Correlation between aggregated molecular cancer subtypes and selected clinical features

Breast Invasive Carcinoma (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/C14Q7TBM

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 12 different clustering approaches and 12 clinical features across 1097 patients, 82 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', and 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
CNMF clustering analysis on RPPA data identified 7 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', and 'RACE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
6 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', and 'RACE'.
9 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 82 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	PATHOLOGIC STAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	RADIATION THERAPY	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	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.918 (0.944)	0.0016 (0.00404)	0.00025 (0.00072)	1e-05 (4.97e-05)	0.175 (0.257)	0.0666 (0.111)	0.206 (0.28)	0.753 (0.822)	1e-05 (4.97e-05)	0.883 (0.931)	0.18 (0.257)	0.559 (0.659)
mRNA cHierClus subtypes	0.185 (0.261)	1.13e-05 (5.43e-05)	0.0129 (0.0265)	0.2 (0.278)	0.00142 (0.00365)	0.942 (0.962)	0.223 (0.3)	0.446 (0.545)	1e-05 (4.97e-05)	0.00777 (0.0167)	0.0163 (0.0318)	0.695 (0.775)
Copy Number Ratio CNMF subtypes	0.407 (0.501)	0.00308 (0.00705)	0.0242 (0.0446)	1e-05 (4.97e-05)	0.00079 (0.00207)	0.148 (0.224)	7e-05 (0.00024)	0.0588 (0.0996)	1e-05 (4.97e-05)	0.0269 (0.0489)	8e-05 (0.000262)	0.886 (0.931)
METHLYATION CNMF	0.000782 (0.00207)	4.65e-05 (0.000176)	0.00251 (0.00593)	0.00041 (0.00114)	0.0156 (0.0309)	0.3 (0.389)	0.0439 (0.0781)	0.97 (0.97)	1e-05 (4.97e-05)	0.0147 (0.0299)	2e-05 (8.73e-05)	0.9 (0.939)
RPPA CNMF subtypes	0.00212 (0.00509)	0.0152 (0.0305)	2e-05 (8.73e-05)	3e-05 (0.000123)	0.00962 (0.0201)	0.259 (0.342)	0.18 (0.257)	0.00175 (0.00434)	1e-05 (4.97e-05)	0.0508 (0.0881)	1e-05 (4.97e-05)	0.654 (0.753)
RPPA cHierClus subtypes	0.101 (0.161)	0.00203 (0.00495)	0.0982 (0.159)	0.205 (0.28)	0.536 (0.643)	0.957 (0.97)	0.0718 (0.119)	0.177 (0.257)	1e-05 (4.97e-05)	0.486 (0.588)	0.00013 (0.00039)	0.373 (0.466)
RNAseq CNMF subtypes	4.93e-05 (0.00018)	7.1e-07 (4.97e-05)	0.00279 (0.00648)	0.0001 (0.00032)	1e-05 (4.97e-05)	0.848 (0.904)	0.0218 (0.0408)	0.818 (0.883)	1e-05 (4.97e-05)	1.62e-05 (7.52e-05)	1e-05 (4.97e-05)	0.366 (0.466)
RNAseq cHierClus subtypes	3.31e-06 (4.97e-05)	1.37e-10 (1.97e-08)	1e-05 (4.97e-05)	1e-05 (4.97e-05)	1e-05 (4.97e-05)	0.28 (0.366)	0.0189 (0.0362)	0.177 (0.257)	1e-05 (4.97e-05)	3.56e-05 (0.000142)	1e-05 (4.97e-05)	0.548 (0.652)
MIRSEQ CNMF	0.607 (0.71)	0.00513 (0.0114)	0.00945 (0.02)	1e-05 (4.97e-05)	0.256 (0.342)	0.694 (0.775)	0.336 (0.432)	0.914 (0.944)	1e-05 (4.97e-05)	0.105 (0.167)	1e-05 (4.97e-05)	0.372 (0.466)
MIRSEQ CHIERARCHICAL	0.0316 (0.0569)	6.34e-05 (0.000223)	3e-05 (0.000123)	5e-05 (0.00018)	8e-05 (0.000262)	0.112 (0.175)	0.134 (0.208)	0.738 (0.811)	1e-05 (4.97e-05)	0.00337 (0.00759)	1e-05 (4.97e-05)	0.18 (0.257)
MIRseq Mature CNMF subtypes	0.967 (0.97)	0.0786 (0.129)	0.00027 (0.000762)	4e-05 (0.000156)	0.00066 (0.00179)	0.021 (0.0399)	0.637 (0.74)	0.138 (0.212)	1e-05 (4.97e-05)	0.0528 (0.0905)	1e-05 (4.97e-05)	0.675 (0.765)
MIRseq Mature cHierClus subtypes	0.201 (0.278)	0.0465 (0.0816)	0.00011 (0.000337)	0.00011 (0.000337)	0.00022 (0.000647)	0.712 (0.789)	0.67 (0.765)	0.375 (0.466)	1e-05 (4.97e-05)	0.00554 (0.0121)	1e-05 (4.97e-05)	0.821 (0.883)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	234	118	174

'mRNA CNMF subtypes' versus 'Time to Death'

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

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	514	83	0.2 - 282.9 (33.7)
subtype1	228	35	0.2 - 281.3 (31.9)
subtype2	117	20	0.3 - 282.9 (50.7)
subtype3	169	28	0.2 - 255.7 (32.6)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0016 (Kruskal-Wallis (anova)), Q value = 0.004

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

	nPatients	Mean (Std.Dev)
ALL	520	58.1 (13.2)
subtype1	230	60.2 (13.1)
subtype2	118	55.6 (13.3)
subtype3	172	56.9 (13.1)

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00025 (Fisher's exact test), Q value = 0.00072

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	51	35	3	1	182	113	78	13	19	13	13
subtype1	23	19	3	1	71	55	34	8	2	7	9
subtype2	20	9	0	0	41	20	19	0	5	0	3
subtype3	8	7	0	0	70	38	25	5	12	6	1

