Correlation between aggregated molecular cancer subtypes and selected clinical features

Kidney Chromophobe (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1TD9W35

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 11 clinical features across 66 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

3 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 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 7 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
7 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
7 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 11 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	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)
AGE	Kruskal-Wallis (anova)	0.828 (1.00)	0.499 (1.00)	0.345 (1.00)	0.566 (1.00)	0.00741 (0.622)	0.355 (1.00)	0.353 (1.00)	0.27 (1.00)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.587 (1.00)	0.0312 (1.00)	0.087 (1.00)	0.118 (1.00)	0.132 (1.00)	0.619 (1.00)	0.0523 (1.00)	0.032 (1.00)
PATHOLOGY T STAGE	Fisher's exact test	0.498 (1.00)	0.0172 (1.00)	0.0499 (1.00)	0.0787 (1.00)	0.129 (1.00)	0.643 (1.00)	0.0417 (1.00)	0.0524 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	0.565 (1.00)	0.383 (1.00)	0.114 (1.00)	0.165 (1.00)	0.722 (1.00)	0.263 (1.00)	0.656 (1.00)	0.287 (1.00)
PATHOLOGY M STAGE	Fisher's exact test	0.924 (1.00)	0.251 (1.00)	0.151 (1.00)	0.0698 (1.00)	0.156 (1.00)	0.0286 (1.00)	0.257 (1.00)	0.206 (1.00)
GENDER	Fisher's exact test	0.655 (1.00)	0.754 (1.00)	0.366 (1.00)	0.104 (1.00)	0.495 (1.00)	0.583 (1.00)	0.226 (1.00)	0.515 (1.00)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)	0.12 (1.00)	0.615 (1.00)	0.244 (1.00)	0.164 (1.00)	0.0528 (1.00)	0.164 (1.00)	0.097 (1.00)	0.116 (1.00)
NUMBERPACKYEARSSMOKED	Kruskal-Wallis (anova)		0.3 (1.00)	0.118 (1.00)	0.118 (1.00)	0.268 (1.00)
RACE	Fisher's exact test	0.122 (1.00)	0.805 (1.00)	0.458 (1.00)	0.502 (1.00)	0.105 (1.00)	0.232 (1.00)	0.648 (1.00)	0.162 (1.00)
ETHNICITY	Fisher's exact test	0.712 (1.00)	0.667 (1.00)	0.141 (1.00)	0.0926 (1.00)	0.665 (1.00)	0.271 (1.00)	0.147 (1.00)	0.741 (1.00)

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

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

Cluster Labels	1	2	3
Number of samples	5	47	14

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

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Year
ALL	59	2	18.0 - 4622.0 (1986.0)
subtype1	4	0	1125.0 - 2820.0 (2059.0)
subtype2	41	2	18.0 - 4622.0 (1986.0)
subtype3	14	0	76.0 - 4169.0 (2078.0)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.828 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	66	51.5 (14.3)
subtype1	5	51.6 (11.7)
subtype2	47	52.1 (14.7)
subtype3	14	49.6 (14.5)

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

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

P value = 0.587 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	25	14	6
subtype1	1	2	2	0
subtype2	13	18	10	6
subtype3	7	5	2	0

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

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

P value = 0.498 (Fisher's exact test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	25	20
subtype1	1	2	2
subtype2	13	18	16
subtype3	7	5	2

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.565 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1+N2
ALL	40	5
subtype1	2	0
subtype2	30	5
subtype3	8	0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.924 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	MX
ALL	34	2	9
subtype1	3	0	1
subtype2	23	2	7
subtype3	8	0	1

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.655 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	27	39
subtype1	3	2
subtype2	18	29
subtype3	6	8

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

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

P value = 0.12 (Kruskal-Wallis (anova)), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	1	90.0 (NA)
subtype2	7	85.7 (9.8)
subtype3	3	96.7 (5.8)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.122 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	58
subtype1	1	1	3
subtype2	1	2	42
subtype3	0	1	13

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.712 (Fisher's exact test), Q value = 1

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	32
subtype1	0	3
subtype2	2	20
subtype3	2	9

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S12. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	18	35	13

'METHLYATION CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	59	2	18.0 - 4622.0 (1986.0)
subtype1	16	0	714.0 - 4169.0 (2104.0)
subtype2	31	2	76.0 - 4622.0 (1986.0)
subtype3	12	0	18.0 - 2991.0 (1684.0)

