Correlation between aggregated molecular cancer subtypes and selected clinical features

Breast Invasive Carcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1708042

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 1013 patients, 76 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 8 subtypes that correlate to 'AGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', and 'RACE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 5 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'RACE'.
5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', '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 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'HISTOLOGICAL.TYPE', 'NUMBER.OF.LYMPH.NODES', and 'RACE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY.T.STAGE' and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that correlate to 'AGE', 'PATHOLOGY.T.STAGE', 'HISTOLOGICAL.TYPE', and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death', 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', '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', 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'GENDER', 'HISTOLOGICAL.TYPE', 'NUMBER.OF.LYMPH.NODES', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'RACE'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'NUMBER.OF.LYMPH.NODES', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'RACE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', '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, 76 significant findings detected.


Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	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	0.049 (1.00)	3.15e-08 (4.51e-06)	0.0398 (1.00)	0.0365 (1.00)	0.00116 (0.0905)	0.46 (1.00)	0.0276 (1.00)	1e-05 (0.0014)	0.31 (1.00)	0.00993 (0.622)	0.0026 (0.179)	0.284 (1.00)
mRNA cHierClus subtypes	0.111 (1.00)	5.4e-10 (7.78e-08)	0.0451 (1.00)	0.00451 (0.3)	0.00545 (0.354)	0.398 (1.00)	0.0994 (1.00)	1e-05 (0.0014)	0.0113 (0.691)	0.0179 (1.00)	0.00106 (0.0837)	0.762 (1.00)
Copy Number Ratio CNMF subtypes	0.193 (1.00)	0.0132 (0.777)	0.0002 (0.0194)	1e-05 (0.0014)	0.00052 (0.0452)	0.0216 (1.00)	3e-05 (0.00333)	1e-05 (0.0014)	0.0533 (1.00)	0.00097 (0.0776)	0.00044 (0.0387)	0.901 (1.00)
METHLYATION CNMF	0.0338 (1.00)	9.76e-06 (0.00138)	4e-05 (0.00428)	0.0004 (0.0356)	0.00014 (0.0139)	0.816 (1.00)	0.281 (1.00)	1e-05 (0.0014)	0.81 (1.00)	0.000795 (0.0647)	2e-05 (0.00238)	0.782 (1.00)
RPPA CNMF subtypes	0.0557 (1.00)	0.0119 (0.713)	0.0333 (1.00)	0.00198 (0.145)	0.157 (1.00)	0.346 (1.00)	0.0548 (1.00)	3e-05 (0.00333)	0.725 (1.00)	0.551 (1.00)	0.0579 (1.00)	1 (1.00)
RPPA cHierClus subtypes	0.207 (1.00)	0.00228 (0.16)	0.0189 (1.00)	7e-05 (0.00721)	0.0902 (1.00)	0.329 (1.00)	0.0841 (1.00)	0.00025 (0.0235)	0.069 (1.00)	0.0409 (1.00)	0.00141 (0.107)	0.968 (1.00)
RNAseq CNMF subtypes	0.000632 (0.0538)	2.75e-06 (0.000391)	0.00015 (0.0147)	0.00039 (0.0351)	2e-05 (0.00238)	0.178 (1.00)	0.0388 (1.00)	1e-05 (0.0014)	0.0849 (1.00)	2.95e-05 (0.00331)	1e-05 (0.0014)	0.237 (1.00)
RNAseq cHierClus subtypes	0.00165 (0.123)	9.06e-05 (0.00915)	2e-05 (0.00238)	4e-05 (0.00428)	3e-05 (0.00333)	0.141 (1.00)	0.00078 (0.0647)	1e-05 (0.0014)	0.387 (1.00)	0.000319 (0.0291)	1e-05 (0.0014)	0.565 (1.00)
MIRSEQ CNMF	0.00988 (0.622)	0.0476 (1.00)	1e-05 (0.0014)	2e-05 (0.00238)	0.0002 (0.0194)	7e-05 (0.00721)	0.37 (1.00)	1e-05 (0.0014)	0.00125 (0.0962)	0.00447 (0.3)	1e-05 (0.0014)	0.371 (1.00)
MIRSEQ CHIERARCHICAL	0.021 (1.00)	0.00219 (0.155)	1e-05 (0.0014)	4e-05 (0.00428)	2e-05 (0.00238)	0.00025 (0.0235)	0.23 (1.00)	1e-05 (0.0014)	0.00067 (0.0563)	0.000783 (0.0647)	1e-05 (0.0014)	0.151 (1.00)
MIRseq Mature CNMF subtypes	0.00202 (0.145)	0.0367 (1.00)	1e-05 (0.0014)	2e-05 (0.00238)	3e-05 (0.00333)	0.00022 (0.0209)	0.448 (1.00)	1e-05 (0.0014)	0.00011 (0.011)	0.00375 (0.255)	1e-05 (0.0014)	0.269 (1.00)
MIRseq Mature cHierClus subtypes	0.0373 (1.00)	0.00801 (0.513)	5e-05 (0.0052)	0.00031 (0.0285)	2e-05 (0.00238)	0.00062 (0.0533)	0.0854 (1.00)	1e-05 (0.0014)	1e-05 (0.0014)	0.00181 (0.134)	1e-05 (0.0014)	0.137 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4	5	6	7	8
Number of samples	21	39	121	101	110	74	20	40

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.049 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	511	72	0.1 - 234.3 (27.2)
subtype1	20	3	0.3 - 92.0 (15.3)
subtype2	39	3	1.4 - 157.4 (43.4)
subtype3	116	18	0.2 - 188.7 (26.6)
subtype4	100	13	0.2 - 211.6 (27.5)
subtype5	108	11	0.3 - 234.3 (25.6)
subtype6	70	15	0.1 - 189.0 (30.3)
subtype7	19	3	1.0 - 97.5 (41.8)
subtype8	39	6	0.3 - 112.4 (27.5)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 3.15e-08 (Kruskal-Wallis (anova)), Q value = 4.5e-06

