Correlation between aggregated molecular cancer subtypes and selected clinical features

Pheochromocytoma and Paraganglioma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1SJ1K21

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 10 different clustering approaches and 10 clinical features across 179 patients, 21 significant findings detected with P value < 0.05 and Q value < 0.25.

5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'RADIATION_THERAPY'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'TUMOR_TISSUE_SITE' and 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'TUMOR_TISSUE_SITE' and 'HISTOLOGICAL_TYPE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', and 'HISTOLOGICAL_TYPE'.
7 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'GENDER', and 'HISTOLOGICAL_TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 21 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	TUMOR TISSUE SITE	GENDER	RADIATION THERAPY	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	NUMBER OF LYMPH NODES	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.309 (0.739)	0.346 (0.739)	0.128 (0.504)	0.309 (0.739)	0.0267 (0.146)	0.289 (0.739)	0.192 (0.582)	0.846 (0.984)	0.456 (0.817)	0.474 (0.817)
METHLYATION CNMF	0.367 (0.765)	0.189 (0.582)	1e-05 (0.000111)	0.133 (0.504)	0.00082 (0.00586)	0.464 (0.817)	1e-05 (0.000111)	0.891 (1.00)	0.238 (0.643)	1 (1.00)
RPPA CNMF subtypes	100 (1.00)	0.648 (0.879)	0.177 (0.582)	0.905 (1.00)		0.208 (0.593)	0.343 (0.739)	0.693 (0.9)	0.499 (0.818)	0.391 (0.771)
RPPA cHierClus subtypes	100 (1.00)	0.188 (0.582)	0.171 (0.582)	0.136 (0.504)		0.401 (0.771)	0.266 (0.7)		0.833 (0.984)	1 (1.00)
RNAseq CNMF subtypes	0.816 (0.984)	0.39 (0.771)	1e-05 (0.000111)	0.322 (0.739)	0.588 (0.879)	0.42 (0.793)	1e-05 (0.000111)	0.807 (0.984)	0.207 (0.593)	0.0586 (0.266)
RNAseq cHierClus subtypes	0.817 (0.984)	0.00266 (0.0166)	1e-05 (0.000111)	0.497 (0.818)	0.12 (0.504)	0.843 (0.984)	1e-05 (0.000111)	0.632 (0.879)	0.773 (0.967)	0.192 (0.582)
MIRSEQ CNMF	0.127 (0.504)	0.469 (0.817)	4e-05 (0.000364)	0.981 (1.00)	0.0278 (0.146)	0.347 (0.739)	1e-05 (0.000111)	0.92 (1.00)	0.928 (1.00)	0.629 (0.879)
MIRSEQ CHIERARCHICAL	0.635 (0.879)	0.918 (1.00)	2e-05 (2e-04)	0.859 (0.987)	0.625 (0.879)	0.648 (0.879)	0.00012 (0.001)	0.578 (0.879)	0.67 (0.881)	0.331 (0.739)
MIRseq Mature CNMF subtypes	0.659 (0.879)	0.00289 (0.017)	0.00013 (0.001)	0.634 (0.879)	0.438 (0.812)	0.759 (0.961)	0.00197 (0.0131)	0.759 (0.961)	0.656 (0.879)	0.598 (0.879)
MIRseq Mature cHierClus subtypes	0.628 (0.879)	0.035 (0.175)	1e-05 (0.000111)	0.0492 (0.234)	0.483 (0.818)	0.533 (0.859)	1e-05 (0.000111)	0.317 (0.739)	0.234 (0.643)	0.395 (0.771)

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	4	5
Number of samples	37	40	10	50	25

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

P value = 0.309 (logrank test), Q value = 0.74

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	162	6	0.1 - 316.7 (24.9)
subtype1	37	3	1.2 - 316.7 (31.5)
subtype2	40	0	0.8 - 93.1 (24.3)
subtype3	10	1	0.8 - 116.2 (17.6)
subtype4	50	2	0.1 - 137.6 (25.4)
subtype5	25	0	0.7 - 127.6 (19.9)

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 'YEARS_TO_BIRTH'

