Correlation between aggregated molecular cancer subtypes and selected clinical features

Testicular Germ Cell Tumors (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12J69ZR

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 8 clinical features across 133 patients, 32 significant findings 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 correlate to 'NEOPLASM_DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', and 'PATHOLOGY_T_STAGE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', and 'PATHOLOGY_M_STAGE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', and 'PATHOLOGY_M_STAGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', and 'PATHOLOGY_T_STAGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', and 'PATHOLOGY_M_STAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', and 'PATHOLOGY_M_STAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', and 'PATHOLOGY_M_STAGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', and 'PATHOLOGY_T_STAGE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', and 'PATHOLOGY_T_STAGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 32 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.369 (0.534)	0.134 (0.289)	0.00222 (0.0197)	0.744 (0.902)	0.787 (0.918)	0.325 (0.521)	0.858 (0.918)	0.623 (0.791)
METHLYATION CNMF	0.456 (0.639)	0.00219 (0.0197)	0.00394 (0.0287)	0.0132 (0.0502)	0.103 (0.229)	0.171 (0.351)	0.374 (0.534)	0.662 (0.828)
RPPA CNMF subtypes	0.854 (0.918)	0.00661 (0.03)	6e-05 (0.0044)	0.00011 (0.0044)	0.0156 (0.0532)	0.033 (0.0851)	0.587 (0.757)	0.84 (0.918)
RPPA cHierClus subtypes	0.545 (0.715)	0.00928 (0.0391)	0.00357 (0.0286)	0.191 (0.373)	0.205 (0.382)	0.00674 (0.03)	0.837 (0.918)	0.719 (0.884)
RNAseq CNMF subtypes	0.326 (0.521)	0.00485 (0.0293)	0.00061 (0.00976)	0.00512 (0.0293)	0.349 (0.534)	0.0705 (0.166)	0.861 (0.918)	0.37 (0.534)
RNAseq cHierClus subtypes	0.018 (0.0532)	0.0583 (0.141)	0.00571 (0.0298)	0.0102 (0.041)	0.218 (0.397)	0.0257 (0.0694)	0.963 (0.975)	0.48 (0.651)
MIRSEQ CNMF	0.918 (0.941)	0.162 (0.342)	0.0499 (0.125)	0.0173 (0.0532)	0.296 (0.494)	0.026 (0.0694)	0.978 (0.978)	0.35 (0.534)
MIRSEQ CHIERARCHICAL	0.884 (0.918)	0.00597 (0.0298)	0.00075 (0.01)	0.00505 (0.0293)	0.253 (0.441)	0.0163 (0.0532)	0.863 (0.918)	0.366 (0.534)
MIRseq Mature CNMF subtypes	0.237 (0.422)	0.0167 (0.0532)	0.00026 (0.00693)	0.00043 (0.0086)	0.293 (0.494)	0.0782 (0.179)	0.824 (0.918)	0.489 (0.652)
MIRseq Mature cHierClus subtypes	0.83 (0.918)	0.0199 (0.0568)	0.00221 (0.0197)	0.0173 (0.0532)	0.198 (0.377)	0.182 (0.364)	0.877 (0.918)	0.48 (0.651)

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	44	43	46

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

P value = 0.369 (logrank test), Q value = 0.53

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	133	4	0.1 - 229.2 (37.7)
subtype1	44	2	0.1 - 161.7 (34.3)
subtype2	43	0	0.2 - 191.3 (36.8)
subtype3	46	2	0.6 - 229.2 (59.5)

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 = 0.134 (Kruskal-Wallis (anova)), Q value = 0.29

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

	nPatients	Mean (Std.Dev)
ALL	133	32.0 (9.3)
subtype1	44	32.8 (8.3)
subtype2	43	32.6 (8.4)
subtype3	46	30.5 (11.0)

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 'NEOPLASM_DISEASESTAGE'