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

	nPatients	Mean (Std.Dev)
ALL	520	58.1 (13.2)
subtype1	20	60.7 (13.6)
subtype2	38	48.5 (10.6)
subtype3	119	58.3 (14.3)
subtype4	101	53.9 (12.5)
subtype5	110	62.6 (12.5)
subtype6	72	59.1 (12.3)
subtype7	20	60.4 (9.9)
subtype8	40	60.6 (12.0)

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0398 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	46	37	6	185	110	77	15	19	14	1	16
subtype1	1	0	0	8	3	5	1	0	2	0	1
subtype2	1	3	0	12	10	8	0	2	1	1	1
subtype3	8	6	4	36	34	20	6	3	1	0	3
subtype4	8	10	0	45	19	9	1	4	3	0	2
subtype5	14	14	0	43	15	10	5	2	3	0	4
subtype6	4	2	0	21	19	15	2	6	3	0	2
subtype7	4	1	0	6	4	4	0	0	0	0	1
subtype8	6	1	2	14	6	6	0	2	1	0	2

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

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

P value = 0.0365 (Fisher's exact test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	133	311	59	20
subtype1	6	13	0	2
subtype2	8	21	9	0
subtype3	25	75	14	6
subtype4	23	67	8	2
subtype5	40	55	9	6
subtype6	11	49	12	2
subtype7	7	10	3	0
subtype8	13	21	4	2

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 = 0.00116 (Fisher's exact test), Q value = 0.09

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

nPatients	N0	N1	N2	N3
ALL	255	170	61	29
subtype1	6	6	7	1
subtype2	14	16	5	3
subtype3	49	50	14	4
subtype4	64	24	8	5
subtype5	66	31	7	5
subtype6	24	26	14	8
subtype7	11	5	3	0
subtype8	21	12	3	3

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 = 0.46 (Fisher's exact test), Q value = 1

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

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	496	14	14
subtype1	0	19	2	0
subtype2	1	37	1	0
subtype3	0	116	1	4
subtype4	0	97	3	1
subtype5	1	101	3	5
subtype6	0	70	3	1
subtype7	0	19	0	1
subtype8	0	37	1	2

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 = 0.0276 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	520	6
subtype1	20	1
subtype2	39	0
subtype3	117	4
subtype4	101	0
subtype5	110	0
subtype6	74	0
subtype7	19	1
subtype8	40	0

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	448	41	1	12	2	21
subtype1	20	0	0	0	0	1
subtype2	28	8	0	3	0	0
subtype3	113	1	0	1	1	5
subtype4	92	1	1	0	0	6
subtype5	85	14	0	5	1	5
subtype6	67	5	0	0	0	2
subtype7	19	1	0	0	0	0
subtype8	24	11	0	3	0	2

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

'mRNA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.31 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	168	358
subtype1	3	18
subtype2	14	25
subtype3	35	86
subtype4	41	60
subtype5	33	77
subtype6	22	52
subtype7	8	12
subtype8	12	28

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

'mRNA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00993 (Kruskal-Wallis (anova)), Q value = 0.62

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	14	1.4 (2.2)
subtype2	34	1.9 (2.4)
subtype3	86	1.6 (2.3)
subtype4	93	1.4 (3.2)
subtype5	84	1.8 (3.9)
subtype6	47	2.7 (3.5)
subtype7	15	1.2 (2.5)
subtype8	35	2.7 (6.3)

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 = 0.0026 (Fisher's exact test), Q value = 0.18

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	2	0	14
subtype2	0	4	3	28
subtype3	0	10	12	74
subtype4	0	6	12	75
subtype5	0	2	5	81
subtype6	1	9	2	43
subtype7	0	1	5	11
subtype8	0	0	1	35

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

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.284 (Fisher's exact test), Q value = 1

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	0	14
subtype2	1	32
subtype3	0	83
subtype4	1	83
subtype5	2	70
subtype6	2	46
subtype7	1	14
subtype8	0	30

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	4	5
Number of samples	102	156	118	92	58

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.111 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	511	72	0.1 - 234.3 (27.2)
subtype1	98	15	0.2 - 234.3 (26.3)
subtype2	155	17	0.3 - 220.9 (31.2)
subtype3	112	21	0.1 - 189.0 (24.4)
subtype4	91	12	0.3 - 211.6 (28.6)
subtype5	55	7	0.3 - 129.6 (23.9)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 5.4e-10 (Kruskal-Wallis (anova)), Q value = 7.8e-08

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

	nPatients	Mean (Std.Dev)
ALL	520	58.1 (13.2)
subtype1	100	59.8 (13.1)
subtype2	154	55.2 (13.0)
subtype3	116	59.6 (12.2)
subtype4	92	53.5 (12.6)
subtype5	58	66.9 (11.9)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0451 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	46	37	6	185	110	77	15	19	14	1	16
subtype1	6	5	2	33	29	16	5	2	1	0	3
subtype2	20	12	4	56	24	23	1	6	3	1	6
subtype3	6	4	0	36	27	24	5	8	6	0	2
subtype4	8	9	0	40	18	8	1	3	3	0	2
subtype5	6	7	0	20	12	6	3	0	1	0	3

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

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

P value = 0.00451 (Fisher's exact test), Q value = 0.3

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	133	311	59	20
subtype1	19	63	14	5
subtype2	54	79	19	3
subtype3	18	78	15	7
subtype4	22	60	8	1
subtype5	20	31	3	4

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 = 0.00545 (Fisher's exact test), Q value = 0.35

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

nPatients	N0	N1	N2	N3
ALL	255	170	61	29
subtype1	44	43	9	4
subtype2	81	47	15	9
subtype3	41	39	25	10
subtype4	58	23	7	4
subtype5	31	18	5	2