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

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

nPatients	T1	T2	T3	T4
ALL	132	312	60	20
subtype1	64	134	23	12
subtype2	47	51	19	1
subtype3	21	127	18	7

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.175 (Fisher's exact test), Q value = 0.26

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

nPatients	N0	N1	N2	N3
ALL	255	171	60	29
subtype1	108	85	24	9
subtype2	62	38	10	6
subtype3	85	48	26	14

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0666 (Fisher's exact test), Q value = 0.11

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

nPatients	0	1
ALL	495	15
subtype1	217	8
subtype2	113	0
subtype3	165	7

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.206 (Fisher's exact test), Q value = 0.28

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

nPatients	FEMALE	MALE
ALL	520	6
subtype1	229	5
subtype2	118	0
subtype3	173	1

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.753 (Fisher's exact test), Q value = 0.82

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	208	264
subtype1	95	113
subtype2	45	64
subtype3	68	87

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	446	43	1	12	2	21
subtype1	199	14	0	6	2	13
subtype2	84	24	0	6	0	4
subtype3	163	5	1	0	0	4

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.883 (Kruskal-Wallis (anova)), Q value = 0.93

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	408	1.8 (3.5)
subtype1	166	1.6 (2.9)
subtype2	109	1.9 (4.2)
subtype3	133	2.0 (3.6)

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.18 (Fisher's exact test), Q value = 0.26

Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	34	40	361
subtype1	0	13	18	151
subtype2	0	5	7	100
subtype3	1	16	15	110

Figure S11. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.559 (Fisher's exact test), Q value = 0.66

Table S13. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	372
subtype1	4	152
subtype2	2	94
subtype3	1	126

Figure S12. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S14. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	162	272	92

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.185 (logrank test), Q value = 0.26

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	514	83	0.2 - 282.9 (33.7)
subtype1	157	23	0.3 - 281.3 (33.0)
subtype2	266	47	0.2 - 212.2 (33.7)
subtype3	91	13	0.3 - 282.9 (35.3)

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.13e-05 (Kruskal-Wallis (anova)), Q value = 5.4e-05

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	520	58.1 (13.2)
subtype1	160	61.2 (13.4)
subtype2	268	57.8 (12.9)
subtype3	92	53.4 (12.7)

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0129 (Fisher's exact test), Q value = 0.027

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	51	35	3	1	182	113	78	13	19	13	13
subtype1	17	13	3	1	55	38	19	6	0	3	6
subtype2	25	14	0	0	87	56	51	6	16	8	6
subtype3	9	8	0	0	40	19	8	1	3	2	1

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.2 (Fisher's exact test), Q value = 0.28

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	132	312	60	20
subtype1	47	93	13	8
subtype2	63	159	39	11
subtype3	22	60	8	1

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00142 (Fisher's exact test), Q value = 0.0037

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	255	171	60	29
subtype1	80	62	12	4
subtype2	117	85	42	21
subtype3	58	24	6	4

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.942 (Fisher's exact test), Q value = 0.96

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	495	15
subtype1	151	4
subtype2	256	8
subtype3	88	3

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.223 (Fisher's exact test), Q value = 0.3

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	520	6
subtype1	158	4
subtype2	270	2
subtype3	92	0

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.446 (Fisher's exact test), Q value = 0.54

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	208	264
subtype1	65	81
subtype2	112	132
subtype3	31	51

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	446	43	1	12	2	21
subtype1	142	5	0	4	2	9
subtype2	220	37	0	8	0	7
subtype3	84	1	1	0	0	5

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00777 (Kruskal-Wallis (anova)), Q value = 0.017

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	408	1.8 (3.5)
subtype1	116	1.3 (2.2)
subtype2	206	2.2 (4.1)
subtype3	86	1.5 (3.3)

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.0163 (Fisher's exact test), Q value = 0.032

Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	34	40	361
subtype1	0	10	17	100
subtype2	1	21	11	191
subtype3	0	3	12	70

Figure S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.695 (Fisher's exact test), Q value = 0.78

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	372
subtype1	1	108
subtype2	5	189
subtype3	1	75

Figure S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

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

Table S27. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	330	131	295	294	29

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

P value = 0.407 (logrank test), Q value = 0.5

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	1065	148	0.0 - 282.9 (28.2)
subtype1	326	38	0.2 - 170.2 (26.8)
subtype2	130	18	0.3 - 129.6 (23.1)
subtype3	289	39	0.0 - 281.3 (32.9)
subtype4	291	48	0.0 - 282.9 (25.9)
subtype5	29	5	6.5 - 127.3 (26.0)

Figure S25. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00308 (Kruskal-Wallis (anova)), Q value = 0.007

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	1064	58.6 (13.2)
subtype1	323	58.9 (13.4)
subtype2	129	59.6 (12.5)
subtype3	292	59.6 (13.4)
subtype4	292	56.5 (12.9)
subtype5	28	64.4 (13.3)

Figure S26. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0242 (Fisher's exact test), Q value = 0.045

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	89	83	6	6	352	253	2	155	27	65	20	13
subtype1	23	27	2	1	102	74	1	62	12	15	6	5
subtype2	9	8	0	1	38	37	0	16	5	12	2	1
subtype3	36	32	4	3	85	64	1	37	4	19	2	5
subtype4	20	15	0	1	116	70	0	38	6	14	9	2
subtype5	1	1	0	0	11	8	0	2	0	5	1	0

Figure S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	274	625	137	40
subtype1	85	194	35	15
subtype2	22	83	19	7
subtype3	105	134	49	6
subtype4	54	199	29	11
subtype5	8	15	5	1

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00079 (Fisher's exact test), Q value = 0.0021

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	506	358	119	77
subtype1	136	117	49	19
subtype2	57	44	12	16
subtype3	147	102	18	21
subtype4	156	83	38	16
subtype5	10	12	2	5

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.148 (Fisher's exact test), Q value = 0.22

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	889	22
subtype1	281	7
subtype2	102	2
subtype3	241	2
subtype4	244	10
subtype5	21	1

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 7e-05 (Fisher's exact test), Q value = 0.00024