P value = 0.00222 (Fisher's exact test), Q value = 0.02

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	19	25	11	5	6	1	1	2	1	6	5	46
subtype1	4	15	5	1	4	0	1	0	1	1	1	10
subtype2	10	6	4	0	2	1	0	1	0	0	1	15
subtype3	5	4	2	4	0	0	0	1	0	5	3	21

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 = 0.744 (Fisher's exact test), Q value = 0.9

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

nPatients	T1	T2	T3
ALL	75	51	6
subtype1	27	14	2
subtype2	24	18	1
subtype3	24	19	3

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 = 0.787 (Fisher's exact test), Q value = 0.92

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

nPatients	N0	N1+N2
ALL	46	13
subtype1	13	5
subtype2	13	3
subtype3	20	5

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 = 0.325 (Fisher's exact test), Q value = 0.52

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

nPatients	0	1
ALL	114	4
subtype1	37	1
subtype2	38	0
subtype3	39	3

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 'RACE'

P value = 0.858 (Fisher's exact test), Q value = 0.92

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	118
subtype1	2	3	38
subtype2	1	1	38
subtype3	1	2	42

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.623 (Fisher's exact test), Q value = 0.79

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	111
subtype1	2	37
subtype2	4	37
subtype3	5	37

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S10. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	53	45	35

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.456 (logrank test), Q value = 0.64

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	133	4	0.1 - 229.2 (37.7)
subtype1	53	2	0.1 - 191.3 (27.6)
subtype2	45	2	0.5 - 229.2 (59.8)
subtype3	35	0	0.4 - 184.4 (51.0)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00219 (Kruskal-Wallis (anova)), Q value = 0.02

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	133	32.0 (9.3)
subtype1	53	34.3 (8.3)
subtype2	45	28.6 (9.4)
subtype3	35	32.7 (9.8)

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00394 (Fisher's exact test), Q value = 0.029

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	19	25	11	5	6	1	1	2	1	6	5	46
subtype1	9	18	5	0	1	1	1	0	0	1	0	15
subtype2	4	3	4	3	5	0	0	1	1	4	2	16
subtype3	6	4	2	2	0	0	0	1	0	1	3	15

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0132 (Fisher's exact test), Q value = 0.05

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	75	51	6
subtype1	34	16	2
subtype2	18	26	1
subtype3	23	9	3

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.103 (Fisher's exact test), Q value = 0.23

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	46	13
subtype1	15	3
subtype2	17	9
subtype3	14	1

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.171 (Fisher's exact test), Q value = 0.35

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	114	4
subtype1	48	0
subtype2	38	2
subtype3	28	2

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'RACE'

P value = 0.374 (Fisher's exact test), Q value = 0.53

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	118
subtype1	3	1	48
subtype2	0	3	42
subtype3	1	2	28

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.662 (Fisher's exact test), Q value = 0.83

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	111
subtype1	4	48
subtype2	3	37
subtype3	4	26

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S19. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	47	11	27	18

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.854 (logrank test), Q value = 0.92

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	103	4	0.1 - 229.2 (41.5)
subtype1	47	1	0.1 - 176.2 (27.6)
subtype2	11	0	14.1 - 191.3 (63.2)
subtype3	27	1	4.9 - 212.7 (59.6)
subtype4	18	2	0.6 - 229.2 (61.6)

Figure S17. 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 = 0.00661 (Kruskal-Wallis (anova)), Q value = 0.03

Table S21. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	103	32.0 (9.0)
subtype1	47	34.3 (8.2)
subtype2	11	31.1 (7.1)
subtype3	27	32.0 (10.8)
subtype4	18	26.3 (7.0)

Figure S18. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.0044

Table S22. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	14	20	8	5	3	1	1	1	5	4	38
subtype1	11	13	4	0	0	1	0	0	0	2	15
subtype2	0	1	4	0	2	0	0	1	1	0	2
subtype3	3	3	0	2	0	0	1	0	2	1	14
subtype4	0	3	0	3	1	0	0	0	2	1	7