P value = 0.346 (Kruskal-Wallis (anova)), Q value = 0.74

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

	nPatients	Mean (Std.Dev)
ALL	162	47.5 (15.2)
subtype1	37	48.1 (15.9)
subtype2	40	49.1 (15.1)
subtype3	10	55.6 (16.7)
subtype4	50	45.0 (14.8)
subtype5	25	45.8 (14.4)

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.128 (Fisher's exact test), Q value = 0.5

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

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	134	28
subtype1	33	4
subtype2	28	12
subtype3	10	0
subtype4	42	8
subtype5	21	4

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.309 (Fisher's exact test), Q value = 0.74

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	88	74
subtype1	21	16
subtype2	21	19
subtype3	8	2
subtype4	28	22
subtype5	10	15

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0267 (Fisher's exact test), Q value = 0.15

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

nPatients	NO	YES
ALL	155	5
subtype1	33	4
subtype2	40	0
subtype3	10	0
subtype4	49	0
subtype5	23	1

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.289 (Kruskal-Wallis (anova)), Q value = 0.74

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

	nPatients	Mean (Std.Dev)
ALL	60	96.8 (6.2)
subtype1	18	96.7 (5.9)
subtype2	14	95.0 (9.4)
subtype3	1	90.0 (NA)
subtype4	20	99.0 (3.1)
subtype5	7	95.7 (5.3)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.192 (Fisher's exact test), Q value = 0.58

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

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	16	11	135
subtype1	3	1	33
subtype2	5	7	28
subtype3	0	0	10
subtype4	6	1	43
subtype5	2	2	21

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.846 (Kruskal-Wallis (anova)), Q value = 0.98

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

	nPatients	Mean (Std.Dev)
ALL	18	1.0 (3.0)
subtype1	5	0.6 (0.9)
subtype2	2	0.0 (0.0)
subtype3	2	0.0 (0.0)
subtype4	7	2.0 (4.9)
subtype5	2	0.5 (0.7)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.456 (Fisher's exact test), Q value = 0.82

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	19	134
subtype1	1	2	2	31
subtype2	0	1	8	31
subtype3	0	0	2	8
subtype4	0	2	6	41
subtype5	0	0	1	23

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.474 (Fisher's exact test), Q value = 0.82

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	128
subtype1	0	29
subtype2	0	33
subtype3	0	8
subtype4	1	41
subtype5	1	17

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	45	84	50

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.367 (logrank test), Q value = 0.77

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	6	0.1 - 316.7 (24.8)
subtype1	45	3	0.2 - 316.7 (30.5)
subtype2	84	3	0.1 - 114.8 (23.8)
subtype3	50	0	0.7 - 116.2 (25.9)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.189 (Kruskal-Wallis (anova)), Q value = 0.58

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

	nPatients	Mean (Std.Dev)
ALL	179	47.3 (15.1)
subtype1	45	46.6 (15.3)
subtype2	84	49.5 (14.5)
subtype3	50	44.3 (15.7)

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

'METHLYATION CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00011

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

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	147	32
subtype1	24	21
subtype2	82	2
subtype3	41	9

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.133 (Fisher's exact test), Q value = 0.5

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	78
subtype1	22	23
subtype2	45	39
subtype3	34	16

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.00082 (Fisher's exact test), Q value = 0.0059

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	172	5
subtype1	40	5
subtype2	82	0
subtype3	50	0

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.464 (Kruskal-Wallis (anova)), Q value = 0.82

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	62	96.8 (6.2)
subtype1	11	95.5 (6.9)
subtype2	30	98.0 (4.1)
subtype3	21	95.7 (8.1)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00011

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

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	18	13	148
subtype1	12	8	25
subtype2	2	0	82
subtype3	4	5	41

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

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.891 (Kruskal-Wallis (anova)), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	21	0.9 (2.8)
subtype1	7	2.0 (4.9)
subtype2	9	0.3 (0.7)
subtype3	5	0.2 (0.4)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.238 (Fisher's exact test), Q value = 0.64