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	1067	12
subtype1	318	12
subtype2	131	0
subtype3	295	0
subtype4	294	0
subtype5	29	0

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0588 (Fisher's exact test), Q value = 0.1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	437	547
subtype1	135	167
subtype2	42	79
subtype3	134	136
subtype4	111	152
subtype5	15	13

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	768	201	6	9	30	17	46
subtype1	0	262	41	0	2	11	4	10
subtype2	0	78	45	0	0	4	1	3
subtype3	0	152	97	0	2	10	11	23
subtype4	1	259	10	5	5	5	0	8
subtype5	0	17	8	1	0	0	1	2

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0269 (Kruskal-Wallis (anova)), Q value = 0.049

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	913	2.4 (4.7)
subtype1	258	2.4 (3.9)
subtype2	116	3.1 (5.6)
subtype3	259	2.2 (4.8)
subtype4	256	2.1 (4.6)
subtype5	24	3.7 (6.0)

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 8e-05 (Fisher's exact test), Q value = 0.00026

Table S38. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	60	181	744
subtype1	0	24	49	212
subtype2	0	7	14	101
subtype3	0	11	36	231
subtype4	1	16	75	183
subtype5	0	2	7	17

Figure S35. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.886 (Fisher's exact test), Q value = 0.93

Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	39	874
subtype1	10	261
subtype2	4	99
subtype3	13	246
subtype4	12	242
subtype5	0	26

Figure S36. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S40. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	224	184	145	141	88

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.000782 (logrank test), Q value = 0.0021

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	780	102	0.0 - 282.9 (27.5)
subtype1	223	23	0.3 - 263.3 (28.3)
subtype2	184	35	0.0 - 160.9 (24.7)
subtype3	145	17	1.1 - 216.8 (35.8)
subtype4	140	23	0.0 - 282.9 (24.7)
subtype5	88	4	0.2 - 169.5 (25.1)

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 4.65e-05 (Kruskal-Wallis (anova)), Q value = 0.00018

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	769	58.2 (13.1)
subtype1	219	58.5 (13.4)
subtype2	184	61.8 (12.9)
subtype3	143	56.5 (12.1)
subtype4	138	55.7 (12.5)
subtype5	85	56.5 (14.0)

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00251 (Fisher's exact test), Q value = 0.0059

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	60	61	5	6	244	187	2	126	18	53	11	5
subtype1	17	19	3	1	60	63	0	39	5	12	1	3
subtype2	8	11	0	2	53	41	1	38	8	16	4	1
subtype3	22	10	1	1	47	26	1	23	3	9	2	0
subtype4	6	9	1	1	63	38	0	13	1	5	2	1
subtype5	7	12	0	1	21	19	0	13	1	11	2	0

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00041 (Fisher's exact test), Q value = 0.0011

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	199	447	109	24
subtype1	61	129	27	5
subtype2	30	109	34	11
subtype3	54	69	19	3
subtype4	26	96	15	3
subtype5	28	44	14	2

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0156 (Fisher's exact test), Q value = 0.031

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	349	269	95	57
subtype1	86	91	29	13
subtype2	74	59	29	17
subtype3	69	48	18	10
subtype4	83	40	11	6
subtype5	37	31	8	11

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.3 (Fisher's exact test), Q value = 0.39

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	609	13
subtype1	180	1
subtype2	136	5
subtype3	114	2
subtype4	112	3
subtype5	67	2

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0439 (Fisher's exact test), Q value = 0.078

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	773	9
subtype1	222	2
subtype2	180	4
subtype3	145	0
subtype4	141	0
subtype5	85	3

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

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

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	316	412
subtype1	93	116
subtype2	74	98
subtype3	56	80
subtype4	58	71
subtype5	35	47

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	511	179	6	9	26	15	34
subtype1	0	140	52	0	0	12	5	15
subtype2	0	128	42	0	0	6	4	4
subtype3	0	82	50	2	3	4	1	3
subtype4	1	114	5	4	6	1	0	9
subtype5	0	47	30	0	0	3	5	3

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0147 (Kruskal-Wallis (anova)), Q value = 0.03

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	717	2.6 (4.9)
subtype1	211	2.4 (4.2)
subtype2	163	3.4 (5.8)
subtype3	136	2.4 (5.1)
subtype4	130	1.7 (3.4)
subtype5	77	3.3 (5.7)

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 2e-05 (Fisher's exact test), Q value = 8.7e-05

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	38	160	568
subtype1	0	10	32	179
subtype2	0	11	35	134
subtype3	1	7	26	108
subtype4	0	7	56	75
subtype5	0	3	11	72

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.9 (Fisher's exact test), Q value = 0.94

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	675
subtype1	12	190
subtype2	11	162
subtype3	7	127
subtype4	5	122
subtype5	3	74

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S53. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	199	239	30	208	133	67	10

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.00212 (logrank test), Q value = 0.0051

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	874	122	0.0 - 282.9 (27.7)
subtype1	194	32	0.0 - 282.9 (22.0)
subtype2	238	32	0.2 - 170.2 (27.6)
subtype3	30	6	0.3 - 89.1 (17.0)
subtype4	205	21	0.3 - 263.3 (37.7)
subtype5	130	19	0.3 - 212.2 (26.5)
subtype6	67	7	0.2 - 106.8 (24.4)
subtype7	10	5	1.0 - 146.5 (30.2)

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0152 (Kruskal-Wallis (anova)), Q value = 0.03

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	872	58.4 (13.3)
subtype1	195	57.4 (13.6)
subtype2	235	60.9 (13.7)
subtype3	29	61.7 (14.9)
subtype4	204	57.0 (12.8)
subtype5	133	58.4 (12.9)
subtype6	66	55.4 (11.7)
subtype7	10	55.7 (10.1)

Figure S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 8.7e-05

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	62	64	4	4	296	213	2	134	23	53	18	6
subtype1	6	11	0	3	80	52	0	21	3	14	7	0
subtype2	17	22	1	0	73	63	1	38	6	10	4	3
subtype3	3	5	0	0	8	5	0	4	2	0	0	1
subtype4	22	18	1	1	52	58	1	34	4	12	3	1
subtype5	9	2	2	0	55	21	0	23	6	10	3	1
subtype6	5	6	0	0	27	12	0	14	0	3	0	0
subtype7	0	0	0	0	1	2	0	0	2	4	1	0