Figure S19. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00011 (Fisher's exact test), Q value = 0.0044

Table S23. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	60	40	3
subtype1	31	16	0
subtype2	2	9	0
subtype3	21	4	2
subtype4	6	11	1

Figure S20. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0156 (Fisher's exact test), Q value = 0.053

Table S24. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	40	9
subtype1	16	0
subtype2	5	4
subtype3	12	2
subtype4	7	3

Figure S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.033 (Fisher's exact test), Q value = 0.085

Table S25. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	89	4
subtype1	43	0
subtype2	9	1
subtype3	21	3
subtype4	16	0

Figure S22. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.587 (Fisher's exact test), Q value = 0.76

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	4	91
subtype1	3	2	39
subtype2	0	0	11
subtype3	1	0	25
subtype4	0	2	16

Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.84 (Fisher's exact test), Q value = 0.92

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	85
subtype1	5	39
subtype2	0	10
subtype3	3	21
subtype4	1	15

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S28. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	49	19	35

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.545 (logrank test), Q value = 0.72

Table S29. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	103	4	0.1 - 229.2 (41.5)
subtype1	49	1	0.1 - 191.3 (30.4)
subtype2	19	0	4.9 - 170.3 (67.7)
subtype3	35	3	0.6 - 229.2 (59.3)

Figure S25. 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 = 0.00928 (Kruskal-Wallis (anova)), Q value = 0.039

Table S30. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	103	32.0 (9.0)
subtype1	49	34.1 (8.0)
subtype2	19	32.3 (10.9)
subtype3	35	28.7 (8.5)

Figure S26. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00357 (Fisher's exact test), Q value = 0.029

Table S31. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	14	20	8	5	3	1	1	1	5	4	38
subtype1	11	14	3	0	0	1	0	0	1	2	16
subtype2	0	3	4	1	1	0	0	1	1	1	6
subtype3	3	3	1	4	2	0	1	0	3	1	16

Figure S27. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.191 (Fisher's exact test), Q value = 0.37

Table S32. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	60	40	3
subtype1	33	16	0
subtype2	10	8	1
subtype3	17	16	2

Figure S28. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.205 (Fisher's exact test), Q value = 0.38

Table S33. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	40	9
subtype1	17	1
subtype2	8	3
subtype3	15	5

Figure S29. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00674 (Fisher's exact test), Q value = 0.03

Table S34. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	89	4
subtype1	45	0
subtype2	13	3
subtype3	31	1

Figure S30. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.837 (Fisher's exact test), Q value = 0.92

Table S35. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	4	91
subtype1	3	2	42
subtype2	0	0	18
subtype3	1	2	31

Figure S31. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.719 (Fisher's exact test), Q value = 0.88

Table S36. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	85
subtype1	5	41
subtype2	2	14
subtype3	2	30

Figure S32. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S37. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	68	36	29

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.326 (logrank test), Q value = 0.52

Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	133	4	0.1 - 229.2 (37.7)
subtype1	68	1	0.1 - 191.3 (26.3)
subtype2	36	3	0.6 - 229.2 (60.8)
subtype3	29	0	4.9 - 184.4 (58.9)

Figure S33. 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 = 0.00485 (Kruskal-Wallis (anova)), Q value = 0.029

Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	133	32.0 (9.3)
subtype1	68	33.8 (8.1)
subtype2	36	28.8 (10.1)
subtype3	29	31.6 (10.3)

Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00061 (Fisher's exact test), Q value = 0.0098

Table S40. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	19	25	11	5	6	1	1	2	1	6	5	46
subtype1	15	19	7	0	1	1	1	0	0	1	2	18
subtype2	2	3	4	3	4	0	0	1	1	3	1	13
subtype3	2	3	0	2	1	0	0	1	0	2	2	15

Figure S35. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00512 (Fisher's exact test), Q value = 0.029

Table S41. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	75	51	6
subtype1	42	23	2
subtype2	13	22	1
subtype3	20	6	3