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	6	20	148
subtype1	0	1	7	36
subtype2	1	2	5	73
subtype3	0	3	8	39

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

'METHLYATION CNMF' versus 'ETHNICITY'

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

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	138
subtype1	1	35
subtype2	2	61
subtype3	2	42

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

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S23. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	17	21	23	18

'RPPA CNMF subtypes' versus 'Time to Death'

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

Table S24. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	79	2	0.7 - 137.6 (26.9)
subtype1	17	0	0.8 - 78.2 (26.9)
subtype2	21	1	0.9 - 137.6 (25.1)
subtype3	23	0	0.7 - 127.6 (27.2)
subtype4	18	1	2.9 - 108.3 (38.1)

Figure S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.648 (Kruskal-Wallis (anova)), Q value = 0.88

Table S25. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	79	47.8 (14.7)
subtype1	17	49.9 (15.9)
subtype2	21	45.4 (15.9)
subtype3	23	50.3 (14.8)
subtype4	18	45.2 (11.8)

Figure S22. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.177 (Fisher's exact test), Q value = 0.58

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	67	12
subtype1	15	2
subtype2	15	6
subtype3	22	1
subtype4	15	3

Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.905 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	40	39
subtype1	8	9
subtype2	11	10
subtype3	13	10
subtype4	8	10

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.208 (Kruskal-Wallis (anova)), Q value = 0.59

Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	33	98.8 (3.3)
subtype1	6	100.0 (0.0)
subtype2	10	100.0 (0.0)
subtype3	11	97.3 (4.7)
subtype4	6	98.3 (4.1)

Figure S25. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.343 (Fisher's exact test), Q value = 0.74

Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	5	6	68
subtype1	2	0	15
subtype2	2	3	16
subtype3	0	1	22
subtype4	1	2	15

Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.693 (Kruskal-Wallis (anova)), Q value = 0.9

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	10	1.6 (4.0)
subtype1	2	0.0 (0.0)
subtype2	1	1.0 (NA)
subtype3	3	0.7 (0.6)
subtype4	4	3.2 (6.5)

Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.499 (Fisher's exact test), Q value = 0.82

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	9	65
subtype1	2	1	13
subtype2	1	1	19
subtype3	0	4	19
subtype4	1	3	14

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.391 (Fisher's exact test), Q value = 0.77

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	58
subtype1	1	11
subtype2	0	16
subtype3	0	20
subtype4	0	11

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S33. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	33	18	28

'RPPA cHierClus subtypes' versus 'Time to Death'

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

Table S34. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	79	2	0.7 - 137.6 (26.9)
subtype1	33	2	0.9 - 137.6 (30.5)
subtype2	18	0	0.8 - 81.7 (25.9)
subtype3	28	0	0.7 - 72.8 (24.5)

Figure S30. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.188 (Kruskal-Wallis (anova)), Q value = 0.58

Table S35. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	79	47.8 (14.7)
subtype1	33	50.9 (13.6)
subtype2	18	48.2 (12.9)
subtype3	28	43.8 (16.4)

Figure S31. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.171 (Fisher's exact test), Q value = 0.58

Table S36. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	67	12
subtype1	25	8
subtype2	16	2
subtype3	26	2

Figure S32. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.136 (Fisher's exact test), Q value = 0.5

Table S37. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	40	39
subtype1	20	13
subtype2	10	8
subtype3	10	18

Figure S33. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.401 (Kruskal-Wallis (anova)), Q value = 0.77

Table S38. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	33	98.8 (3.3)
subtype1	14	98.6 (3.6)
subtype2	9	100.0 (0.0)
subtype3	10	98.0 (4.2)

Figure S34. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.266 (Fisher's exact test), Q value = 0.7

Table S39. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	5	6	68
subtype1	2	5	26
subtype2	2	0	16
subtype3	1	1	26

Figure S35. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.833 (Fisher's exact test), Q value = 0.98

Table S40. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	9	65
subtype1	1	4	28
subtype2	1	1	16
subtype3	2	4	21

Figure S36. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

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

Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	58
subtype1	1	24
subtype2	0	15
subtype3	0	19

Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S42. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	38	70	50	21

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.816 (logrank test), Q value = 0.98

Table S43. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	6	0.1 - 316.7 (24.8)
subtype1	38	2	0.1 - 316.7 (18.5)
subtype2	70	2	0.1 - 114.8 (24.9)
subtype3	50	2	0.8 - 137.6 (23.2)
subtype4	21	0	0.8 - 111.2 (27.9)

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.39 (Kruskal-Wallis (anova)), Q value = 0.77

Table S44. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	179	47.3 (15.1)
subtype1	38	44.8 (15.3)
subtype2	70	49.5 (14.8)
subtype3	50	45.7 (15.4)
subtype4	21	48.5 (15.1)

Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00011

Table S45. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	147	32
subtype1	19	19
subtype2	67	3
subtype3	40	10
subtype4	21	0

Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.322 (Fisher's exact test), Q value = 0.74

Table S46. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	78
subtype1	25	13
subtype2	35	35
subtype3	27	23
subtype4	14	7

Figure S41. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.588 (Fisher's exact test), Q value = 0.88

Table S47. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	172	5
subtype1	36	2
subtype2	67	1
subtype3	48	2
subtype4	21	0

Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.42 (Kruskal-Wallis (anova)), Q value = 0.79

Table S48. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	62	96.8 (6.2)
subtype1	6	96.7 (8.2)
subtype2	27	97.8 (5.1)
subtype3	17	94.7 (8.0)
subtype4	12	97.5 (4.5)

Figure S43. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00011

Table S49. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	18	13	148
subtype1	11	8	19
subtype2	3	0	67
subtype3	4	5	41
subtype4	0	0	21

Figure S44. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.807 (Kruskal-Wallis (anova)), Q value = 0.98

Table S50. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	21	0.9 (2.8)
subtype1	6	2.2 (5.3)
subtype2	5	0.2 (0.4)
subtype3	9	0.4 (0.7)
subtype4	1	0.0 (NA)

Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.207 (Fisher's exact test), Q value = 0.59

Table S51. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	6	20	148
subtype1	0	1	7	27
subtype2	1	2	3	63
subtype3	0	2	6	42
subtype4	0	1	4	16

Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0586 (Fisher's exact test), Q value = 0.27

Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	138
subtype1	2	26
subtype2	1	56
subtype3	0	40
subtype4	2	16

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S53. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	39	71	48	21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.817 (logrank test), Q value = 0.98

Table S54. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	6	0.1 - 316.7 (24.8)
subtype1	39	2	0.8 - 127.6 (28.1)
subtype2	71	2	0.1 - 114.8 (23.9)
subtype3	48	2	0.2 - 316.7 (19.0)
subtype4	21	0	0.8 - 111.2 (27.9)

Figure S48. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00266 (Kruskal-Wallis (anova)), Q value = 0.017

Table S55. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	179	47.3 (15.1)
subtype1	39	51.9 (12.6)
subtype2	71	49.3 (14.8)
subtype3	48	40.8 (15.6)
subtype4	21	47.1 (15.3)

Figure S49. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00011

Table S56. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	147	32
subtype1	34	5
subtype2	68	3
subtype3	24	24
subtype4	21	0

Figure S50. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.497 (Fisher's exact test), Q value = 0.82

Table S57. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	78
subtype1	23	16
subtype2	41	30
subtype3	23	25
subtype4	14	7

Figure S51. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.12 (Fisher's exact test), Q value = 0.5

Table S58. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	172	5
subtype1	39	0
subtype2	69	1
subtype3	44	4
subtype4	20	0

Figure S52. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.843 (Kruskal-Wallis (anova)), Q value = 0.98

Table S59. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	62	96.8 (6.2)
subtype1	13	96.2 (6.5)
subtype2	25	97.6 (5.2)
subtype3	12	95.8 (9.0)
subtype4	12	96.7 (4.9)

Figure S53. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00011

Table S60. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	18	13	148
subtype1	3	2	34
subtype2	3	0	68
subtype3	12	11	25
subtype4	0	0	21

