Correlation between aggregated molecular cancer subtypes and selected clinical features

Breast Invasive Carcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1C53JV5

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 1080 patients, 95 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 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', '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', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', and 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', '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', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'NUMBER_OF_LYMPH_NODES', 'RACE', and 'ETHNICITY'.
6 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_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, 95 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	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	0.0515 (0.0757)	3.15e-08 (1.51e-06)	0.041 (0.0649)	0.0364 (0.0596)	0.0011 (0.00278)	0.673 (0.723)	0.0278 (0.0476)	1e-05 (4.11e-05)	0.307 (0.374)	0.00776 (0.016)	0.00249 (0.00588)	0.287 (0.353)
mRNA cHierClus subtypes	0.158 (0.204)	5.4e-10 (3.89e-08)	0.0285 (0.0482)	0.00406 (0.00886)	0.00523 (0.0111)	0.629 (0.686)	0.102 (0.14)	1e-05 (4.11e-05)	0.0113 (0.022)	0.0137 (0.0255)	0.00112 (0.00278)	0.763 (0.79)
Copy Number Ratio CNMF subtypes	0.141 (0.187)	0.000592 (0.00167)	0.046 (0.0705)	1e-05 (4.11e-05)	0.00312 (0.00702)	0.596 (0.655)	7e-05 (0.000246)	1e-05 (4.11e-05)	0.212 (0.268)	0.000622 (0.00172)	0.00065 (0.00177)	0.978 (0.985)
METHLYATION CNMF	0.0986 (0.136)	7.2e-07 (2.59e-05)	0.0106 (0.0212)	0.0177 (0.0318)	0.0007 (0.00186)	0.57 (0.638)	0.0194 (0.0337)	1e-05 (4.11e-05)	0.0326 (0.054)	0.000293 (0.000899)	1e-05 (4.11e-05)	0.785 (0.808)
RPPA CNMF subtypes	0.198 (0.252)	0.0119 (0.0228)	0.0164 (0.0299)	0.00109 (0.00278)	0.155 (0.201)	0.427 (0.492)	0.0562 (0.0818)	5e-05 (0.000185)	0.746 (0.779)	0.486 (0.551)	0.0508 (0.0754)	1 (1.00)
RPPA cHierClus subtypes	0.587 (0.65)	0.000142 (0.00047)	0.0108 (0.0214)	0.00233 (0.00569)	0.11 (0.15)	0.371 (0.437)	0.333 (0.4)	0.00046 (0.00135)	0.0811 (0.113)	0.0476 (0.0722)	0.00271 (0.00629)	0.684 (0.73)
RNAseq CNMF subtypes	0.0736 (0.104)	2.46e-05 (9.85e-05)	1e-05 (4.11e-05)	1e-05 (4.11e-05)	0.0001 (0.000343)	0.797 (0.814)	0.00399 (0.00884)	1e-05 (4.11e-05)	0.732 (0.772)	0.00247 (0.00588)	1e-05 (4.11e-05)	0.117 (0.157)
RNAseq cHierClus subtypes	9.37e-06 (4.11e-05)	4.28e-10 (3.89e-08)	1e-05 (4.11e-05)	1e-05 (4.11e-05)	1e-05 (4.11e-05)	0.253 (0.314)	0.0397 (0.0635)	1e-05 (4.11e-05)	0.0623 (0.0898)	3.12e-05 (0.000122)	1e-05 (4.11e-05)	0.318 (0.384)
MIRSEQ CNMF	0.134 (0.179)	0.00804 (0.0163)	1e-05 (4.11e-05)	1e-05 (4.11e-05)	0.0187 (0.0329)	0.939 (0.952)	0.234 (0.293)	1e-05 (4.11e-05)	1e-05 (4.11e-05)	0.0183 (0.0326)	1e-05 (4.11e-05)	0.0421 (0.0657)
MIRSEQ CHIERARCHICAL	0.0121 (0.0229)	0.000144 (0.00047)	7e-05 (0.000246)	0.00071 (0.00186)	0.00025 (8e-04)	0.149 (0.195)	0.0424 (0.0657)	1e-05 (4.11e-05)	1e-05 (4.11e-05)	0.000592 (0.00167)	1e-05 (4.11e-05)	0.374 (0.438)
MIRseq Mature CNMF subtypes	0.345 (0.411)	0.0378 (0.0611)	0.0028 (0.0064)	0.00038 (0.00114)	0.00487 (0.0105)	0.0683 (0.0973)	0.437 (0.5)	1e-05 (4.11e-05)	1e-05 (4.11e-05)	0.0312 (0.0522)	1e-05 (4.11e-05)	0.571 (0.638)
MIRseq Mature cHierClus subtypes	0.0502 (0.0754)	0.0158 (0.0291)	4e-05 (0.000152)	1e-05 (4.11e-05)	0.00029 (0.000899)	0.403 (0.468)	0.64 (0.693)	1e-05 (4.11e-05)	1e-05 (4.11e-05)	0.00588 (0.0123)	1e-05 (4.11e-05)	0.734 (0.772)