Figure S36. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.349 (Fisher's exact test), Q value = 0.53

Table S42. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	46	13
subtype1	20	3
subtype2	15	7
subtype3	11	3

Figure S37. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0705 (Fisher's exact test), Q value = 0.17

Table S43. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	114	4
subtype1	60	0
subtype2	32	2
subtype3	22	2

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.861 (Fisher's exact test), Q value = 0.92

Table S44. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	118
subtype1	3	3	59
subtype2	0	2	34
subtype3	1	1	25

Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.37 (Fisher's exact test), Q value = 0.53

Table S45. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	111
subtype1	5	59
subtype2	2	32
subtype3	4	20

Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S46. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	42	26	10	17	8	20	10

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.018 (logrank test), Q value = 0.053

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	133	4	0.1 - 229.2 (37.7)
subtype1	42	1	0.1 - 154.6 (22.8)
subtype2	26	0	0.4 - 191.3 (29.0)
subtype3	10	2	0.6 - 212.7 (43.7)
subtype4	17	0	8.9 - 184.4 (68.0)
subtype5	8	0	6.9 - 161.7 (38.7)
subtype6	20	1	12.9 - 229.2 (61.6)
subtype7	10	0	4.9 - 125.7 (40.8)

Figure S41. 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 = 0.0583 (Kruskal-Wallis (anova)), Q value = 0.14

Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	133	32.0 (9.3)
subtype1	42	34.7 (8.7)
subtype2	26	32.5 (7.0)
subtype3	10	30.0 (8.4)
subtype4	17	29.0 (8.3)
subtype5	8	28.5 (6.8)
subtype6	20	29.0 (11.9)
subtype7	10	35.0 (13.2)

Figure S42. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00571 (Fisher's exact test), Q value = 0.03

Table S49. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	19	25	11	5	6	1	1	2	1	6	5	46
subtype1	9	12	5	0	1	0	1	0	0	1	2	9
subtype2	6	7	2	0	0	1	0	0	0	0	0	9
subtype3	1	0	1	0	0	0	0	0	0	2	1	5
subtype4	2	2	0	1	1	0	0	0	0	1	1	9
subtype5	0	0	2	1	3	0	0	0	1	0	0	1
subtype6	1	3	1	2	1	0	0	1	0	2	0	8
subtype7	0	1	0	1	0	0	0	1	0	0	1	5

Figure S43. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0102 (Fisher's exact test), Q value = 0.041

Table S50. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	75	51	6
subtype1	21	18	2
subtype2	21	5	0
subtype3	6	3	1
subtype4	11	4	2
subtype5	3	5	0
subtype6	6	14	0
subtype7	7	2	1

Figure S44. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.218 (Fisher's exact test), Q value = 0.4

Table S51. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	46	13
subtype1	11	2
subtype2	9	1
subtype3	5	1
subtype4	8	2
subtype5	2	4
subtype6	9	3
subtype7	2	0

Figure S45. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0257 (Fisher's exact test), Q value = 0.069

Table S52. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	114	4
subtype1	37	0
subtype2	23	0
subtype3	8	1
subtype4	15	1
subtype5	7	1
subtype6	19	0
subtype7	5	1

Figure S46. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.963 (Fisher's exact test), Q value = 0.98

Table S53. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	118
subtype1	2	2	36
subtype2	1	1	23
subtype3	0	1	9
subtype4	1	0	15
subtype5	0	0	8
subtype6	0	1	19
subtype7	0	1	8

Figure S47. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.48 (Fisher's exact test), Q value = 0.65

Table S54. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	111
subtype1	2	37
subtype2	3	22
subtype3	0	9
subtype4	3	13
subtype5	1	7
subtype6	1	18
subtype7	1	5

Figure S48. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S55. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	51	53	29

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.918 (logrank test), Q value = 0.94