Figure S54. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.632 (Kruskal-Wallis (anova)), Q value = 0.88

Table S61. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	21	0.9 (2.8)
subtype1	9	1.8 (4.3)
subtype2	5	0.2 (0.4)
subtype3	6	0.2 (0.4)
subtype4	1	0.0 (NA)

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.773 (Fisher's exact test), Q value = 0.97

Table S62. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	6	20	148
subtype1	0	2	4	33
subtype2	1	2	5	60
subtype3	0	1	8	38
subtype4	0	1	3	17

Figure S56. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.192 (Fisher's exact test), Q value = 0.58

Table S63. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	138
subtype1	1	29
subtype2	2	55
subtype3	0	38
subtype4	2	16

Figure S57. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S64. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	72	44	63

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.127 (logrank test), Q value = 0.5

Table S65. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	6	0.1 - 316.7 (24.8)
subtype1	72	5	0.2 - 316.7 (24.5)
subtype2	44	1	0.7 - 137.6 (25.9)
subtype3	63	0	0.1 - 114.8 (24.2)

Figure S58. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.469 (Kruskal-Wallis (anova)), Q value = 0.82

Table S66. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	179	47.3 (15.1)
subtype1	72	45.5 (16.2)
subtype2	44	48.3 (14.1)
subtype3	63	48.7 (14.5)

Figure S59. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00036

Table S67. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	147	32
subtype1	48	24
subtype2	39	5
subtype3	60	3

Figure S60. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.981 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	78
subtype1	40	32
subtype2	25	19
subtype3	36	27

Figure S61. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.0278 (Fisher's exact test), Q value = 0.15

Table S69. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	172	5
subtype1	66	5
subtype2	44	0
subtype3	62	0

Figure S62. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.347 (Kruskal-Wallis (anova)), Q value = 0.74

Table S70. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	62	96.8 (6.2)
subtype1	19	94.7 (8.4)
subtype2	21	97.6 (4.4)
subtype3	22	97.7 (5.3)

Figure S63. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00011

Table S71. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	18	13	148
subtype1	11	13	48
subtype2	4	0	40
subtype3	3	0	60

Figure S64. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.92 (Kruskal-Wallis (anova)), Q value = 1

Table S72. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	21	0.9 (2.8)
subtype1	11	1.4 (3.9)
subtype2	6	0.3 (0.8)
subtype3	4	0.2 (0.5)

Figure S65. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.928 (Fisher's exact test), Q value = 1

Table S73. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	6	20	148
subtype1	1	3	9	58
subtype2	0	1	6	37
subtype3	0	2	5	53

Figure S66. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.629 (Fisher's exact test), Q value = 0.88

Table S74. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	138
subtype1	1	57
subtype2	2	32
subtype3	2	49

Figure S67. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S75. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	77	59	18	25

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.635 (logrank test), Q value = 0.88

Table S76. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	6	0.1 - 316.7 (24.8)
subtype1	77	4	0.2 - 316.7 (20.1)
subtype2	59	2	0.1 - 114.8 (24.8)
subtype3	18	0	0.7 - 86.9 (31.2)
subtype4	25	0	0.8 - 111.2 (23.2)

Figure S68. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.918 (Kruskal-Wallis (anova)), Q value = 1

Table S77. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	179	47.3 (15.1)
subtype1	77	46.4 (15.7)
subtype2	59	48.6 (15.3)
subtype3	18	47.2 (13.0)
subtype4	25	47.5 (14.9)

Figure S69. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_TISSUE_SITE'

P value = 2e-05 (Fisher's exact test), Q value = 2e-04

Table S78. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	147	32
subtype1	50	27
subtype2	56	3
subtype3	18	0
subtype4	23	2

Figure S70. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.859 (Fisher's exact test), Q value = 0.99

Table S79. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	78
subtype1	42	35
subtype2	35	24
subtype3	9	9
subtype4	15	10

Figure S71. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.625 (Fisher's exact test), Q value = 0.88

Table S80. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	172	5
subtype1	72	4
subtype2	57	1
subtype3	18	0
subtype4	25	0