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 = 0.0515 (logrank test), Q value = 0.076

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	512	75	0.2 - 234.3 (31.0)
subtype1	20	3	0.3 - 92.0 (15.3)
subtype2	39	3	1.9 - 157.4 (43.7)
subtype3	116	19	0.2 - 188.7 (30.8)
subtype4	100	13	0.2 - 211.6 (31.9)
subtype5	108	12	0.3 - 234.3 (28.7)
subtype6	71	15	0.3 - 189.0 (31.0)
subtype7	19	3	1.0 - 99.1 (44.8)
subtype8	39	7	0.3 - 134.4 (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 'YEARS_TO_BIRTH'

P value = 3.15e-08 (Kruskal-Wallis (anova)), Q value = 1.5e-06

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	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: 'YEARS_TO_BIRTH'

'mRNA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.041 (Fisher's exact test), Q value = 0.065

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	50	33	6	184	111	77	15	19	14	1	13
subtype1	1	0	0	8	3	5	1	0	2	0	0
subtype2	2	2	0	12	10	8	0	2	1	1	1
subtype3	8	6	4	35	35	20	6	3	1	0	3
subtype4	9	9	0	45	19	9	1	4	3	0	1
subtype5	16	12	0	43	15	10	5	2	3	0	4
subtype6	4	2	0	21	19	15	2	6	3	0	1
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 = 0.0364 (Fisher's exact test), Q value = 0.06

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 = 0.0011 (Fisher's exact test), Q value = 0.0028

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 = 0.673 (Fisher's exact test), Q value = 0.72

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

nPatients	0	1
ALL	495	15
subtype1	19	2
subtype2	37	1
subtype3	115	2
subtype4	97	3
subtype5	101	3
subtype6	70	3
subtype7	19	0
subtype8	37	1

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 = 0.0278 (Fisher's exact test), Q value = 0.048

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 = 4.1e-05

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 = 0.307 (Fisher's exact test), Q value = 0.37

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 = 0.00776 (Kruskal-Wallis (anova)), Q value = 0.016

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 = 0.00249 (Fisher's exact test), Q value = 0.0059

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 = 0.287 (Fisher's exact test), Q value = 0.35

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 = 0.158 (logrank test), Q value = 0.2

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	512	75	0.2 - 234.3 (31.0)
subtype1	98	16	0.2 - 234.3 (32.0)
subtype2	155	19	0.3 - 220.9 (33.1)
subtype3	113	21	0.2 - 189.0 (25.1)
subtype4	91	12	0.3 - 211.6 (32.8)
subtype5	55	7	0.3 - 129.6 (24.6)

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 = 5.4e-10 (Kruskal-Wallis (anova)), Q value = 3.9e-08

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	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: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0285 (Fisher's exact test), Q value = 0.048

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	50	33	6	184	111	77	15	19	14	1	13
subtype1	6	5	2	32	30	16	5	2	1	0	3
subtype2	21	11	4	56	24	23	1	6	3	1	5
subtype3	6	4	0	36	27	24	5	8	6	0	1
subtype4	9	8	0	40	18	8	1	3	3	0	1
subtype5	8	5	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 = 0.00406 (Fisher's exact test), Q value = 0.0089