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 = 0.398 (Fisher's exact test), Q value = 1

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

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	496	14	14
subtype1	0	97	1	4
subtype2	2	146	3	5
subtype3	0	111	6	1
subtype4	0	88	3	1
subtype5	0	54	1	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 = 0.0994 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	520	6
subtype1	98	4
subtype2	155	1
subtype3	117	1
subtype4	92	0
subtype5	58	0

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	448	41	1	12	2	21
subtype1	97	1	0	0	1	3
subtype2	113	29	0	7	0	7
subtype3	110	6	0	0	0	2
subtype4	84	1	1	0	0	5
subtype5	44	4	0	5	1	4

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

'mRNA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0113 (Fisher's exact test), Q value = 0.69

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

nPatients	NO	YES
ALL	168	358
subtype1	28	74
subtype2	46	110
subtype3	29	89
subtype4	40	52
subtype5	25	33

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

'mRNA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0179 (Kruskal-Wallis (anova)), Q value = 1

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	70	1.4 (1.9)
subtype2	135	2.0 (4.4)
subtype3	74	2.4 (3.4)
subtype4	86	1.5 (3.3)
subtype5	43	1.5 (2.7)

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 = 0.00106 (Fisher's exact test), Q value = 0.084

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	7	10	60
subtype2	0	6	8	128
subtype3	1	16	4	65
subtype4	0	3	12	70
subtype5	0	2	6	38

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

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.762 (Fisher's exact test), Q value = 1

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	0	66
subtype2	3	119
subtype3	2	74
subtype4	1	75
subtype5	1	38

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	375	234	92	253	41

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

P value = 0.193 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	974	114	0.0 - 234.3 (21.4)
subtype1	366	38	0.0 - 234.3 (23.1)
subtype2	226	29	0.1 - 162.1 (21.3)
subtype3	90	8	0.0 - 189.0 (19.2)
subtype4	251	31	0.0 - 211.6 (21.5)
subtype5	41	8	2.6 - 220.9 (21.6)

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 'AGE'

P value = 0.0132 (Kruskal-Wallis (anova)), Q value = 0.78

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

	nPatients	Mean (Std.Dev)
ALL	982	58.7 (13.1)
subtype1	372	58.7 (13.1)
subtype2	228	59.5 (13.9)
subtype3	90	61.1 (11.9)
subtype4	252	56.6 (12.6)
subtype5	40	61.2 (12.4)

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

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

P value = 2e-04 (Fisher's exact test), Q value = 0.019

Table S30. Clustering Approach #3: '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 III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	80	77	9	9	335	224	2	138	27	59	17	1	16
subtype1	40	43	7	7	103	83	1	44	6	27	5	0	9
subtype2	11	15	1	1	76	47	1	48	14	11	7	0	2
subtype3	6	2	0	1	30	26	0	11	4	9	2	0	1
subtype4	19	15	1	0	108	59	0	30	3	10	3	1	3
subtype5	4	2	0	0	18	9	0	5	0	2	0	0	1

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	258	581	117	36
subtype1	139	171	54	10
subtype2	46	151	23	14
subtype3	11	60	15	6
subtype4	48	175	23	5
subtype5	14	24	2	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 = 0.00052 (Fisher's exact test), Q value = 0.045

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

nPatients	N0	N1	N2	N3
ALL	468	330	111	70
subtype1	176	131	29	31
subtype2	90	80	42	15
subtype3	40	33	7	11
subtype4	145	69	28	11
subtype5	17	17	5	2

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 = 0.0216 (Fisher's exact test), Q value = 1

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

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	845	17	131
subtype1	1	304	5	65
subtype2	0	208	7	19
subtype3	0	76	2	14
subtype4	0	222	3	28
subtype5	1	35	0	5

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 = 3e-05 (Fisher's exact test), Q value = 0.0033

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

nPatients	FEMALE	MALE
ALL	984	11
subtype1	375	0
subtype2	224	10
subtype3	91	1
subtype4	253	0
subtype5	41	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 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	735	166	5	29	14	44
subtype1	0	221	107	0	15	10	22
subtype2	0	202	18	0	4	3	7
subtype3	0	55	31	0	5	0	1
subtype4	1	226	5	4	4	0	12
subtype5	0	31	5	1	1	1	2

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0533 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	282	713
subtype1	92	283
subtype2	72	162
subtype3	20	72
subtype4	83	170
subtype5	15	26

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00097 (Kruskal-Wallis (anova)), Q value = 0.078

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	837	2.4 (4.6)
subtype1	323	2.3 (4.6)
subtype2	177	2.7 (4.1)
subtype3	76	3.1 (5.5)
subtype4	223	1.9 (4.7)
subtype5	38	2.3 (4.2)

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 = 0.00044 (Fisher's exact test), Q value = 0.039

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	56	137	708
subtype1	1	16	44	286
subtype2	0	19	23	153
subtype3	0	4	6	72
subtype4	0	15	59	164
subtype5	0	2	5	33

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 = 0.901 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	36	796
subtype1	17	301
subtype2	7	179
subtype3	2	70
subtype4	9	211
subtype5	1	35

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	127	193	169	85	116

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0338 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	684	76	0.0 - 234.3 (21.3)
subtype1	127	18	0.2 - 211.6 (21.2)
subtype2	189	18	0.2 - 234.3 (22.1)
subtype3	168	26	0.0 - 173.0 (20.5)
subtype4	85	4	0.4 - 157.4 (16.9)
subtype5	115	10	0.0 - 194.3 (28.4)

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

'METHLYATION CNMF' versus 'AGE'