Figure S72. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.648 (Kruskal-Wallis (anova)), Q value = 0.88

Table S81. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	62	96.8 (6.2)
subtype1	21	95.2 (8.1)
subtype2	22	97.3 (5.5)
subtype3	8	98.8 (3.5)
subtype4	11	97.3 (4.7)

Figure S73. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.00012 (Fisher's exact test), Q value = 0.001

Table S82. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	18	13	148
subtype1	13	13	51
subtype2	3	0	56
subtype3	0	0	18
subtype4	2	0	23

Figure S74. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.578 (Kruskal-Wallis (anova)), Q value = 0.88

Table S83. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	21	0.9 (2.8)
subtype1	14	1.2 (3.4)
subtype2	3	0.3 (0.6)
subtype3	3	0.0 (0.0)
subtype4	1	0.0 (NA)

Figure S75. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.67 (Fisher's exact test), Q value = 0.88

Table S84. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	6	20	148
subtype1	0	3	10	63
subtype2	1	2	4	49
subtype3	0	0	1	17
subtype4	0	1	5	19

Figure S76. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.331 (Fisher's exact test), Q value = 0.74

Table S85. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	138
subtype1	1	61
subtype2	2	45
subtype3	0	12
subtype4	2	20

Figure S77. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S86. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	35	56	17	40	30

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

P value = 0.659 (logrank test), Q value = 0.88

Table S87. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	6	0.1 - 316.7 (24.9)
subtype1	35	0	0.2 - 127.6 (30.5)
subtype2	56	2	0.1 - 114.8 (24.6)
subtype3	17	1	1.1 - 116.2 (17.9)
subtype4	40	2	1.1 - 121.0 (26.3)
subtype5	30	1	0.9 - 316.7 (23.5)

Figure S78. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00289 (Kruskal-Wallis (anova)), Q value = 0.017

Table S88. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	178	47.2 (15.1)
subtype1	35	50.7 (13.4)
subtype2	56	47.6 (14.3)
subtype3	17	56.3 (11.6)
subtype4	40	41.1 (15.6)
subtype5	30	45.7 (16.6)

Figure S79. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.00013 (Fisher's exact test), Q value = 0.001

Table S89. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	146	32
subtype1	31	4
subtype2	54	2
subtype3	15	2
subtype4	27	13
subtype5	19	11

Figure S80. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.634 (Fisher's exact test), Q value = 0.88

Table S90. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	77
subtype1	22	13
subtype2	30	26
subtype3	11	6
subtype4	24	16
subtype5	14	16

Figure S81. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.438 (Fisher's exact test), Q value = 0.81

Table S91. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	171	5
subtype1	35	0
subtype2	54	1
subtype3	17	0
subtype4	38	2
subtype5	27	2

Figure S82. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.759 (Kruskal-Wallis (anova)), Q value = 0.96

Table S92. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	61	96.7 (6.3)
subtype1	13	97.7 (4.4)
subtype2	20	97.0 (5.7)
subtype3	4	97.5 (5.0)
subtype4	10	93.0 (10.6)
subtype5	14	97.9 (4.3)

Figure S83. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00197 (Fisher's exact test), Q value = 0.013

Table S93. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	18	13	147
subtype1	2	2	31
subtype2	2	0	54
subtype3	1	1	15
subtype4	8	5	27
subtype5	5	5	20

Figure S84. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.759 (Kruskal-Wallis (anova)), Q value = 0.96

Table S94. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	21	0.9 (2.8)
subtype1	9	1.8 (4.3)
subtype2	4	0.2 (0.5)
subtype3	1	0.0 (NA)
subtype4	5	0.2 (0.4)
subtype5	2	0.0 (0.0)

Figure S85. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.656 (Fisher's exact test), Q value = 0.88

Table S95. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	6	20	147
subtype1	0	0	6	29
subtype2	0	2	5	47
subtype3	1	1	1	13
subtype4	0	2	5	32
subtype5	0	1	3	26

Figure S86. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.598 (Fisher's exact test), Q value = 0.88

Table S96. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	137
subtype1	1	24
subtype2	2	45
subtype3	0	12
subtype4	0	31
subtype5	2	25

