Correlation between molecular cancer subtypes and selected clinical features

Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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): Kidney Renal Papillary Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1NC5Z5G

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 8 clinical features across 104 patients, 9 significant findings detected with P value < 0.05 and Q value < 0.25.

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.
5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'PATHOLOGY.T' and 'TUMOR.STAGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 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 'GENDER', 'PATHOLOGY.T', and 'TUMOR.STAGE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'GENDER'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY.T'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 9 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	TUMOR STAGE
Statistical Tests	logrank test	ANOVA	Fisher's exact test	ANOVA	Chi-square test	Chi-square test	Chi-square test	Chi-square test
mRNA CNMF subtypes	100 (1.00)	0.182 (1.00)	0.585 (1.00)		0.0623 (1.00)		1 (1.00)	0.292 (1.00)
mRNA cHierClus subtypes	100 (1.00)	0.948 (1.00)	1 (1.00)		0.216 (1.00)		1 (1.00)
Copy Number Ratio CNMF subtypes	0.0309 (1.00)	0.399 (1.00)	0.0797 (1.00)	0.787 (1.00)	0.0702 (1.00)	0.848 (1.00)	0.632 (1.00)	0.231 (1.00)
METHLYATION CNMF	0.147 (1.00)	0.0601 (1.00)	0.219 (1.00)	0.192 (1.00)	1.85e-08 (1.09e-06)	0.115 (1.00)	0.099 (1.00)	2.46e-06 (0.000143)
RNAseq CNMF subtypes	0.0331 (1.00)	0.00566 (0.277)	0.0139 (0.614)	0.433 (1.00)	6.14e-05 (0.00344)	0.55 (1.00)	0.0127 (0.569)	7.89e-05 (0.00434)
RNAseq cHierClus subtypes	0.0119 (0.545)	0.495 (1.00)	0.000483 (0.0256)	0.578 (1.00)	0.000174 (0.00939)	0.072 (1.00)	0.00815 (0.391)	1.4e-05 (8e-04)
MIRSEQ CNMF	0.987 (1.00)	0.111 (1.00)	0.00489 (0.249)	0.488 (1.00)	0.00975 (0.458)	0.133 (1.00)	0.284 (1.00)	0.018 (0.774)
MIRSEQ CHIERARCHICAL	0.354 (1.00)	0.541 (1.00)	0.0627 (1.00)	0.395 (1.00)	0.000669 (0.0348)	0.29 (1.00)	0.408 (1.00)	0.00519 (0.259)

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 = 100 (logrank test), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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 'TUMOR.STAGE'

P value = 0.292 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'TUMOR.STAGE'

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

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'TUMOR.STAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S7. 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 = 100 (logrank test), Q value = 1

Table S8. 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 S6. 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), Q value = 1

Table S9. 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 S7. 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), Q value = 1

Table S10. 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 S8. 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), Q value = 1

Table S11. 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 S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

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

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	5	2	1	1
subtype1	2	0	0	0
subtype2	1	2	1	1
subtype3	2	0	0	0

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S13. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	20	38	9	20	17

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

P value = 0.0309 (logrank test), Q value = 1

Table S14. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	97	14	0.0 - 182.7 (13.7)
subtype1	20	4	0.0 - 80.8 (14.1)
subtype2	34	3	0.2 - 129.9 (21.0)
subtype3	9	1	0.0 - 50.5 (15.5)
subtype4	19	4	0.5 - 25.4 (11.1)
subtype5	15	2	0.6 - 182.7 (32.1)

Figure S10. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.399 (ANOVA), Q value = 1

Table S15. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	101	59.6 (12.4)
subtype1	20	58.5 (16.0)
subtype2	38	60.7 (12.9)
subtype3	9	52.8 (7.7)
subtype4	19	59.7 (10.9)
subtype5	15	62.7 (8.9)

Figure S11. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0797 (Chi-square test), Q value = 1

Table S16. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	70
subtype1	8	12
subtype2	17	21
subtype3	1	8
subtype4	6	14
subtype5	2	15

Figure S12. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.787 (ANOVA), Q value = 1

Table S17. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	22	87.7 (23.3)
subtype1	4	92.5 (5.0)
subtype2	7	87.1 (21.4)
subtype3	3	93.3 (5.8)
subtype4	6	78.3 (38.7)
subtype5	2	100.0 (0.0)

Figure S13. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0702 (Chi-square test), Q value = 1

Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	58	13	33
subtype1	7	2	11
subtype2	26	4	8
subtype3	6	2	1
subtype4	8	2	10
subtype5	11	3	3

Figure S14. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.848 (Chi-square test), Q value = 1

Table S19. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	20	12	4
subtype1	5	6	1
subtype2	5	2	1
subtype3	1	0	0
subtype4	7	3	2
subtype5	2	1	0