P value = 9.76e-06 (Kruskal-Wallis (anova)), Q value = 0.0014

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

	nPatients	Mean (Std.Dev)
ALL	679	58.1 (13.0)
subtype1	126	55.5 (12.2)
subtype2	190	58.2 (13.2)
subtype3	167	62.3 (12.6)
subtype4	82	55.9 (13.4)
subtype5	114	56.4 (12.3)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 4e-05 (Fisher's exact test), Q value = 0.0043

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

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	52	58	5	8	220	158	2	110	17	46	8	5
subtype1	6	8	0	0	61	31	0	12	1	3	3	1
subtype2	13	16	3	3	51	55	0	31	6	12	1	2
subtype3	9	10	0	1	48	38	1	38	8	11	3	2
subtype4	6	12	0	2	19	18	0	13	1	13	1	0
subtype5	18	12	2	2	41	16	1	16	1	7	0	0

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

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

P value = 4e-04 (Fisher's exact test), Q value = 0.036

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	183	397	88	20
subtype1	22	89	12	3
subtype2	55	108	24	5
subtype3	32	99	28	10
subtype4	25	47	12	1
subtype5	49	54	12	1

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 = 0.00014 (Fisher's exact test), Q value = 0.014

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	305	240	87	49
subtype1	76	35	11	4
subtype2	67	87	22	13
subtype3	67	55	32	11
subtype4	35	27	10	13
subtype5	60	36	12	8

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 = 0.816 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	CM0 (I+)	M0	M1	MX
ALL	1	562	8	119
subtype1	0	105	3	19
subtype2	1	160	1	31
subtype3	0	135	3	31
subtype4	0	68	1	16
subtype5	0	94	0	22

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.281 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	682	8
subtype1	127	0
subtype2	190	3
subtype3	166	3
subtype4	83	2
subtype5	116	0

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	473	142	5	24	12	32
subtype1	1	104	4	3	2	0	12
subtype2	0	117	44	0	14	7	11
subtype3	0	124	34	0	3	3	5
subtype4	0	51	29	0	3	1	1
subtype5	0	77	31	2	2	1	3

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

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.81 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	215	475
subtype1	44	83
subtype2	60	133
subtype3	54	115
subtype4	23	62
subtype5	34	82

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

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000795 (Kruskal-Wallis (anova)), Q value = 0.065

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	637	2.6 (4.8)
subtype1	120	1.5 (3.0)
subtype2	180	2.5 (4.4)
subtype3	149	3.2 (5.4)
subtype4	78	4.1 (6.2)
subtype5	110	2.1 (4.9)

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 = 0.0024

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	34	112	529
subtype1	0	5	43	76
subtype2	0	8	19	163
subtype3	0	12	23	130
subtype4	0	4	8	72
subtype5	1	5	19	88

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.782 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	35	590
subtype1	6	107
subtype2	10	165
subtype3	11	144
subtype4	2	73
subtype5	6	101

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
Number of samples	154	135	120

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0557 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	396	51	0.1 - 189.0 (28.7)
subtype1	147	25	0.1 - 186.5 (28.0)
subtype2	133	13	0.2 - 146.5 (25.3)
subtype3	116	13	0.3 - 189.0 (33.0)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0119 (Kruskal-Wallis (anova)), Q value = 0.71

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

	nPatients	Mean (Std.Dev)
ALL	407	57.9 (13.1)
subtype1	154	56.0 (13.3)
subtype2	134	60.3 (13.8)
subtype3	119	57.6 (11.6)

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

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0333 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	32	30	4	137	94	62	13	15	14	8
subtype1	7	8	1	55	36	24	2	10	8	3
subtype2	12	12	0	46	38	14	6	1	3	3
subtype3	13	10	3	36	20	24	5	4	3	2

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

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

P value = 0.00198 (Fisher's exact test), Q value = 0.14

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	94	246	50	18
subtype1	22	106	22	4
subtype2	34	83	11	6
subtype3	38	57	17	8

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 = 0.157 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	193	132	52	25
subtype1	72	45	22	12
subtype2	64	53	10	6
subtype3	57	34	20	7

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 = 0.346 (Fisher's exact test), Q value = 1

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

nPatients	CM0 (I+)	M0	M1	MX
ALL	1	387	14	7
subtype1	0	145	8	1
subtype2	0	128	3	4
subtype3	1	114	3	2

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 = 0.0548 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	404	5
subtype1	154	0
subtype2	131	4
subtype3	119	1

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.0033

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

nPatients	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	354	30	1	8	2	14
subtype1	141	4	1	1	0	7
subtype2	119	5	0	4	2	5
subtype3	94	21	0	3	0	2

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

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.725 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	144	265
subtype1	57	97
subtype2	44	91
subtype3	43	77

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

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.551 (Kruskal-Wallis (anova)), Q value = 1

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	333	1.8 (3.5)
subtype1	118	2.0 (3.6)
subtype2	112	1.2 (2.0)
subtype3	103	2.4 (4.5)

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 = 0.0579 (Fisher's exact test), Q value = 1

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	27	29	294
subtype1	1	16	14	98
subtype2	0	7	9	101
subtype3	0	4	6	95

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

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

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	6	297
subtype1	2	114
subtype2	2	98
subtype3	2	85

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	4	5
Number of samples	64	111	93	109	32

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.207 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	396	51	0.1 - 189.0 (28.7)
subtype1	62	8	0.3 - 129.7 (28.4)
subtype2	109	10	0.2 - 173.0 (30.4)
subtype3	91	13	0.3 - 189.0 (31.7)
subtype4	104	15	0.2 - 129.6 (26.9)
subtype5	30	5	0.1 - 76.8 (27.9)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00228 (Kruskal-Wallis (anova)), Q value = 0.16

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

	nPatients	Mean (Std.Dev)
ALL	407	57.9 (13.1)
subtype1	63	61.9 (13.8)
subtype2	110	57.7 (11.8)
subtype3	93	54.2 (12.9)
subtype4	109	59.3 (13.2)
subtype5	32	56.5 (13.9)