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 = 0.00523 (Fisher's exact test), Q value = 0.011

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 = 0.629 (Fisher's exact test), Q value = 0.69

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

nPatients	0	1
ALL	495	15
subtype1	96	2
subtype2	146	3
subtype3	111	6
subtype4	88	3
subtype5	54	1

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 = 0.102 (Fisher's exact test), Q value = 0.14

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 = 4.1e-05

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 = 0.022

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 = 0.0137 (Kruskal-Wallis (anova)), Q value = 0.026

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 = 0.00112 (Fisher's exact test), Q value = 0.0028

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 = 0.763 (Fisher's exact test), Q value = 0.79

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	271	400	97	251	43

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

P value = 0.141 (logrank test), Q value = 0.19

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	1044	128	0.0 - 234.3 (24.5)
subtype1	265	37	0.2 - 162.1 (23.1)
subtype2	392	42	0.0 - 234.3 (28.4)
subtype3	96	11	0.3 - 189.0 (21.0)
subtype4	248	30	0.2 - 211.6 (22.3)
subtype5	43	8	2.6 - 220.9 (23.5)

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 = 0.000592 (Kruskal-Wallis (anova)), Q value = 0.0017

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

	nPatients	Mean (Std.Dev)
ALL	1047	58.6 (13.2)
subtype1	264	59.0 (13.9)
subtype2	396	58.3 (13.0)
subtype3	96	62.0 (12.5)
subtype4	250	56.6 (12.6)
subtype5	41	63.6 (11.9)

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 'NEOPLASM_DISEASESTAGE'

P value = 0.046 (Fisher's exact test), Q value = 0.07

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	89	81	8	3	353	245	2	150	29	63	20	1	13
subtype1	13	17	2	0	88	64	1	48	13	14	8	0	3
subtype2	48	42	5	2	112	86	1	56	6	28	6	0	7
subtype3	6	4	0	1	33	28	0	9	4	7	2	0	2
subtype4	19	15	1	0	103	56	0	31	6	11	4	1	1
subtype5	3	3	0	0	17	11	0	6	0	3	0	0	0

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 = 4.1e-05

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

nPatients	T1	T2	T3	T4
ALL	272	616	130	40
subtype1	56	175	25	14
subtype2	142	183	65	9
subtype3	13	63	14	7
subtype4	48	170	22	9
subtype5	13	25	4	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 = 0.00312 (Fisher's exact test), Q value = 0.007

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

nPatients	N0	N1	N2	N3
ALL	501	350	117	75
subtype1	101	101	42	18
subtype2	198	129	34	31
subtype3	44	35	6	10
subtype4	141	67	30	13
subtype5	17	18	5	3

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 = 0.596 (Fisher's exact test), Q value = 0.65

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

nPatients	0	1
ALL	879	21
subtype1	233	9
subtype2	317	6
subtype3	79	2
subtype4	216	4
subtype5	34	0

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 = 0.00025

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

nPatients	FEMALE	MALE
ALL	1051	11
subtype1	261	10
subtype2	400	0
subtype3	96	1
subtype4	251	0
subtype5	43	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 = 4.1e-05

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	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	763	192	6	4	30	17	48
subtype1	0	228	24	0	0	6	4	9
subtype2	0	230	119	0	3	14	12	22
subtype3	0	53	36	0	0	5	0	3
subtype4	1	223	5	5	1	4	0	11
subtype5	0	29	8	1	0	1	1	3

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 = 0.212 (Fisher's exact test), Q value = 0.27

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

nPatients	NO	YES
ALL	288	774
subtype1	78	193
subtype2	93	307
subtype3	26	71
subtype4	77	174
subtype5	14	29

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 = 0.000622 (Kruskal-Wallis (anova)), Q value = 0.0017

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

	nPatients	Mean (Std.Dev)
ALL	897	2.4 (4.7)
subtype1	207	2.6 (4.0)
subtype2	352	2.3 (4.7)
subtype3	81	2.9 (5.1)
subtype4	218	2.0 (4.8)
subtype5	39	3.0 (5.9)