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.499 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	66	51.5 (14.3)
subtype1	18	49.7 (13.7)
subtype2	35	50.9 (14.0)
subtype3	13	55.7 (16.3)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0312 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	25	14	6
subtype1	8	7	3	0
subtype2	6	17	8	4
subtype3	7	1	3	2

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

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0172 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	25	20
subtype1	8	7	3
subtype2	6	17	12
subtype3	7	1	5

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.383 (Fisher's exact test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	12	0
subtype2	23	4
subtype3	5	1

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

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

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	34	2	9
subtype1	12	0	2
subtype2	17	2	3
subtype3	5	0	4

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.754 (Fisher's exact test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	39
subtype1	8	10
subtype2	15	20
subtype3	4	9

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.615 (Kruskal-Wallis (anova)), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	3	93.3 (5.8)
subtype2	3	90.0 (10.0)
subtype3	5	86.0 (11.4)

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.3 (Kruskal-Wallis (anova)), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	11	25.1 (21.9)
subtype1	3	12.0 (13.1)
subtype2	6	32.7 (26.8)
subtype3	2	22.0 (2.8)

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'RACE'

P value = 0.805 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	58
subtype1	0	1	17
subtype2	2	3	28
subtype3	0	0	13

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.667 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	32
subtype1	1	11
subtype2	1	12
subtype3	2	9

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S24. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	19	22	15	10

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	59	2	18.0 - 4622.0 (1986.0)
subtype1	18	1	1125.0 - 4169.0 (2371.5)
subtype2	20	0	76.0 - 4622.0 (1937.0)
subtype3	12	1	637.0 - 3474.0 (2044.0)
subtype4	9	0	18.0 - 2991.0 (953.0)

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.345 (Kruskal-Wallis (anova)), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	51.5 (14.3)
subtype1	19	47.7 (12.9)
subtype2	22	51.8 (15.1)
subtype3	15	51.6 (14.3)
subtype4	10	58.0 (14.6)

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.087 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	25	14	6
subtype1	7	6	5	1
subtype2	8	11	2	1
subtype3	1	7	5	2
subtype4	5	1	2	2

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0499 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	25	20
subtype1	7	6	6
subtype2	8	11	3
subtype3	1	7	7
subtype4	5	1	4

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.114 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	12	1
subtype2	15	0
subtype3	10	3
subtype4	3	1

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.151 (Fisher's exact test), Q value = 1

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	34	2	9
subtype1	13	0	2
subtype2	11	1	2
subtype3	7	1	1
subtype4	3	0	4

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.366 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	39
subtype1	9	10
subtype2	11	11
subtype3	5	10
subtype4	2	8

Figure S28. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.244 (Kruskal-Wallis (anova)), Q value = 1

Table S32. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	3	93.3 (5.8)
subtype2	3	93.3 (11.5)
subtype3	1	90.0 (NA)
subtype4	4	82.5 (9.6)

Figure S29. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.118 (Kruskal-Wallis (anova)), Q value = 1

Table S33. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	11	25.1 (21.9)
subtype1	3	14.3 (11.2)
subtype2	4	15.8 (16.5)
subtype3	3	50.0 (24.1)
subtype4	1	20.0 (NA)

Figure S30. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.458 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	58
subtype1	1	0	18
subtype2	0	2	18
subtype3	1	2	12
subtype4	0	0	10

Figure S31. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.141 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	32
subtype1	0	10
subtype2	4	10
subtype3	0	5
subtype4	0	7

Figure S32. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S36. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	19	16	7	6	10	4	4

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	59	2	18.0 - 4622.0 (1986.0)
subtype1	18	1	1125.0 - 4169.0 (2293.5)
subtype2	15	0	76.0 - 4622.0 (1977.0)
subtype3	5	0	18.0 - 2582.0 (1897.0)
subtype4	6	0	714.0 - 3322.0 (1684.0)
subtype5	7	1	1366.0 - 3474.0 (2064.0)
subtype6	4	0	637.0 - 2674.0 (1998.5)
subtype7	4	0	400.0 - 2304.0 (830.5)

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.566 (Kruskal-Wallis (anova)), Q value = 1

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	51.5 (14.3)
subtype1	19	46.8 (13.3)
subtype2	16	53.5 (16.0)
subtype3	7	53.1 (15.9)
subtype4	6	60.0 (11.8)
subtype5	10	53.6 (12.7)
subtype6	4	46.5 (15.1)
subtype7	4	50.0 (17.1)