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

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0189 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE X
ALL	32	30	4	137	94	62	13	15	14	8
subtype1	9	6	1	21	12	5	5	0	2	3
subtype2	11	13	3	31	22	20	3	3	3	2
subtype3	4	6	0	43	20	11	1	3	3	2
subtype4	7	5	0	32	30	22	4	4	4	1
subtype5	1	0	0	10	10	4	0	5	2	0

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

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

P value = 7e-05 (Fisher's exact test), Q value = 0.0072

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	94	246	50	18
subtype1	22	32	3	6
subtype2	38	53	14	6
subtype3	14	68	10	1
subtype4	15	73	18	3
subtype5	5	20	5	2

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 = 0.0902 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	193	132	52	25
subtype1	35	19	5	3
subtype2	50	37	17	5
subtype3	53	25	11	4
subtype4	47	37	16	7
subtype5	8	14	3	6

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 = 0.329 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	CM0 (I+)	M0	M1	MX
ALL	1	387	14	7
subtype1	0	58	2	4
subtype2	1	105	3	2
subtype3	0	89	3	1
subtype4	0	105	4	0
subtype5	0	30	2	0

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 = 0.0841 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	404	5
subtype1	63	1
subtype2	111	0
subtype3	93	0
subtype4	105	4
subtype5	32	0

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00025 (Fisher's exact test), Q value = 0.023

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

nPatients	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	354	30	1	8	2	14
subtype1	59	1	0	3	0	1
subtype2	90	17	0	3	0	1
subtype3	86	0	1	0	0	6
subtype4	90	10	0	2	2	5
subtype5	29	2	0	0	0	1

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

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.069 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	144	265
subtype1	24	40
subtype2	42	69
subtype3	41	52
subtype4	29	80
subtype5	8	24

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

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0409 (Kruskal-Wallis (anova)), Q value = 1

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	333	1.8 (3.5)
subtype1	49	0.9 (1.9)
subtype2	98	2.3 (4.3)
subtype3	82	1.8 (3.6)
subtype4	82	1.6 (2.7)
subtype5	22	3.1 (4.2)

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 = 0.00141 (Fisher's exact test), Q value = 0.11

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	27	29	294
subtype1	0	3	4	46
subtype2	0	2	4	94
subtype3	0	7	13	65
subtype4	0	9	7	72
subtype5	1	6	1	17

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.968 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	6	297
subtype1	1	47
subtype2	2	78
subtype3	1	77
subtype4	2	71
subtype5	0	24

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	186	141	224	158	237	48	16

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.000632 (logrank test), Q value = 0.054

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	989	117	0.0 - 234.3 (21.5)
subtype1	185	24	0.2 - 211.6 (22.1)
subtype2	141	10	0.7 - 157.4 (25.9)
subtype3	220	18	0.2 - 234.3 (19.0)
subtype4	153	30	0.0 - 189.0 (20.9)
subtype5	228	30	0.1 - 188.7 (22.9)
subtype6	47	3	0.3 - 194.3 (18.4)
subtype7	15	2	2.3 - 130.2 (35.2)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 2.75e-06 (Kruskal-Wallis (anova)), Q value = 0.00039

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

	nPatients	Mean (Std.Dev)
ALL	997	58.7 (13.1)
subtype1	186	55.2 (12.0)
subtype2	139	55.5 (12.0)
subtype3	222	61.4 (12.5)
subtype4	153	59.9 (12.9)
subtype5	233	59.2 (14.4)
subtype6	48	61.2 (11.9)
subtype7	16	60.1 (15.1)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00015 (Fisher's exact test), Q value = 0.015

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	80	80	10	9	341	228	2	138	27	59	17	1	17
subtype1	13	14	0	0	89	39	0	17	2	6	3	0	2
subtype2	11	14	2	5	32	35	1	20	0	15	3	1	2
subtype3	25	24	2	1	78	43	0	24	7	11	3	0	6
subtype4	9	11	1	1	49	35	0	27	5	11	5	0	4
subtype5	15	12	5	0	73	62	1	43	13	8	2	0	3
subtype6	4	4	0	2	14	10	0	6	0	7	1	0	0
subtype7	3	1	0	0	6	4	0	1	0	1	0	0	0

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

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

P value = 0.00039 (Fisher's exact test), Q value = 0.035

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	264	589	118	36
subtype1	38	124	18	5
subtype2	45	69	26	0
subtype3	80	113	22	9
subtype4	34	99	17	8
subtype5	54	145	24	13
subtype6	9	28	10	1
subtype7	4	11	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 = 2e-05 (Fisher's exact test), Q value = 0.0024

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

nPatients	N0	N1	N2	N3
ALL	477	335	111	70
subtype1	118	48	13	7
subtype2	56	53	12	17
subtype3	116	71	20	15
subtype4	61	51	27	14
subtype5	91	95	35	9
subtype6	25	13	3	7
subtype7	10	4	1	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 = 0.178 (Fisher's exact test), Q value = 1

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

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	859	17	132
subtype1	0	164	3	19
subtype2	1	113	3	24
subtype3	1	188	3	32
subtype4	0	135	5	18
subtype5	0	211	2	24
subtype6	0	36	1	11
subtype7	0	12	0	4

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 = 0.0388 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	999	11
subtype1	186	0
subtype2	141	0
subtype3	222	2
subtype4	157	1
subtype5	229	8
subtype6	48	0
subtype7	16	0

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	749	167	5	29	14	44
subtype1	1	161	1	4	1	2	15
subtype2	0	71	56	0	10	1	3
subtype3	0	146	54	0	9	3	12
subtype4	0	142	10	0	2	0	4
subtype5	0	196	18	0	6	8	9
subtype6	0	19	27	0	1	0	1
subtype7	0	14	1	1	0	0	0