Figure S51. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00012

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	201	533	115	36
subtype1	31	138	24	6
subtype2	57	143	30	8
subtype3	10	14	2	4
subtype4	66	104	33	5
subtype5	21	81	20	11
subtype6	16	45	6	0
subtype7	0	8	0	2

Figure S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00962 (Fisher's exact test), Q value = 0.02

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	412	291	104	65
subtype1	101	62	19	16
subtype2	107	85	28	13
subtype3	16	5	5	1
subtype4	88	78	25	14
subtype5	63	40	17	12
subtype6	36	18	10	3
subtype7	1	3	0	6

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.259 (Fisher's exact test), Q value = 0.34

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	739	20
subtype1	161	8
subtype2	201	4
subtype3	28	0
subtype4	175	4
subtype5	102	3
subtype6	63	0
subtype7	9	1

Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.18 (Fisher's exact test), Q value = 0.26

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	876	10
subtype1	197	2
subtype2	234	5
subtype3	30	0
subtype4	206	2
subtype5	133	0
subtype6	67	0
subtype7	9	1

Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.00175 (Fisher's exact test), Q value = 0.0043

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	365	448
subtype1	87	92
subtype2	112	110
subtype3	9	17
subtype4	66	129
subtype5	65	58
subtype6	25	36
subtype7	1	6

Figure S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	642	155	6	7	23	14	38
subtype1	0	167	12	4	2	3	2	9
subtype2	0	166	40	0	0	9	7	17
subtype3	0	24	4	0	1	1	0	0
subtype4	0	130	69	1	0	5	1	2
subtype5	1	92	23	0	3	4	4	6
subtype6	0	56	5	1	1	1	0	3
subtype7	0	7	2	0	0	0	0	1

Figure S57. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0508 (Kruskal-Wallis (anova)), Q value = 0.088

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	758	2.4 (4.7)
subtype1	166	2.6 (5.3)
subtype2	203	2.2 (4.1)
subtype3	19	1.5 (3.4)
subtype4	195	2.6 (5.1)
subtype5	108	2.0 (3.7)
subtype6	60	2.1 (3.6)
subtype7	7	11.0 (11.2)

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	59	137	629
subtype1	0	19	57	112
subtype2	0	12	26	184
subtype3	0	1	4	19
subtype4	0	12	18	168
subtype5	0	10	23	89
subtype6	1	5	8	51
subtype7	0	0	1	6

Figure S59. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.654 (Fisher's exact test), Q value = 0.75

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	28	730
subtype1	6	170
subtype2	8	196
subtype3	1	22
subtype4	10	164
subtype5	2	110
subtype6	1	61
subtype7	0	7

Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S66. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	225	401	260

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.101 (logrank test), Q value = 0.16

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	874	122	0.0 - 282.9 (27.7)
subtype1	222	37	0.0 - 282.9 (28.3)
subtype2	396	54	0.2 - 170.2 (24.6)
subtype3	256	31	0.2 - 263.3 (34.3)

Figure S61. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00203 (Kruskal-Wallis (anova)), Q value = 0.005

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	872	58.4 (13.3)
subtype1	222	56.0 (13.1)
subtype2	393	59.8 (14.1)
subtype3	257	58.3 (12.0)

Figure S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0982 (Fisher's exact test), Q value = 0.16

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	62	64	4	4	296	213	2	134	23	53	18	6
subtype1	9	13	0	1	98	50	1	26	4	15	4	0
subtype2	29	33	1	2	118	97	1	66	14	24	10	5
subtype3	24	18	3	1	80	66	0	42	5	14	4	1

Figure S63. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.205 (Fisher's exact test), Q value = 0.28

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	201	533	115	36
subtype1	41	150	25	9
subtype2	89	240	53	18
subtype3	71	143	37	9

Figure S64. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.536 (Fisher's exact test), Q value = 0.64

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	412	291	104	65
subtype1	114	70	20	18
subtype2	184	126	52	29
subtype3	114	95	32	18

Figure S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.957 (Fisher's exact test), Q value = 0.97

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	739	20
subtype1	197	5
subtype2	330	10
subtype3	212	5

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0718 (Fisher's exact test), Q value = 0.12

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	876	10
subtype1	225	0
subtype2	393	8
subtype3	258	2

Figure S67. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.177 (Fisher's exact test), Q value = 0.26

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	365	448
subtype1	92	109
subtype2	177	195
subtype3	96	144

Figure S68. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	642	155	6	7	23	14	38
subtype1	1	199	7	3	4	0	0	11
subtype2	0	285	60	2	1	17	13	23
subtype3	0	158	88	1	2	6	1	4

Figure S69. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.486 (Kruskal-Wallis (anova)), Q value = 0.59

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	758	2.4 (4.7)
subtype1	191	2.5 (5.3)
subtype2	330	2.5 (4.6)
subtype3	237	2.3 (4.4)

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.00013 (Fisher's exact test), Q value = 0.00039

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	59	137	629
subtype1	1	21	49	140
subtype2	0	26	65	283
subtype3	0	12	23	206

Figure S71. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.373 (Fisher's exact test), Q value = 0.47

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	28	730
subtype1	5	192
subtype2	12	336
subtype3	11	202

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S79. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	267	171	147	227	209	52	20

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 4.93e-05 (logrank test), Q value = 0.00018

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	1079	151	0.0 - 282.9 (28.3)
subtype1	262	35	0.2 - 188.7 (29.2)
subtype2	167	39	0.0 - 212.2 (24.3)
subtype3	147	13	0.3 - 169.5 (36.2)
subtype4	225	24	0.3 - 281.3 (25.7)
subtype5	207	29	0.0 - 282.9 (27.0)
subtype6	51	6	2.1 - 216.8 (27.9)
subtype7	20	5	12.3 - 130.2 (47.2)