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.118 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	25	14	6
subtype1	7	7	4	1
subtype2	7	7	1	1
subtype3	1	3	1	2
subtype4	1	2	3	0
subtype5	1	3	4	2
subtype6	0	3	1	0
subtype7	4	0	0	0

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0787 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	25	20
subtype1	7	7	5
subtype2	7	7	2
subtype3	1	3	3
subtype4	1	2	3
subtype5	1	3	6
subtype6	0	3	1
subtype7	4	0	0

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.165 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	12	1
subtype2	11	0
subtype3	3	1
subtype4	5	0
subtype5	6	3
subtype6	3	0
subtype7	0	0

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0698 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	34	2	9
subtype1	13	0	2
subtype2	7	1	1
subtype3	4	0	1
subtype4	4	0	1
subtype5	4	1	1
subtype6	2	0	0
subtype7	0	0	3

Figure S38. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.104 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	39
subtype1	9	10
subtype2	10	6
subtype3	1	6
subtype4	0	6
subtype5	4	6
subtype6	1	3
subtype7	2	2

Figure S39. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.164 (Kruskal-Wallis (anova)), Q value = 1

Table S44. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	3	93.3 (5.8)
subtype2	2	90.0 (14.1)
subtype3	1	100.0 (NA)
subtype4	1	90.0 (NA)
subtype5	1	90.0 (NA)
subtype7	3	80.0 (10.0)

Figure S40. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.118 (Kruskal-Wallis (anova)), Q value = 1

Table S45. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	11	25.1 (21.9)
subtype1	3	14.3 (11.2)
subtype2	4	15.8 (16.5)
subtype5	3	50.0 (24.1)
subtype7	1	20.0 (NA)

Figure S41. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.502 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	58
subtype1	1	0	18
subtype2	0	1	14
subtype3	0	0	6
subtype4	0	1	5
subtype5	1	1	8
subtype6	0	1	3
subtype7	0	0	4

Figure S42. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0926 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	32
subtype1	0	10
subtype2	2	9
subtype3	2	1
subtype4	0	3
subtype5	0	3
subtype6	0	2
subtype7	0	4

Figure S43. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S48. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	20	24	22

'MIRSEQ CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	59	2	18.0 - 4622.0 (1986.0)
subtype1	15	0	18.0 - 4622.0 (1815.0)
subtype2	23	1	108.0 - 4311.0 (2670.0)
subtype3	21	1	341.0 - 3474.0 (1971.0)

Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00741 (Kruskal-Wallis (anova)), Q value = 0.62

Table S50. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	51.5 (14.3)
subtype1	20	58.2 (15.9)
subtype2	24	52.2 (13.0)
subtype3	22	44.6 (11.1)

Figure S45. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.132 (Fisher's exact test), Q value = 1

Table S51. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	25	14	6
subtype1	6	4	7	3
subtype2	9	9	3	3
subtype3	6	12	4	0

Figure S46. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.129 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	25	20
subtype1	6	4	10
subtype2	9	9	6
subtype3	6	12	4

Figure S47. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.722 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	10	2
subtype2	14	2
subtype3	16	1

Figure S48. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.156 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	34	2	9
subtype1	8	1	5
subtype2	15	1	1
subtype3	11	0	3

Figure S49. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.495 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	39
subtype1	6	14
subtype2	11	13
subtype3	10	12

Figure S50. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0528 (Kruskal-Wallis (anova)), Q value = 1

Table S56. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	5	82.0 (8.4)
subtype2	2	95.0 (7.1)
subtype3	4	95.0 (5.8)

Figure S51. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.268 (Kruskal-Wallis (anova)), Q value = 1

Table S57. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	11	25.1 (21.9)
subtype1	3	31.7 (14.6)
subtype2	4	11.0 (11.3)
subtype3	4	34.2 (30.2)

Figure S52. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.105 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	58
subtype1	0	0	20
subtype2	2	1	21
subtype3	0	3	17

Figure S53. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.665 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	32
subtype1	1	11
subtype2	1	12
subtype3	2	9

Figure S54. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S60. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	18	11	12	6	5	5	9