Table S56. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	133	4	0.1 - 229.2 (37.7)
subtype1	51	1	0.1 - 176.2 (25.0)
subtype2	53	2	0.4 - 229.2 (39.4)
subtype3	29	1	4.9 - 184.4 (58.9)

Figure S49. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.162 (Kruskal-Wallis (anova)), Q value = 0.34

Table S57. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	133	32.0 (9.3)
subtype1	51	33.6 (8.3)
subtype2	53	30.6 (9.6)
subtype3	29	31.5 (10.4)

Figure S50. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0499 (Fisher's exact test), Q value = 0.12

Table S58. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	19	25	11	5	6	1	1	2	1	6	5	46
subtype1	11	15	5	0	1	0	1	0	0	1	2	13
subtype2	6	7	6	3	4	1	0	1	1	3	1	18
subtype3	2	3	0	2	1	0	0	1	0	2	2	15

Figure S51. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0173 (Fisher's exact test), Q value = 0.053

Table S59. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	75	51	6
subtype1	32	17	1
subtype2	23	28	2
subtype3	20	6	3

Figure S52. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.296 (Fisher's exact test), Q value = 0.49

Table S60. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	46	13
subtype1	16	2
subtype2	20	9
subtype3	10	2

Figure S53. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.026 (Fisher's exact test), Q value = 0.069

Table S61. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	114	4
subtype1	44	0
subtype2	49	1
subtype3	21	3

Figure S54. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.978 (Fisher's exact test), Q value = 0.98

Table S62. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	118
subtype1	2	2	44
subtype2	1	3	49
subtype3	1	1	25

Figure S55. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.35 (Fisher's exact test), Q value = 0.53

Table S63. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	111
subtype1	4	43
subtype2	3	48
subtype3	4	20

Figure S56. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S64. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	68	36	29

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.884 (logrank test), Q value = 0.92

Table S65. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	133	4	0.1 - 229.2 (37.7)
subtype1	68	1	0.1 - 191.3 (26.3)
subtype2	36	2	0.6 - 229.2 (59.7)
subtype3	29	1	4.9 - 184.4 (58.9)

Figure S57. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00597 (Kruskal-Wallis (anova)), Q value = 0.03

Table S66. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	133	32.0 (9.3)
subtype1	68	33.8 (8.1)
subtype2	36	28.8 (9.9)
subtype3	29	31.5 (10.4)

Figure S58. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00075 (Fisher's exact test), Q value = 0.01

Table S67. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	19	25	11	5	6	1	1	2	1	6	5	46
subtype1	15	19	7	0	1	1	1	0	0	1	2	18
subtype2	2	3	4	3	4	0	0	1	1	3	1	13
subtype3	2	3	0	2	1	0	0	1	0	2	2	15

Figure S59. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00505 (Fisher's exact test), Q value = 0.029

Table S68. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	75	51	6
subtype1	42	23	2
subtype2	13	22	1
subtype3	20	6	3

Figure S60. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.253 (Fisher's exact test), Q value = 0.44

Table S69. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	46	13
subtype1	20	3
subtype2	16	8
subtype3	10	2

Figure S61. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.0163 (Fisher's exact test), Q value = 0.053

Table S70. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	114	4
subtype1	60	0
subtype2	33	1
subtype3	21	3

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.863 (Fisher's exact test), Q value = 0.92

Table S71. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	118
subtype1	3	3	59
subtype2	0	2	34
subtype3	1	1	25

Figure S63. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.366 (Fisher's exact test), Q value = 0.53

Table S72. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	111
subtype1	5	59
subtype2	2	32
subtype3	4	20

Figure S64. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S73. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	67	34	32

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

P value = 0.237 (logrank test), Q value = 0.42

Table S74. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	133	4	0.1 - 229.2 (37.7)
subtype1	67	1	0.1 - 191.3 (27.6)
subtype2	34	1	6.9 - 229.2 (60.8)
subtype3	32	2	0.6 - 184.4 (55.6)

Figure S65. 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 = 0.0167 (Kruskal-Wallis (anova)), Q value = 0.053