Figure S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 7.1e-07 (Kruskal-Wallis (anova)), Q value = 5e-05

Table S81. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	1078	58.6 (13.2)
subtype1	262	59.3 (14.5)
subtype2	165	59.9 (13.2)
subtype3	145	54.9 (11.7)
subtype4	225	61.3 (12.6)
subtype5	209	55.6 (12.3)
subtype6	52	61.1 (11.7)
subtype7	20	59.8 (13.6)

Figure S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00279 (Fisher's exact test), Q value = 0.0065

Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	90	85	6	6	358	256	2	156	27	65	20	14
subtype1	18	16	4	1	81	73	1	44	12	10	3	4
subtype2	10	12	0	1	54	38	0	28	6	12	6	2
subtype3	12	14	1	1	31	40	1	25	0	16	3	2
subtype4	25	24	1	1	77	43	0	29	5	10	4	5
subtype5	14	15	0	1	93	47	0	22	4	7	3	1
subtype6	5	3	0	1	16	11	0	6	0	9	1	0
subtype7	6	1	0	0	6	4	0	2	0	1	0	0

Figure S75. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-04 (Fisher's exact test), Q value = 0.00032

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	279	633	138	40
subtype1	61	164	27	13
subtype2	38	106	17	10
subtype3	44	72	31	0
subtype4	78	114	26	9
subtype5	41	137	23	7
subtype6	10	28	13	1
subtype7	7	12	1	0

Figure S76. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	515	361	120	77
subtype1	106	105	36	11
subtype2	65	58	27	16
subtype3	56	56	14	18
subtype4	118	72	20	14
subtype5	129	54	18	8
subtype6	28	12	3	9
subtype7	13	4	2	1

Figure S77. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.848 (Fisher's exact test), Q value = 0.9

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	903	22
subtype1	225	4
subtype2	144	6
subtype3	119	3
subtype4	186	4
subtype5	178	4
subtype6	35	1
subtype7	16	0

Figure S78. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0218 (Fisher's exact test), Q value = 0.041

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	1081	12
subtype1	258	9
subtype2	170	1
subtype3	147	0
subtype4	225	2
subtype5	209	0
subtype6	52	0
subtype7	20	0

Figure S79. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.818 (Fisher's exact test), Q value = 0.88

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	445	553
subtype1	112	134
subtype2	72	74
subtype3	59	80
subtype4	95	116
subtype5	81	107
subtype6	19	30
subtype7	7	12

Figure S80. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S88. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	782	203	6	9	29	17	45
subtype1	0	215	26	0	0	6	9	11
subtype2	0	152	12	0	1	2	0	4
subtype3	0	67	65	0	0	9	1	5
subtype4	0	136	64	0	0	9	4	14
subtype5	1	177	2	5	8	2	3	10
subtype6	0	18	32	0	0	1	0	1
subtype7	0	17	2	1	0	0	0	0

Figure S81. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 1.62e-05 (Kruskal-Wallis (anova)), Q value = 7.5e-05

Table S89. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	925	2.4 (4.6)
subtype1	217	2.2 (3.8)
subtype2	123	3.3 (5.8)
subtype3	135	3.4 (6.1)
subtype4	190	2.0 (4.0)
subtype5	191	1.3 (2.9)
subtype6	50	3.5 (6.2)
subtype7	19	2.1 (5.3)

Figure S82. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S90. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	61	183	753
subtype1	0	16	47	177
subtype2	1	21	24	99
subtype3	0	6	13	123
subtype4	0	9	23	172
subtype5	0	8	69	122
subtype6	0	0	2	46
subtype7	0	1	5	14

Figure S83. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.366 (Fisher's exact test), Q value = 0.47

Table S91. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	39	880
subtype1	9	211
subtype2	2	133
subtype3	7	120
subtype4	12	176
subtype5	6	178
subtype6	2	43
subtype7	1	19

Figure S84. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S92. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	294	166	91	294	188	60

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 3.31e-06 (logrank test), Q value = 5e-05

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	1079	151	0.0 - 282.9 (28.3)
subtype1	288	41	0.2 - 275.9 (25.5)
subtype2	163	36	0.0 - 212.2 (23.1)
subtype3	91	9	0.3 - 169.5 (38.2)
subtype4	293	26	0.3 - 281.3 (32.5)
subtype5	186	27	0.2 - 282.9 (28.4)
subtype6	58	12	0.0 - 102.8 (20.2)

Figure S85. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.37e-10 (Kruskal-Wallis (anova)), Q value = 2e-08

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	1078	58.6 (13.2)
subtype1	287	61.9 (13.9)
subtype2	162	61.8 (12.8)
subtype3	90	56.3 (12.4)
subtype4	293	56.4 (12.5)
subtype5	188	55.0 (12.1)
subtype6	58	59.4 (13.1)

Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	90	85	6	6	358	256	2	156	27	65	20	14
subtype1	20	23	4	2	94	75	1	43	13	7	4	6
subtype2	7	8	0	1	51	38	0	31	8	12	7	2
subtype3	7	9	1	1	17	27	0	14	1	12	1	0
subtype4	42	25	1	1	87	62	0	44	0	23	3	5
subtype5	13	15	0	1	86	41	0	17	4	5	3	1
subtype6	1	5	0	0	23	13	1	7	1	6	2	0

Figure S87. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	279	633	138	40
subtype1	76	170	28	18
subtype2	25	110	21	10
subtype3	25	38	27	1
subtype4	95	156	41	2
subtype5	40	124	17	6
subtype6	18	35	4	3

Figure S88. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S97. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	515	361	120	77
subtype1	126	115	33	12
subtype2	66	48	31	14
subtype3	38	36	5	12
subtype4	147	89	29	25
subtype5	117	51	14	6
subtype6	21	22	8	8

Figure S89. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.28 (Fisher's exact test), Q value = 0.37

Table S98. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	903	22
subtype1	250	5
subtype2	134	7
subtype3	70	1
subtype4	235	3
subtype5	162	4
subtype6	52	2