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

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0849 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	285	725
subtype1	59	127
subtype2	43	98
subtype3	53	171
subtype4	37	121
subtype5	78	159
subtype6	9	39
subtype7	6	10

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

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.95e-05 (Kruskal-Wallis (anova)), Q value = 0.0033

Table S89. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	850	2.3 (4.6)
subtype1	172	1.3 (2.9)
subtype2	128	3.3 (6.2)
subtype3	187	2.1 (4.2)
subtype4	112	3.4 (6.0)
subtype5	191	2.0 (3.3)
subtype6	45	3.0 (5.3)
subtype7	15	2.4 (5.9)

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 = 0.0014

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	57	139	718
subtype1	0	6	54	117
subtype2	0	5	11	119
subtype3	0	8	21	169
subtype4	1	22	17	94
subtype5	0	15	30	166
subtype6	0	0	1	43
subtype7	0	1	5	10

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.237 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	36	803
subtype1	6	157
subtype2	7	115
subtype3	11	170
subtype4	1	122
subtype5	8	188
subtype6	2	36
subtype7	1	15

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	174	229	194	176	181	56

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00165 (logrank test), Q value = 0.12

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	989	117	0.0 - 234.3 (21.5)
subtype1	173	22	0.2 - 211.6 (22.3)
subtype2	228	16	0.0 - 194.3 (24.8)
subtype3	188	19	0.2 - 234.3 (20.2)
subtype4	170	31	0.0 - 189.0 (20.8)
subtype5	176	20	0.1 - 188.7 (22.4)
subtype6	54	9	0.0 - 100.7 (15.6)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 9.06e-05 (Kruskal-Wallis (anova)), Q value = 0.0092

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

	nPatients	Mean (Std.Dev)
ALL	997	58.7 (13.1)
subtype1	174	55.2 (12.1)
subtype2	228	57.2 (12.7)
subtype3	193	60.4 (12.8)
subtype4	173	60.9 (12.5)
subtype5	175	59.5 (14.6)
subtype6	54	59.6 (12.9)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.0024

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	80	80	10	9	341	228	2	138	27	59	17	1	17
subtype1	12	15	0	0	83	36	0	15	2	5	3	0	2
subtype2	24	21	3	7	52	56	0	32	1	25	3	1	4
subtype3	19	21	1	0	68	39	0	24	7	9	1	0	5
subtype4	11	8	0	1	56	38	0	34	8	10	7	0	3
subtype5	12	11	5	1	60	48	1	27	8	4	2	0	2
subtype6	2	4	1	0	22	11	1	6	1	6	1	0	1

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

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

P value = 4e-05 (Fisher's exact test), Q value = 0.0043

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	264	589	118	36
subtype1	38	115	16	4
subtype2	69	111	45	3
subtype3	67	104	14	9
subtype4	29	119	18	10
subtype5	43	108	21	8
subtype6	18	32	4	2

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 = 3e-05 (Fisher's exact test), Q value = 0.0033

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

nPatients	N0	N1	N2	N3
ALL	477	335	111	70
subtype1	111	45	12	6
subtype2	102	77	20	27
subtype3	92	69	18	12
subtype4	74	50	34	12
subtype5	77	74	20	6
subtype6	21	20	7	7

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

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

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	859	17	132
subtype1	0	153	3	18
subtype2	2	183	3	41
subtype3	0	167	1	26
subtype4	0	146	7	23
subtype5	0	161	2	18
subtype6	0	49	1	6

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 = 0.00078 (Fisher's exact test), Q value = 0.065

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

nPatients	FEMALE	MALE
ALL	999	11
subtype1	174	0
subtype2	229	0
subtype3	193	1
subtype4	174	2
subtype5	173	8
subtype6	56	0

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	749	167	5	29	14	44
subtype1	1	153	1	4	1	0	13
subtype2	0	110	104	0	9	1	5
subtype3	0	130	32	0	10	8	14
subtype4	0	150	15	1	4	0	6
subtype5	0	155	10	0	5	5	6
subtype6	0	51	5	0	0	0	0

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

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.387 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	285	725
subtype1	57	117
subtype2	63	166
subtype3	55	139
subtype4	41	135
subtype5	56	125
subtype6	13	43

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

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000319 (Kruskal-Wallis (anova)), Q value = 0.029

Table S102. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	850	2.3 (4.6)
subtype1	161	1.3 (3.0)
subtype2	211	3.1 (5.7)
subtype3	170	2.2 (4.2)
subtype4	132	2.7 (4.3)
subtype5	136	1.7 (2.8)
subtype6	40	4.3 (8.1)

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 = 0.0014

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	57	139	718
subtype1	0	5	53	108
subtype2	0	6	16	195
subtype3	0	7	17	155
subtype4	0	13	21	115
subtype5	0	15	22	117
subtype6	1	11	10	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 = 0.565 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	36	803
subtype1	6	146
subtype2	9	185
subtype3	9	149
subtype4	4	136
subtype5	8	140
subtype6	0	47

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
Number of samples	281	425	287

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00988 (logrank test), Q value = 0.62

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	972	114	0.0 - 234.3 (21.4)
subtype1	279	20	0.0 - 194.3 (20.2)
subtype2	409	51	0.0 - 234.3 (23.1)
subtype3	284	43	0.0 - 211.6 (21.0)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0476 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	980	58.7 (13.1)
subtype1	278	58.4 (12.5)
subtype2	418	59.6 (13.3)
subtype3	284	57.4 (13.3)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	80	79	10	9	335	223	2	136	26	58	16	1	17
subtype1	24	29	6	8	66	65	1	39	6	32	2	0	3
subtype2	40	33	3	1	137	98	1	64	15	14	10	0	9
subtype3	16	17	1	0	132	60	0	33	5	12	4	1	5