Table S75. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	133	32.0 (9.3)
subtype1	67	33.6 (8.0)
subtype2	34	29.6 (10.7)
subtype3	32	31.0 (9.9)

Figure S66. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00026 (Fisher's exact test), Q value = 0.0069

Table S76. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	19	25	11	5	6	1	1	2	1	6	5	46
subtype1	15	18	7	0	1	1	1	0	0	1	2	18
subtype2	2	4	4	3	4	0	0	1	1	3	0	11
subtype3	2	3	0	2	1	0	0	1	0	2	3	17

Figure S67. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00043 (Fisher's exact test), Q value = 0.0086

Table S77. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	75	51	6
subtype1	41	23	2
subtype2	12	22	0
subtype3	22	6	4

Figure S68. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.293 (Fisher's exact test), Q value = 0.49

Table S78. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	46	13
subtype1	20	3
subtype2	14	7
subtype3	12	3

Figure S69. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0782 (Fisher's exact test), Q value = 0.18

Table S79. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	114	4
subtype1	59	0
subtype2	31	2
subtype3	24	2

Figure S70. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.824 (Fisher's exact test), Q value = 0.92

Table S80. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	118
subtype1	3	3	58
subtype2	0	1	33
subtype3	1	2	27

Figure S71. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.489 (Fisher's exact test), Q value = 0.65

Table S81. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	111
subtype1	5	58
subtype2	2	31
subtype3	4	22

Figure S72. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S82. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	36	32	36	29

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

P value = 0.83 (logrank test), Q value = 0.92

Table S83. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	133	4	0.1 - 229.2 (37.7)
subtype1	36	1	0.1 - 191.3 (28.5)
subtype2	32	0	0.4 - 176.2 (24.8)
subtype3	36	2	0.6 - 229.2 (60.8)
subtype4	29	1	4.9 - 184.4 (52.4)

Figure S73. 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 = 0.0199 (Kruskal-Wallis (anova)), Q value = 0.057

Table S84. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	133	32.0 (9.3)
subtype1	36	33.9 (8.4)
subtype2	32	33.7 (8.0)
subtype3	36	29.1 (10.2)
subtype4	29	31.1 (10.2)

Figure S74. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00221 (Fisher's exact test), Q value = 0.02

Table S85. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IS
ALL	19	25	11	5	6	1	1	2	1	6	5	46
subtype1	9	13	3	0	1	0	1	0	0	0	0	9
subtype2	6	6	4	0	0	1	0	0	0	1	2	9
subtype3	2	3	4	3	4	0	0	1	1	4	1	12
subtype4	2	3	0	2	1	0	0	1	0	1	2	16

Figure S75. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0173 (Fisher's exact test), Q value = 0.053

Table S86. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	75	51	6
subtype1	21	13	1
subtype2	21	10	1
subtype3	13	22	1
subtype4	20	6	3

Figure S76. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.198 (Fisher's exact test), Q value = 0.38

Table S87. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1+N2
ALL	46	13
subtype1	14	1
subtype2	6	2
subtype3	15	8
subtype4	11	2

Figure S77. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.182 (Fisher's exact test), Q value = 0.36

Table S88. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	114	4
subtype1	33	0
subtype2	27	0
subtype3	32	2
subtype4	22	2

Figure S78. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.877 (Fisher's exact test), Q value = 0.92

Table S89. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	118
subtype1	2	1	32
subtype2	1	2	27
subtype3	0	2	34
subtype4	1	1	25

Figure S79. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.48 (Fisher's exact test), Q value = 0.65

Table S90. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	111
subtype1	2	33
subtype2	3	26
subtype3	2	32
subtype4	4	20

Figure S80. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/TGCT-TP/15111345/TGCT-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/TGCT-TP/15092576/TGCT-TP.merged_data.txt
Number of patients = 133
Number of clustering approaches = 10
Number of selected clinical features = 8
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

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)

Made with Nozzle