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S61. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	59	2	18.0 - 4622.0 (1986.0)
subtype1	16	0	1125.0 - 3745.0 (2518.5)
subtype2	11	1	76.0 - 4311.0 (1986.0)
subtype3	11	0	1364.0 - 2619.0 (1997.0)
subtype4	6	0	712.0 - 3322.0 (1164.0)
subtype5	4	0	341.0 - 2991.0 (1858.0)
subtype6	4	1	1945.0 - 4622.0 (2722.5)
subtype7	7	0	18.0 - 2304.0 (876.0)

Figure S55. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.355 (Kruskal-Wallis (anova)), Q value = 1

Table S62. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	51.5 (14.3)
subtype1	18	49.2 (12.4)
subtype2	11	58.5 (14.3)
subtype3	12	47.8 (12.6)
subtype4	6	47.3 (15.0)
subtype5	5	51.4 (19.9)
subtype6	5	46.0 (13.5)
subtype7	9	58.4 (15.6)

Figure S56. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.619 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	25	14	6
subtype1	7	5	5	1
subtype2	3	5	1	2
subtype3	2	8	2	0
subtype4	3	2	1	0
subtype5	1	2	2	0
subtype6	1	2	1	1
subtype7	4	1	2	2

Figure S57. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.643 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	25	20
subtype1	7	5	6
subtype2	3	5	3
subtype3	2	8	2
subtype4	3	2	1
subtype5	1	2	2
subtype6	1	2	2
subtype7	4	1	4

Figure S58. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.263 (Fisher's exact test), Q value = 1

Table S65. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	11	1
subtype2	6	1
subtype3	10	0
subtype4	4	0
subtype5	3	0
subtype6	3	2
subtype7	3	1

Figure S59. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.0286 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	34	2	9
subtype1	13	0	1
subtype2	6	1	0
subtype3	9	0	2
subtype4	1	0	1
subtype5	2	0	1
subtype6	1	1	1
subtype7	2	0	3

Figure S60. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.583 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	39
subtype1	10	8
subtype2	5	6
subtype3	4	8
subtype4	2	4
subtype5	1	4
subtype6	3	2
subtype7	2	7

Figure S61. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.164 (Kruskal-Wallis (anova)), Q value = 1

Table S68. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	2	95.0 (7.1)
subtype3	3	93.3 (5.8)
subtype4	1	90.0 (NA)
subtype5	1	100.0 (NA)
subtype6	1	80.0 (NA)
subtype7	3	80.0 (10.0)

Figure S62. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.232 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	58
subtype1	1	0	17
subtype2	1	0	10
subtype3	0	3	9
subtype4	0	1	4
subtype5	0	0	4
subtype6	0	0	5
subtype7	0	0	9

Figure S63. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.271 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	32
subtype1	1	8
subtype2	0	5
subtype3	1	5
subtype4	0	2
subtype5	2	2
subtype6	0	3
subtype7	0	7

Figure S64. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S71. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	10	16	16	10	14

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S72. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	59	2	18.0 - 4622.0 (1986.0)
subtype1	8	0	108.0 - 2949.0 (2161.0)
subtype2	16	0	76.0 - 4311.0 (1684.0)
subtype3	14	1	1364.0 - 4169.0 (2565.5)
subtype4	10	0	341.0 - 2619.0 (2010.5)
subtype5	11	1	18.0 - 4622.0 (1971.0)

Figure S65. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.353 (Kruskal-Wallis (anova)), Q value = 1

Table S73. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	51.5 (14.3)
subtype1	10	50.2 (10.7)
subtype2	16	52.1 (15.9)
subtype3	16	49.1 (15.7)
subtype4	10	47.3 (12.1)
subtype5	14	57.6 (14.3)

Figure S66. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0523 (Fisher's exact test), Q value = 1

Table S74. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	25	14	6
subtype1	5	0	3	2
subtype2	6	8	2	0
subtype3	4	9	1	2
subtype4	2	5	3	0
subtype5	4	3	5	2

Figure S67. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0417 (Fisher's exact test), Q value = 1

Table S75. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	25	20
subtype1	5	0	5
subtype2	6	8	2
subtype3	4	9	3
subtype4	2	5	3
subtype5	4	3	7

Figure S68. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.656 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	6	1
subtype2	9	0
subtype3	11	2
subtype4	6	0
subtype5	8	2

Figure S69. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.257 (Fisher's exact test), Q value = 1

