

Table S36. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	32	8	23
subtype1	7	2	7
subtype2	14	5	6
subtype3	11	1	10

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0693 (Chi-square test)

Table S37. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	14	9	3
subtype1	1	5	2
subtype2	4	2	0
subtype3	9	2	1

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0402 (Chi-square test)

Table S38. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	42	5	9
subtype1	8	4	3
subtype2	15	1	4
subtype3	19	0	2

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.00334 (Chi-square test)

Table S39. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	29	3	16	7
subtype1	6	1	1	6
subtype2	13	1	5	1
subtype3	10	1	10	0

Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S40. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	14	15	14

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00206 (logrank test)

Table S41. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	43	8	0.5 - 86.7 (11.6)
subtype1	14	2	0.9 - 86.7 (25.6)
subtype2	15	1	0.5 - 58.5 (5.8)
subtype3	14	5	2.8 - 40.8 (11.2)

Figure S35. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.257 (ANOVA)

Table S42. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	43	60.0 (12.3)
subtype1	14	64.3 (10.0)
subtype2	15	59.0 (10.5)
subtype3	14	56.8 (15.4)

Figure S36. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.918 (Fisher's exact test)

Table S43. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	13	30
subtype1	4	10
subtype2	4	11
subtype3	5	9

Figure S37. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0389 (Chi-square test)

Table S44. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	19	7	17
subtype1	4	1	9
subtype2	8	5	2
subtype3	7	1	6

Figure S38. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0601 (Fisher's exact test)

Table S45. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1
ALL	7	9
subtype1	6	3
subtype2	0	0
subtype3	1	6

Figure S39. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0161 (Fisher's exact test)

Table S46. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	30	5
subtype1	13	0
subtype2	8	0
subtype3	9	5

Figure S40. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.00425 (Chi-square test)

Table S47. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	16	2	12	5
subtype1	3	1	9	0
subtype2	6	0	2	0
subtype3	7	1	1	5

Figure S41. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S48. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	13	15	15

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0135 (logrank test)

Table S49. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	43	8	0.5 - 86.7 (11.6)
subtype1	13	4	2.8 - 40.8 (11.6)
subtype2	15	3	0.9 - 86.7 (21.6)
subtype3	15	1	0.5 - 63.7 (5.9)

Figure S42. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.789 (ANOVA)

Table S50. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	43	60.0 (12.3)
subtype1	13	58.0 (15.3)
subtype2	15	60.9 (10.7)
subtype3	15	60.8 (11.5)

Figure S43. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.597 (Fisher's exact test)

Table S51. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	13	30
subtype1	5	8
subtype2	3	12
subtype3	5	10

Figure S44. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0252 (Chi-square test)

Table S52. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	19	7	17
subtype1	7	1	5
subtype2	4	1	10
subtype3	8	5	2

Figure S45. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.119 (Fisher's exact test)

Table S53. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1
ALL	7	9
subtype1	1	5
subtype2	6	3
subtype3	0	1

Figure S46. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.143 (Fisher's exact test)

Table S54. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	30	5
subtype1	9	4
subtype2	13	1
subtype3	8	0

Figure S47. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0222 (Chi-square test)

Table S55. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	16	2	12	5
subtype1	7	1	1	4
subtype2	3	1	9	1
subtype3	6	0	2	0

Figure S48. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Methods & Data

Input

Cluster data file = KIRP.mergedcluster.txt
Clinical data file = KIRP.clin.merged.picked.txt
Number of patients = 95
Number of clustering approaches = 7
Number of selected clinical features = 8
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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

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

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[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)

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

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