Figure S15. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.632 (Chi-square test), Q value = 1

Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	54	5	36
subtype1	9	2	7
subtype2	21	1	14
subtype3	3	0	4
subtype4	10	2	7
subtype5	11	0	4

Figure S16. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.231 (Chi-square test), Q value = 1

Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	53	7	24	9
subtype1	7	1	7	3
subtype2	23	4	7	1
subtype3	5	0	1	0
subtype4	7	1	7	4
subtype5	11	1	2	1

Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S22. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	21	40	27

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.147 (logrank test), Q value = 1

Table S23. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	81	12	0.0 - 182.7 (14.6)
subtype1	19	4	0.0 - 80.8 (26.0)
subtype2	36	2	0.0 - 129.9 (14.4)
subtype3	26	6	0.2 - 182.7 (13.3)

Figure S18. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

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

Table S24. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	85	60.0 (12.6)
subtype1	20	65.7 (10.9)
subtype2	39	57.6 (10.0)
subtype3	26	59.2 (15.9)

Figure S19. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.219 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	30	58
subtype1	8	13
subtype2	10	30
subtype3	12	15

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

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

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

Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	21	91.9 (12.9)
subtype1	5	80.0 (22.4)
subtype2	14	95.7 (5.1)
subtype3	2	95.0 (7.1)

Figure S21. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 1.85e-08 (Chi-square test), Q value = 1.1e-06

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

nPatients	T1	T2	T3+T4
ALL	51	6	31
subtype1	15	0	6
subtype2	32	5	3
subtype3	4	1	22

Figure S22. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.115 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	20	10	4
subtype1	3	2	1
subtype2	8	0	0
subtype3	9	8	3

Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.099 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	45	4	36
subtype1	12	1	7
subtype2	17	0	21
subtype3	16	3	8

Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 2.46e-06 (Chi-square test), Q value = 0.00014

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

nPatients	I	II	III	IV
ALL	48	5	23	8
subtype1	14	1	3	2
subtype2	30	3	4	0
subtype3	4	1	16	6

Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	26	24	25

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0331 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	72	13	0.5 - 182.7 (15.1)
subtype1	24	3	0.5 - 54.9 (13.1)
subtype2	23	8	0.9 - 93.3 (13.2)
subtype3	25	2	6.4 - 182.7 (26.0)

Figure S26. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00566 (ANOVA), Q value = 0.28

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

	nPatients	Mean (Std.Dev)
ALL	72	59.7 (13.1)
subtype1	24	56.5 (12.1)
subtype2	23	55.8 (14.2)
subtype3	25	66.3 (10.5)

Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0139 (Fisher's exact test), Q value = 0.61

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

nPatients	FEMALE	MALE
ALL	23	52
subtype1	5	21
subtype2	13	11
subtype3	5	20

Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.433 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	12	86.7 (27.7)
subtype1	7	94.3 (5.3)
subtype2	3	63.3 (55.1)
subtype3	2	95.0 (7.1)

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 6.14e-05 (Chi-square test), Q value = 0.0034

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

nPatients	T1	T2	T3+T4
ALL	38	9	28
subtype1	20	5	1
subtype2	5	2	17
subtype3	13	2	10

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.55 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	15	12	3
subtype1	2	0	0
subtype2	6	8	2
subtype3	7	4	1

Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0127 (Chi-square test), Q value = 0.57

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

nPatients	M0	M1	MX
ALL	46	5	17
subtype1	14	0	7
subtype2	12	5	6
subtype3	20	0	4

Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 7.89e-05 (Chi-square test), Q value = 0.0043

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

nPatients	I	II	III	IV
ALL	35	3	21	8
subtype1	19	0	2	0
subtype2	4	1	10	7
subtype3	12	2	9	1

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S40. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	18	36	21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0119 (logrank test), Q value = 0.55

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	72	13	0.5 - 182.7 (15.1)
subtype1	17	6	2.8 - 80.8 (10.8)
subtype2	34	3	0.5 - 182.7 (13.8)
subtype3	21	4	0.9 - 123.6 (25.1)

Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.495 (ANOVA), Q value = 1

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	72	59.7 (13.1)
subtype1	17	57.3 (15.0)
subtype2	34	59.3 (11.7)
subtype3	21	62.3 (13.7)

Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.000483 (Fisher's exact test), Q value = 0.026

Table S43. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	23	52
subtype1	11	7
subtype2	4	32
subtype3	8	13

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.578 (ANOVA), Q value = 1

Table S44. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	12	86.7 (27.7)
subtype1	3	93.3 (5.8)
subtype2	7	95.7 (5.3)
subtype3	2	45.0 (63.6)

Figure S37. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.000174 (Chi-square test), Q value = 0.0094

Table S45. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	38	9	28
subtype1	7	2	9
subtype2	26	6	4
subtype3	5	1	15