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

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

P value = 2e-05 (Fisher's exact test), Q value = 0.0024

Table S109. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	263	576	115	36
subtype1	87	137	49	7
subtype2	121	244	40	20
subtype3	55	195	26	9

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 = 2e-04 (Fisher's exact test), Q value = 0.019

Table S110. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	470	329	109	68
subtype1	120	96	27	34
subtype2	188	152	55	20
subtype3	162	81	27	14

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 = 7e-05 (Fisher's exact test), Q value = 0.0072

Table S111. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	843	16	132
subtype1	1	217	2	61
subtype2	1	376	10	38
subtype3	0	250	4	33

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.37 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	982	11
subtype1	280	1
subtype2	419	6
subtype3	283	4

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	735	165	5	28	14	44
subtype1	0	138	125	0	7	4	7
subtype2	0	350	32	0	17	5	21
subtype3	1	247	8	5	4	5	16

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

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00125 (Fisher's exact test), Q value = 0.096

Table S114. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	278	715
subtype1	57	224
subtype2	140	285
subtype3	81	206

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

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00447 (Kruskal-Wallis (anova)), Q value = 0.3

Table S115. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	839	2.3 (4.6)
subtype1	251	3.3 (6.1)
subtype2	342	2.0 (3.4)
subtype3	246	1.9 (4.2)

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 = 0.0014

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	57	138	709
subtype1	0	11	32	227
subtype2	1	30	31	307
subtype3	0	16	75	175

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.371 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	36	795
subtype1	12	242
subtype2	17	313
subtype3	7	240

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
Number of samples	155	182	401	129	126

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.021 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	972	114	0.0 - 234.3 (21.4)
subtype1	154	11	0.0 - 170.2 (19.7)
subtype2	177	16	0.2 - 234.3 (21.6)
subtype3	388	51	0.0 - 189.0 (22.0)
subtype4	128	18	0.2 - 211.6 (23.6)
subtype5	125	18	0.1 - 194.3 (18.2)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00219 (Kruskal-Wallis (anova)), Q value = 0.16

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

	nPatients	Mean (Std.Dev)
ALL	980	58.7 (13.1)
subtype1	153	58.7 (12.4)
subtype2	180	59.7 (14.5)
subtype3	393	59.3 (13.0)
subtype4	129	54.5 (12.1)
subtype5	125	59.5 (12.5)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE TIS	STAGE X
ALL	80	79	10	9	335	223	2	136	26	58	16	1	17
subtype1	13	16	2	6	33	39	1	23	1	20	1	0	0
subtype2	16	17	4	0	59	47	0	26	8	2	0	0	3
subtype3	34	26	3	1	133	87	1	58	14	23	10	1	10
subtype4	7	14	0	0	63	24	0	11	0	5	1	0	3
subtype5	10	6	1	2	47	26	0	18	3	8	4	0	1

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

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

P value = 4e-05 (Fisher's exact test), Q value = 0.0043

Table S122. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	263	576	115	36
subtype1	48	71	35	1
subtype2	60	96	18	7
subtype3	94	243	41	22
subtype4	28	88	10	2
subtype5	33	78	11	4

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 = 2e-05 (Fisher's exact test), Q value = 0.0024

Table S123. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2	N3
ALL	470	329	109	68
subtype1	66	54	14	21
subtype2	82	74	19	2
subtype3	176	136	49	31
subtype4	85	30	8	5
subtype5	61	35	19	9

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 = 0.00025 (Fisher's exact test), Q value = 0.023

Table S124. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	843	16	132
subtype1	0	115	1	39
subtype2	1	159	0	22
subtype3	1	352	10	38
subtype4	0	113	1	15
subtype5	0	104	4	18

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.23 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	982	11
subtype1	155	0
subtype2	178	4
subtype3	395	6
subtype4	129	0
subtype5	125	1

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	735	165	5	28	14	44
subtype1	0	58	91	0	3	1	2
subtype2	0	135	20	0	9	6	12
subtype3	0	318	45	1	14	7	16
subtype4	1	114	1	4	1	0	7
subtype5	0	110	8	0	1	0	7

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00067 (Fisher's exact test), Q value = 0.056

Table S127. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	278	715
subtype1	22	133
subtype2	57	125
subtype3	122	279
subtype4	39	90
subtype5	38	88

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000783 (Kruskal-Wallis (anova)), Q value = 0.065

Table S128. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	839	2.3 (4.6)
subtype1	145	3.6 (6.2)
subtype2	149	1.5 (2.8)
subtype3	324	2.4 (4.2)
subtype4	115	1.3 (2.9)
subtype5	106	2.9 (5.9)

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 = 0.0014

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	57	138	709
subtype1	0	6	19	129
subtype2	0	7	17	132
subtype3	1	29	37	291
subtype4	0	5	44	74
subtype5	0	10	21	83

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

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

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	36	795
subtype1	6	139
subtype2	12	127
subtype3	11	315
subtype4	3	108
subtype5	4	106

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
Number of samples	250	227	194

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

P value = 0.00202 (logrank test), Q value = 0.15

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	659	76	0.0 - 194.3 (20.1)
subtype1	244	34	0.1 - 189.0 (21.8)
subtype2	224	14	0.0 - 194.3 (17.2)
subtype3	191	28	0.0 - 130.2 (20.2)

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 'AGE'

P value = 0.0367 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	658	58.4 (12.9)
subtype1	246	57.6 (13.7)
subtype2	222	60.4 (12.6)
subtype3	190	57.3 (12.1)

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

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	69	51	5	9	226	153	2	89	13	43	5	5
subtype1	34	23	1	2	81	59	0	34	6	6	2	2
subtype2	19	19	4	7	54	55	2	31	4	30	2	0
subtype3	16	9	0	0	91	39	0	24	3	7	1	3

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

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

P value = 2e-05 (Fisher's exact test), Q value = 0.0024

Table S135. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	196	379	77	19
subtype1	94	129	18	9
subtype2	60	120	43	4
subtype3	42	130	16	6