Figure S90. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0189 (Fisher's exact test), Q value = 0.036

Table S99. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	1081	12
subtype1	285	9
subtype2	164	2
subtype3	91	0
subtype4	293	1
subtype5	188	0
subtype6	60	0

Figure S91. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.177 (Fisher's exact test), Q value = 0.26

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	445	553
subtype1	129	143
subtype2	70	74
subtype3	42	44
subtype4	106	168
subtype5	72	99
subtype6	26	25

Figure S92. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S101. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	782	203	6	9	29	17	45
subtype1	0	224	24	0	0	10	15	21
subtype2	0	138	16	0	3	4	1	4
subtype3	0	27	56	0	0	5	1	2
subtype4	0	176	100	1	0	8	0	9
subtype5	1	163	2	5	5	2	0	9
subtype6	0	54	5	0	1	0	0	0

Figure S93. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 3.56e-05 (Kruskal-Wallis (anova)), Q value = 0.00014

Table S102. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	925	2.4 (4.6)
subtype1	233	1.9 (3.3)
subtype2	121	3.1 (5.0)
subtype3	87	3.7 (6.5)
subtype4	268	2.4 (4.8)
subtype5	173	1.3 (2.9)
subtype6	43	4.2 (7.8)

Figure S94. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S103. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	61	183	753
subtype1	0	19	42	197
subtype2	0	14	20	106
subtype3	0	3	9	77
subtype4	0	8	34	235
subtype5	0	5	65	110
subtype6	1	12	13	28

Figure S95. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.548 (Fisher's exact test), Q value = 0.65

Table S104. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	39	880
subtype1	12	229
subtype2	3	129
subtype3	2	77
subtype4	15	236
subtype5	6	159
subtype6	1	50

Figure S96. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S105. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5	6
Number of samples	140	274	211	218	187	47

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.607 (logrank test), Q value = 0.71

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	1063	147	0.0 - 282.9 (28.1)
subtype1	138	20	0.0 - 275.9 (29.9)
subtype2	269	40	0.2 - 255.7 (24.3)
subtype3	206	22	0.2 - 263.3 (25.1)
subtype4	218	28	0.0 - 282.9 (32.8)
subtype5	185	30	0.3 - 234.3 (26.1)
subtype6	47	7	1.7 - 281.3 (34.4)

Figure S97. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00513 (Kruskal-Wallis (anova)), Q value = 0.011

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	1062	58.6 (13.2)
subtype1	134	57.1 (14.1)
subtype2	273	57.8 (13.0)
subtype3	210	61.0 (13.6)
subtype4	214	57.4 (12.7)
subtype5	185	59.8 (13.0)
subtype6	46	58.2 (11.7)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00945 (Fisher's exact test), Q value = 0.02

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	89	85	7	6	353	250	2	153	26	64	20	14
subtype1	14	6	0	1	44	35	1	24	2	9	2	2
subtype2	10	17	1	1	117	57	0	37	7	12	8	3
subtype3	13	22	3	1	66	47	0	31	8	15	1	4
subtype4	23	19	1	2	53	59	1	33	3	20	3	0
subtype5	23	17	1	1	59	38	0	25	5	7	5	4
subtype6	6	4	1	0	14	14	0	3	1	1	1	1

Figure S99. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S109. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	279	620	135	40
subtype1	31	85	19	4
subtype2	45	191	26	11
subtype3	55	118	28	9
subtype4	70	102	43	3
subtype5	66	99	12	10
subtype6	12	25	7	3

Figure S100. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.256 (Fisher's exact test), Q value = 0.34

Table S110. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	508	356	118	75
subtype1	58	49	18	10
subtype2	146	74	34	15
subtype3	99	68	24	15
subtype4	93	85	18	21
subtype5	86	66	21	10
subtype6	26	14	3	4

Figure S101. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.694 (Fisher's exact test), Q value = 0.78

Table S111. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	887	21
subtype1	120	2
subtype2	230	8
subtype3	170	2
subtype4	165	3
subtype5	164	5
subtype6	38	1

Figure S102. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.336 (Fisher's exact test), Q value = 0.43

Table S112. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	1065	12
subtype1	139	1
subtype2	270	4
subtype3	208	3
subtype4	218	0
subtype5	183	4
subtype6	47	0

Figure S103. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.914 (Fisher's exact test), Q value = 0.94

Table S113. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	441	543
subtype1	61	70
subtype2	111	135
subtype3	84	111
subtype4	86	117
subtype5	79	87
subtype6	20	23

Figure S104. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S114. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	768	200	6	9	29	17	46
subtype1	0	106	17	1	0	5	4	7
subtype2	1	245	4	5	5	3	1	9
subtype3	0	135	48	0	0	7	9	12
subtype4	0	95	106	0	4	5	3	5
subtype5	0	154	14	0	0	8	0	11
subtype6	0	33	11	0	0	1	0	2

Figure S105. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.105 (Kruskal-Wallis (anova)), Q value = 0.17

Table S115. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	915	2.4 (4.6)
subtype1	117	2.7 (5.3)
subtype2	227	2.0 (4.4)
subtype3	171	2.5 (4.6)
subtype4	206	2.9 (5.4)
subtype5	158	1.8 (3.2)
subtype6	36	2.2 (4.7)

Figure S106. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S116. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	61	182	745
subtype1	0	17	21	97
subtype2	0	11	81	156
subtype3	0	8	37	141
subtype4	1	13	29	171
subtype5	0	11	7	148
subtype6	0	1	7	32

Figure S107. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.372 (Fisher's exact test), Q value = 0.47

Table S117. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	39	873
subtype1	9	122
subtype2	6	222
subtype3	8	161
subtype4	9	193
subtype5	7	137
subtype6	0	38

Figure S108. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S118. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5	6	7	8	9
Number of samples	68	124	187	188	73	117	163	73	84

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0316 (logrank test), Q value = 0.057