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.072 (Chi-square test), Q value = 1

Table S46. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	15	12	3
subtype1	1	6	2
subtype2	4	1	0
subtype3	10	5	1

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

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

P value = 0.00815 (Chi-square test), Q value = 0.39

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	46	5	17
subtype1	9	4	4
subtype2	19	0	11
subtype3	18	1	2

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

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 1.4e-05 (Chi-square test), Q value = 8e-04

Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	35	3	21	8
subtype1	6	1	3	6
subtype2	24	1	4	1
subtype3	5	1	14	1

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

Clustering Approach #7: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3	4
Number of samples	24	34	29	17

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.987 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	97	14	0.0 - 182.7 (13.7)
subtype1	24	4	0.0 - 63.7 (18.1)
subtype2	32	5	0.2 - 182.7 (17.4)
subtype3	24	3	0.0 - 123.6 (13.0)
subtype4	17	2	0.5 - 86.7 (13.7)

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

'MIRSEQ CNMF' versus 'AGE'

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

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

	nPatients	Mean (Std.Dev)
ALL	101	59.6 (12.4)
subtype1	24	62.1 (12.6)
subtype2	34	56.2 (13.7)
subtype3	26	63.1 (10.1)
subtype4	17	57.8 (11.2)

Figure S43. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00489 (Fisher's exact test), Q value = 0.25

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

nPatients	FEMALE	MALE
ALL	34	70
subtype1	3	21
subtype2	16	18
subtype3	6	23
subtype4	9	8

Figure S44. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.488 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	22	87.7 (23.3)
subtype1	5	94.0 (5.5)
subtype2	11	80.9 (31.8)
subtype3	4	95.0 (5.8)
subtype4	2	95.0 (7.1)

Figure S45. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.00975 (Chi-square test), Q value = 0.46

Table S54. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	58	13	33
subtype1	13	2	9
subtype2	19	2	13
subtype3	22	4	3
subtype4	4	5	8

Figure S46. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.133 (Chi-square test), Q value = 1

Table S55. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	20	12	4
subtype1	7	2	1
subtype2	5	6	3
subtype3	5	0	0
subtype4	3	4	0

Figure S47. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.284 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	54	5	36
subtype1	14	0	8
subtype2	17	3	14
subtype3	14	0	11
subtype4	9	2	3

Figure S48. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.018 (Chi-square test), Q value = 0.77

Table S57. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	53	7	24	9
subtype1	12	2	8	0
subtype2	18	3	6	7
subtype3	20	1	4	0
subtype4	3	1	6	2

Figure S49. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S58. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	25	40	11	28

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.354 (logrank test), Q value = 1

Table S59. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	97	14	0.0 - 182.7 (13.7)
subtype1	25	5	0.9 - 86.7 (14.6)
subtype2	36	7	0.2 - 182.7 (17.8)
subtype3	10	0	3.8 - 96.9 (22.6)
subtype4	26	2	0.0 - 123.6 (6.6)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.541 (ANOVA), Q value = 1

Table S60. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	101	59.6 (12.4)
subtype1	25	62.7 (13.0)
subtype2	39	58.7 (13.8)
subtype3	10	57.1 (12.0)
subtype4	27	59.1 (9.6)

Figure S51. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0627 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	70
subtype1	5	20
subtype2	18	22
subtype3	1	10
subtype4	10	18

Figure S52. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

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

P value = 0.395 (ANOVA), Q value = 1

Table S62. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	22	87.7 (23.3)
subtype1	5	74.0 (41.6)
subtype2	11	89.1 (17.0)
subtype3	2	100.0 (0.0)
subtype4	4	95.0 (5.8)

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.000669 (Chi-square test), Q value = 0.035

Table S63. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3+T4
ALL	58	13	33
subtype1	9	2	14
subtype2	22	3	15
subtype3	10	0	1
subtype4	17	8	3

Figure S54. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.29 (Chi-square test), Q value = 1

Table S64. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	20	12	4
subtype1	8	5	1
subtype2	7	7	3
subtype3	1	0	0
subtype4	4	0	0

Figure S55. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.408 (Chi-square test), Q value = 1

Table S65. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	54	5	36
subtype1	17	1	7
subtype2	19	4	17
subtype3	7	0	3
subtype4	11	0	9

Figure S56. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.00519 (Chi-square test), Q value = 0.26

Table S66. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	53	7	24	9
subtype1	9	2	13	1
subtype2	21	3	7	8
subtype3	9	0	1	0
subtype4	14	2	3	0

Figure S57. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

Methods & Data

Input

Cluster data file = KIRP-TP.mergedcluster.txt
Clinical data file = KIRP-TP.clin.merged.picked.txt
Number of patients = 104
Number of clustering approaches = 8
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

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] 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)

[8] 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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