Table S77. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	34	2	9
subtype1	7	1	0
subtype2	5	0	3
subtype3	12	1	1
subtype4	5	0	2
subtype5	5	0	3

Figure S70. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.226 (Fisher's exact test), Q value = 1

Table S78. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	39
subtype1	7	3
subtype2	7	9
subtype3	6	10
subtype4	4	6
subtype5	3	11

Figure S71. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.097 (Kruskal-Wallis (anova)), Q value = 1

Table S79. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype2	3	86.7 (5.8)
subtype3	2	95.0 (7.1)
subtype4	3	96.7 (5.8)
subtype5	3	80.0 (10.0)

Figure S72. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.648 (Fisher's exact test), Q value = 1

Table S80. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	58
subtype1	1	0	9
subtype2	0	2	13
subtype3	1	1	14
subtype4	0	1	8
subtype5	0	0	14

Figure S73. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.147 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	32
subtype1	1	3
subtype2	0	11
subtype3	0	6
subtype4	2	4
subtype5	1	8

Figure S74. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S82. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	14	8	13	5	9	9	8

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S83. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Year
ALL	59	2	18.0 - 4622.0 (1986.0)
subtype1	12	0	1125.0 - 3010.0 (2293.5)
subtype2	8	0	76.0 - 4311.0 (1729.0)
subtype3	12	0	1364.0 - 3474.0 (2010.5)
subtype4	5	0	714.0 - 3322.0 (1553.0)
subtype5	8	1	1665.0 - 4169.0 (2565.5)
subtype6	7	1	341.0 - 4622.0 (1971.0)
subtype7	7	0	18.0 - 2304.0 (876.0)

Figure S75. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.27 (Kruskal-Wallis (anova)), Q value = 1

Table S84. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	51.5 (14.3)
subtype1	14	49.0 (7.9)
subtype2	8	61.2 (14.3)
subtype3	13	45.8 (12.1)
subtype4	5	49.8 (15.3)
subtype5	9	48.9 (18.2)
subtype6	9	53.8 (17.0)
subtype7	8	56.9 (16.0)

Figure S76. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

Table S85. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	25	14	6
subtype1	4	5	4	1
subtype2	2	5	0	1
subtype3	2	9	2	0
subtype4	3	1	1	0
subtype5	5	1	1	2
subtype6	1	4	4	0
subtype7	4	0	2	2

Figure S77. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0524 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	25	20
subtype1	4	5	5
subtype2	2	5	1
subtype3	2	9	2
subtype4	3	1	1
subtype5	5	1	3
subtype6	1	4	4
subtype7	4	0	4

Figure S78. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.287 (Fisher's exact test), Q value = 1

Table S87. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	10	1
subtype2	4	0
subtype3	10	0
subtype4	4	0
subtype5	4	2
subtype6	6	1
subtype7	2	1

Figure S79. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.206 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	34	2	9
subtype1	10	0	1
subtype2	3	1	0
subtype3	9	0	2
subtype4	1	0	1
subtype5	6	1	1
subtype6	3	0	1
subtype7	2	0	3

Figure S80. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.515 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	39
subtype1	9	5
subtype2	4	4
subtype3	5	8
subtype4	1	4
subtype5	3	6
subtype6	3	6
subtype7	2	6

Figure S81. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.116 (Kruskal-Wallis (anova)), Q value = 1

Table S90. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	1	90.0 (NA)
subtype3	3	96.7 (5.8)
subtype4	1	90.0 (NA)
subtype5	2	95.0 (7.1)
subtype6	1	80.0 (NA)
subtype7	3	80.0 (10.0)

Figure S82. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.162 (Fisher's exact test), Q value = 1

Table S91. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	58
subtype1	1	0	13
subtype2	0	0	8
subtype3	0	3	9
subtype4	0	1	4
subtype5	1	0	8
subtype6	0	0	8
subtype7	0	0	8

Figure S83. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.741 (Fisher's exact test), Q value = 1

Table S92. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	32
subtype1	1	5
subtype2	0	5
subtype3	2	4
subtype4	0	2
subtype5	0	5
subtype6	1	5
subtype7	0	6

Figure S84. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Methods & Data

Input

Cluster data file = KICH-TP.mergedcluster.txt
Clinical data file = KICH-TP.merged_data.txt
Number of patients = 66
Number of clustering approaches = 8
Number of selected clinical features = 11
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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] 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)

[5] 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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