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	1063	147	0.0 - 282.9 (28.1)
subtype1	65	12	2.4 - 165.8 (32.9)
subtype2	120	23	0.0 - 146.5 (25.9)
subtype3	187	20	0.3 - 140.3 (33.4)
subtype4	184	22	0.2 - 275.9 (29.5)
subtype5	72	6	0.3 - 281.3 (35.6)
subtype6	117	20	0.3 - 263.3 (28.8)
subtype7	161	27	0.2 - 282.9 (30.2)
subtype8	73	4	1.1 - 170.2 (21.1)
subtype9	84	13	0.3 - 216.8 (19.5)

Figure S109. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 6.34e-05 (Kruskal-Wallis (anova)), Q value = 0.00022

Table S120. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	1062	58.6 (13.2)
subtype1	66	59.6 (13.3)
subtype2	119	59.4 (12.8)
subtype3	185	58.3 (12.1)
subtype4	187	58.9 (15.4)
subtype5	73	57.8 (11.6)
subtype6	116	60.0 (12.5)
subtype7	163	54.3 (11.9)
subtype8	70	62.6 (14.2)
subtype9	83	60.9 (12.8)

Figure S110. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00012

Table S121. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	89	85	7	6	353	250	2	153	26	64	20	14
subtype1	2	5	0	1	17	20	0	13	4	3	1	2
subtype2	13	6	1	1	43	30	0	13	3	9	2	1
subtype3	17	18	1	1	38	50	1	35	1	22	1	1
subtype4	17	18	2	0	60	46	0	32	7	1	1	3
subtype5	12	3	1	0	22	17	0	5	2	6	3	1
subtype6	8	10	1	1	48	17	0	17	3	3	4	4
subtype7	11	15	0	1	74	31	0	14	4	6	3	2
subtype8	4	6	1	0	22	19	1	9	2	8	1	0
subtype9	5	4	0	1	29	20	0	15	0	6	4	0

Figure S111. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00018

Table S122. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	279	620	135	40
subtype1	11	42	10	4
subtype2	33	74	11	6
subtype3	53	85	47	2
subtype4	59	97	23	8
subtype5	18	44	5	6
subtype6	33	71	8	5
subtype7	39	105	12	6
subtype8	17	44	9	3
subtype9	16	58	10	0

Figure S112. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 8e-05 (Fisher's exact test), Q value = 0.00026

Table S123. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	508	356	118	75
subtype1	26	26	11	3
subtype2	57	39	14	11
subtype3	78	65	20	24
subtype4	87	74	21	1
subtype5	40	20	5	8
subtype6	54	41	12	6
subtype7	100	43	12	7
subtype8	29	25	8	9
subtype9	37	23	15	6

Figure S113. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.112 (Fisher's exact test), Q value = 0.18

Table S124. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	887	21
subtype1	62	1
subtype2	112	2
subtype3	135	1
subtype4	160	1
subtype5	62	3
subtype6	99	5
subtype7	139	3
subtype8	54	1
subtype9	64	4

Figure S114. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.134 (Fisher's exact test), Q value = 0.21

Table S125. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	1065	12
subtype1	66	2
subtype2	122	2
subtype3	187	0
subtype4	184	4
subtype5	72	1
subtype6	115	2
subtype7	163	0
subtype8	73	0
subtype9	83	1

Figure S115. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.738 (Fisher's exact test), Q value = 0.81

Table S126. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	441	543
subtype1	34	27
subtype2	47	63
subtype3	74	105
subtype4	78	90
subtype5	31	31
subtype6	47	64
subtype7	64	81
subtype8	32	41
subtype9	34	41

Figure S116. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S127. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	768	200	6	9	29	17	46
subtype1	0	51	4	0	0	4	5	4
subtype2	0	107	7	1	3	1	0	5
subtype3	0	68	110	0	1	4	1	3
subtype4	0	138	20	0	0	6	8	16
subtype5	0	56	13	0	0	3	0	1
subtype6	0	92	12	0	0	7	0	6
subtype7	1	143	2	5	2	2	0	7
subtype8	0	42	24	0	0	2	3	2
subtype9	0	71	8	0	3	0	0	2

Figure S117. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00337 (Kruskal-Wallis (anova)), Q value = 0.0076

Table S128. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	915	2.4 (4.6)
subtype1	58	2.4 (3.8)
subtype2	96	2.5 (5.1)
subtype3	170	3.5 (6.1)
subtype4	156	1.4 (2.2)
subtype5	66	2.5 (5.1)
subtype6	90	1.8 (3.2)
subtype7	147	1.4 (3.0)
subtype8	63	3.2 (5.8)
subtype9	69	3.3 (6.4)

Figure S118. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S129. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	61	182	745
subtype1	0	6	5	56
subtype2	1	13	12	80
subtype3	0	8	25	151
subtype4	0	7	22	133
subtype5	0	4	8	57
subtype6	0	5	12	82
subtype7	0	7	60	89
subtype8	0	7	24	41
subtype9	0	4	14	56

Figure S119. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.18 (Fisher's exact test), Q value = 0.26

Table S130. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	39	873
subtype1	0	64
subtype2	4	87
subtype3	4	165
subtype4	12	136
subtype5	4	59
subtype6	4	86
subtype7	5	138
subtype8	4	68
subtype9	2	70

Figure S120. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S131. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	135	94	59	121	145	114

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

P value = 0.967 (logrank test), Q value = 0.97

Table S132. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	664	86	0.0 - 216.8 (26.7)
subtype1	134	17	1.1 - 160.9 (21.5)
subtype2	94	8	0.2 - 110.6 (24.3)
subtype3	59	9	5.3 - 212.2 (31.4)
subtype4	121	19	2.8 - 139.2 (35.9)
subtype5	142	17	0.0 - 130.2 (24.5)
subtype6	114	16	1.6 - 216.8 (30.1)

Figure S121. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0786 (Kruskal-Wallis (anova)), Q value = 0.13

Table S133. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	653	58.3 (13.2)
subtype1	131	60.0 (14.5)
subtype2	92	60.5 (12.4)
subtype3	58	57.9 (11.5)
subtype4	118	56.4 (13.3)
subtype5	143	56.6 (12.7)
subtype6	111	58.9 (13.3)