Figure S87. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S97. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	21	47	31	16	26	18	19

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

P value = 0.628 (logrank test), Q value = 0.88

Table S98. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	6	0.1 - 316.7 (24.9)
subtype1	21	1	1.2 - 108.3 (19.6)
subtype2	47	2	0.1 - 110.0 (24.4)
subtype3	31	3	0.2 - 316.7 (32.1)
subtype4	16	0	1.1 - 114.8 (24.0)
subtype5	26	0	1.4 - 121.0 (23.0)
subtype6	18	0	0.7 - 86.9 (31.2)
subtype7	19	0	0.8 - 111.2 (25.5)

Figure S88. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.035 (Kruskal-Wallis (anova)), Q value = 0.18

Table S99. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	178	47.2 (15.1)
subtype1	21	52.1 (15.7)
subtype2	47	46.7 (14.5)
subtype3	31	46.8 (14.6)
subtype4	16	54.4 (12.8)
subtype5	26	39.5 (16.2)
subtype6	18	47.2 (13.0)
subtype7	19	48.5 (15.9)

Figure S89. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00011

Table S100. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	ADRENAL GLAND	EXTRA-ADRENAL SITE
ALL	146	32
subtype1	15	6
subtype2	46	1
subtype3	15	16
subtype4	15	1
subtype5	18	8
subtype6	18	0
subtype7	19	0

Figure S90. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0492 (Fisher's exact test), Q value = 0.23

Table S101. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	101	77
subtype1	17	4
subtype2	23	24
subtype3	13	18
subtype4	11	5
subtype5	14	12
subtype6	9	9
subtype7	14	5

Figure S91. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.483 (Fisher's exact test), Q value = 0.82

Table S102. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	171	5
subtype1	21	0
subtype2	44	1
subtype3	28	3
subtype4	16	0
subtype5	25	1
subtype6	18	0
subtype7	19	0

Figure S92. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.533 (Kruskal-Wallis (anova)), Q value = 0.86

Table S103. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	61	96.7 (6.3)
subtype1	3	93.3 (11.5)
subtype2	17	96.5 (6.1)
subtype3	8	95.0 (5.3)
subtype4	6	100.0 (0.0)
subtype5	8	95.0 (10.7)
subtype6	8	98.8 (3.5)
subtype7	11	97.3 (4.7)

Figure S93. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00011

Table S104. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	PARAGANGLIOMA	PARAGANGLIOMA; EXTRA-ADRENAL PHEOCHROMOCYTOMA	PHEOCHROMOCYTOMA
ALL	18	13	147
subtype1	4	2	15
subtype2	1	0	46
subtype3	8	7	16
subtype4	1	0	15
subtype5	4	4	18
subtype6	0	0	18
subtype7	0	0	19

Figure S94. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.317 (Kruskal-Wallis (anova)), Q value = 0.74

Table S105. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	21	0.9 (2.8)
subtype1	3	0.0 (0.0)
subtype2	2	0.0 (0.0)
subtype3	9	1.8 (4.3)
subtype4	1	1.0 (NA)
subtype5	2	0.5 (0.7)
subtype6	3	0.0 (0.0)
subtype7	1	0.0 (NA)

Figure S95. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.234 (Fisher's exact test), Q value = 0.64

Table S106. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	6	20	147
subtype1	0	2	3	16
subtype2	0	1	3	41
subtype3	0	0	4	26
subtype4	1	1	0	13
subtype5	0	1	5	20
subtype6	0	0	1	17
subtype7	0	1	4	14

Figure S96. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.395 (Fisher's exact test), Q value = 0.77

Table S107. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	137
subtype1	1	15
subtype2	2	37
subtype3	0	24
subtype4	0	12
subtype5	0	22
subtype6	0	12
subtype7	2	15

Figure S97. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/PCPG-TP/22542422/PCPG-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/PCPG-TP/22506925/PCPG-TP.merged_data.txt
Number of patients = 179
Number of clustering approaches = 10
Number of selected clinical features = 10
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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