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 = 3e-05 (Fisher's exact test), Q value = 0.0033

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

nPatients	N0	N1	N2	N3
ALL	319	231	68	45
subtype1	117	96	26	7
subtype2	93	79	21	31
subtype3	109	56	21	7

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 = 0.00022 (Fisher's exact test), Q value = 0.021

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

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	543	5	121
subtype1	2	218	2	28
subtype2	0	165	2	60
subtype3	0	160	1	33

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 = 0.448 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	663	8
subtype1	246	4
subtype2	226	1
subtype3	191	3

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	464	139	5	19	14	29
subtype1	0	198	20	0	13	5	14
subtype2	0	98	112	0	5	7	5
subtype3	1	168	7	5	1	2	10

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

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00011 (Fisher's exact test), Q value = 0.011

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

nPatients	NO	YES
ALL	174	497
subtype1	81	169
subtype2	36	191
subtype3	57	137

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

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00375 (Kruskal-Wallis (anova)), Q value = 0.25

Table S141. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	602	2.5 (5.0)
subtype1	229	1.8 (3.7)
subtype2	205	3.6 (6.2)
subtype3	168	2.0 (4.6)

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 = 0.0014

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	52	112	501
subtype1	20	14	214
subtype2	11	37	176
subtype3	21	61	111

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 = 0.269 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	34	603
subtype1	16	221
subtype2	12	202
subtype3	6	180

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	4	5	6
Number of samples	118	66	188	123	110	66

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

P value = 0.0373 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	659	76	0.0 - 194.3 (20.1)
subtype1	117	16	0.2 - 114.2 (22.1)
subtype2	66	12	0.8 - 120.6 (35.7)
subtype3	183	23	0.9 - 189.0 (20.9)
subtype4	117	12	0.0 - 144.6 (18.9)
subtype5	110	4	0.0 - 170.2 (15.7)
subtype6	66	9	0.0 - 194.3 (15.4)

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 'AGE'

P value = 0.00801 (Kruskal-Wallis (anova)), Q value = 0.51

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

	nPatients	Mean (Std.Dev)
ALL	658	58.4 (12.9)
subtype1	118	56.6 (12.5)
subtype2	65	57.2 (12.7)
subtype3	182	56.5 (12.6)
subtype4	119	60.9 (14.1)
subtype5	109	60.6 (12.1)
subtype6	65	60.5 (13.2)

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

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

P value = 5e-05 (Fisher's exact test), Q value = 0.0052

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

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	69	51	5	9	226	153	2	89	13	43	5	5
subtype1	11	6	1	0	58	27	0	9	1	3	0	1
subtype2	12	7	0	1	19	15	0	7	0	4	1	0
subtype3	25	20	2	0	58	40	0	28	4	6	2	3
subtype4	8	5	1	1	44	36	1	17	4	6	0	0
subtype5	9	9	1	6	23	26	1	15	2	18	0	0
subtype6	4	4	0	1	24	9	0	13	2	6	2	1

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

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

P value = 0.00031 (Fisher's exact test), Q value = 0.029

Table S148. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	196	379	77	19
subtype1	29	76	10	3
subtype2	32	28	6	0
subtype3	62	101	17	8
subtype4	25	79	15	4
subtype5	30	52	26	2
subtype6	18	43	3	2

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 = 2e-05 (Fisher's exact test), Q value = 0.0024

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

nPatients	N0	N1	N2	N3
ALL	319	231	68	45
subtype1	74	34	7	3
subtype2	29	30	3	4
subtype3	94	61	22	7
subtype4	47	54	13	6
subtype5	47	35	9	19
subtype6	28	17	14	6

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 = 0.00062 (Fisher's exact test), Q value = 0.053

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

nPatients	CM0 (I+)	M0	M1	MX
ALL	2	543	5	121
subtype1	0	97	0	21
subtype2	0	55	1	10
subtype3	2	164	2	20
subtype4	0	100	0	23
subtype5	0	74	0	36
subtype6	0	53	2	11

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 = 0.0854 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	663	8
subtype1	118	0
subtype2	65	1
subtype3	185	3
subtype4	122	1
subtype5	110	0
subtype6	63	3

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	INFILTRATING CARCINOMA NOS	INFILTRATING DUCTAL CARCINOMA	INFILTRATING LOBULAR CARCINOMA	MEDULLARY CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	464	139	5	19	14	29
subtype1	1	102	1	4	0	0	10
subtype2	0	46	17	0	1	0	2
subtype3	0	155	16	1	10	1	5
subtype4	0	78	15	0	7	12	11
subtype5	0	25	83	0	1	1	0
subtype6	0	58	7	0	0	0	1

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

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients	NO	YES
ALL	174	497
subtype1	33	85
subtype2	17	49
subtype3	58	130
subtype4	41	82
subtype5	6	104
subtype6	19	47

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

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00181 (Kruskal-Wallis (anova)), Q value = 0.13

Table S154. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	602	2.5 (5.0)
subtype1	109	1.2 (2.5)
subtype2	63	2.5 (5.2)
subtype3	165	2.0 (3.8)
subtype4	108	2.3 (4.4)
subtype5	102	3.8 (6.5)
subtype6	55	4.4 (7.6)

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 = 0.0014

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	52	112	501
subtype1	5	45	67
subtype2	3	1	62
subtype3	20	17	150
subtype4	9	18	95
subtype5	5	17	86
subtype6	10	14	41

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 = 0.137 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	34	603
subtype1	6	105
subtype2	7	55
subtype3	6	170
subtype4	9	110
subtype5	5	99
subtype6	1	64

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

Methods & Data

Input

Cluster data file = BRCA-TP.mergedcluster.txt
Clinical data file = BRCA-TP.merged_data.txt
Number of patients = 1013
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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