Figure S122. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00027 (Fisher's exact test), Q value = 0.00076

Table S134. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	62	51	5	5	219	162	2	92	13	42	8	2
subtype1	10	12	0	1	49	32	1	19	2	8	0	1
subtype2	4	11	3	1	23	22	0	13	1	12	4	0
subtype3	7	6	1	0	12	20	0	9	1	1	1	0
subtype4	18	11	1	1	46	21	0	13	3	3	1	1
subtype5	11	6	0	1	63	33	0	22	3	3	1	0
subtype6	12	5	0	1	26	34	1	16	3	15	1	0

Figure S123. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00016

Table S135. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	184	379	85	19
subtype1	32	79	20	3
subtype2	23	50	18	3
subtype3	21	28	9	1
subtype4	54	57	6	4
subtype5	25	104	11	5
subtype6	29	61	21	3

Figure S124. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00066 (Fisher's exact test), Q value = 0.0018

Table S136. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	318	229	67	45
subtype1	65	43	14	8
subtype2	41	31	7	13
subtype3	27	25	6	1
subtype4	61	45	10	4
subtype5	82	39	21	3
subtype6	42	46	9	16

Figure S125. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.021 (Fisher's exact test), Q value = 0.04

Table S137. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	516	8
subtype1	114	0
subtype2	52	4
subtype3	45	1
subtype4	101	1
subtype5	115	1
subtype6	89	1

Figure S126. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.637 (Fisher's exact test), Q value = 0.74

Table S138. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	659	9
subtype1	132	3
subtype2	92	2
subtype3	59	0
subtype4	119	2
subtype5	143	2
subtype6	114	0

Figure S127. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.138 (Fisher's exact test), Q value = 0.21

Table S139. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	282	337
subtype1	70	54
subtype2	40	50
subtype3	19	33
subtype4	47	63
subtype5	57	74
subtype6	49	63

Figure S128. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S140. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	436	161	5	9	16	15	25
subtype1	0	96	17	0	0	5	9	8
subtype2	0	34	55	0	2	1	1	1
subtype3	0	36	14	0	3	1	0	5
subtype4	0	94	15	0	0	6	1	5
subtype5	1	128	2	5	4	2	0	3
subtype6	0	48	58	0	0	1	4	3

Figure S129. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0528 (Kruskal-Wallis (anova)), Q value = 0.091

Table S141. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	600	2.5 (5.0)
subtype1	111	2.4 (4.8)
subtype2	81	3.2 (5.7)
subtype3	56	2.1 (4.4)
subtype4	112	1.8 (4.0)
subtype5	132	1.5 (2.7)
subtype6	108	4.0 (7.4)

Figure S130. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S142. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	51	145	466
subtype1	15	25	95
subtype2	1	36	55
subtype3	2	8	48
subtype4	8	3	110
subtype5	15	56	72
subtype6	10	17	86

Figure S131. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.675 (Fisher's exact test), Q value = 0.77

Table S143. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	29	608
subtype1	6	125
subtype2	4	83
subtype3	3	53
subtype4	7	107
subtype5	3	135
subtype6	6	105

Figure S132. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S144. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	318	233	117

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

P value = 0.201 (logrank test), Q value = 0.28

Table S145. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	664	86	0.0 - 216.8 (26.7)
subtype1	314	49	0.2 - 160.9 (27.9)
subtype2	233	21	0.0 - 216.8 (22.4)
subtype3	117	16	2.8 - 212.2 (34.5)

Figure S133. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0465 (Kruskal-Wallis (anova)), Q value = 0.082

Table S146. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	653	58.3 (13.2)
subtype1	309	57.9 (13.7)
subtype2	228	59.8 (12.8)
subtype3	116	56.2 (12.4)

Figure S134. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00011 (Fisher's exact test), Q value = 0.00034

Table S147. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	62	51	5	5	219	162	2	92	13	42	8	2
subtype1	25	22	2	3	126	74	0	45	7	8	3	1
subtype2	17	18	3	2	59	60	2	33	5	30	3	0
subtype3	20	11	0	0	34	28	0	14	1	4	2	1

Figure S135. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00011 (Fisher's exact test), Q value = 0.00034

Table S148. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	184	379	85	19
subtype1	76	199	31	11
subtype2	60	122	46	5
subtype3	48	58	8	3

Figure S136. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00022 (Fisher's exact test), Q value = 0.00065

Table S149. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	318	229	67	45
subtype1	163	106	35	9
subtype2	100	76	23	31
subtype3	55	47	9	5

Figure S137. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.712 (Fisher's exact test), Q value = 0.79

Table S150. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	516	8
subtype1	257	3
subtype2	161	3
subtype3	98	2

Figure S138. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.67 (Fisher's exact test), Q value = 0.77

Table S151. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	659	9
subtype1	312	6
subtype2	231	2
subtype3	116	1

Figure S139. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.375 (Fisher's exact test), Q value = 0.47

Table S152. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	282	337
subtype1	139	152
subtype2	102	123
subtype3	41	62

Figure S140. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S153. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER, SPECIFY
ALL	1	436	161	5	9	16	15	25
subtype1	1	257	15	5	4	9	11	16
subtype2	0	91	127	0	5	2	3	5
subtype3	0	88	19	0	0	5	1	4

Figure S141. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00554 (Kruskal-Wallis (anova)), Q value = 0.012

Table S154. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	600	2.5 (5.0)
subtype1	282	1.8 (3.9)
subtype2	208	3.7 (6.7)
subtype3	110	1.9 (3.5)

Figure S142. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5e-05

Table S155. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	51	145	466
subtype1	26	80	210
subtype2	18	59	153
subtype3	7	6	103

Figure S143. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.821 (Fisher's exact test), Q value = 0.88

Table S156. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	29	608
subtype1	14	289
subtype2	9	214
subtype3	6	105

Figure S144. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/BRCA-TP/22595637/BRCA-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BRCA-TP/22506573/BRCA-TP.merged_data.txt
Number of patients = 1097
Number of clustering approaches = 12
Number of selected clinical features = 12
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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