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 = 0.00065 (Fisher's exact test), Q value = 0.0018

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	170	736
subtype1	0	22	39	170
subtype2	1	18	51	306
subtype3	0	3	9	73
subtype4	0	15	64	155
subtype5	0	2	7	32

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 = 0.978 (Fisher's exact test), Q value = 0.99

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	858
subtype1	9	210
subtype2	17	332
subtype3	3	72
subtype4	8	208
subtype5	1	36

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	6
Number of samples	153	111	177	128	127	54

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0986 (logrank test), Q value = 0.14

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	746	86	0.0 - 234.3 (23.6)
subtype1	152	18	0.3 - 216.8 (23.6)
subtype2	111	17	0.0 - 160.9 (20.9)
subtype3	175	15	0.2 - 234.3 (22.4)
subtype4	128	11	0.0 - 134.4 (26.7)
subtype5	126	20	0.0 - 211.6 (23.3)
subtype6	54	5	7.3 - 157.4 (23.3)

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 = 7.2e-07 (Kruskal-Wallis (anova)), Q value = 2.6e-05

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

	nPatients	Mean (Std.Dev)
ALL	737	58.1 (13.1)
subtype1	147	56.8 (14.6)
subtype2	111	64.4 (11.4)
subtype3	175	58.9 (12.7)
subtype4	126	56.1 (12.5)
subtype5	126	55.7 (11.8)
subtype6	52	56.5 (13.8)

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

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0106 (Fisher's exact test), Q value = 0.021

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	61	60	4	2	237	176	2	121	19	50	11	5
subtype1	10	6	2	0	44	41	0	30	7	9	2	1
subtype2	7	11	0	1	31	25	1	22	2	8	2	1
subtype3	14	19	1	1	49	46	0	27	4	13	1	2
subtype4	19	10	1	0	41	25	1	20	3	7	1	0
subtype5	6	8	0	0	58	33	0	12	1	3	4	1
subtype6	5	6	0	0	14	6	0	10	2	10	1	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 = 0.0177 (Fisher's exact test), Q value = 0.032

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

nPatients	T1	T2	T3	T4
ALL	195	427	101	24
subtype1	31	94	19	8
subtype2	25	63	20	3
subtype3	53	95	24	4
subtype4	50	60	15	3
subtype5	21	87	14	4
subtype6	15	28	9	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 = 7e-04 (Fisher's exact test), Q value = 0.0019

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

nPatients	N0	N1	N2	N3
ALL	334	258	92	54
subtype1	50	67	22	10
subtype2	53	29	18	8
subtype3	72	71	18	13
subtype4	62	42	16	8
subtype5	77	33	11	5
subtype6	20	16	7	10

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 = 0.57 (Fisher's exact test), Q value = 0.64

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

nPatients	0	1
ALL	590	12
subtype1	127	3
subtype2	77	2
subtype3	140	1
subtype4	97	1
subtype5	104	4
subtype6	45	1

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0194 (Fisher's exact test), Q value = 0.034

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

nPatients	FEMALE	MALE
ALL	742	8
subtype1	151	2
subtype2	109	2
subtype3	176	1
subtype4	128	0
subtype5	127	0
subtype6	51	3

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 = 4.1e-05

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	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	498	164	6	3	25	15	37
subtype1	0	126	11	0	0	5	5	6
subtype2	0	67	37	0	0	4	0	3
subtype3	0	88	57	0	0	12	8	12
subtype4	0	78	40	2	2	2	1	3
subtype5	1	102	4	4	1	1	0	13
subtype6	0	37	15	0	0	1	1	0

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 = 0.0326 (Fisher's exact test), Q value = 0.054

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

nPatients	NO	YES
ALL	220	530
subtype1	59	94
subtype2	28	83
subtype3	50	127
subtype4	30	98
subtype5	42	85
subtype6	11	43

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 = 0.000293 (Kruskal-Wallis (anova)), Q value = 9e-04

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

	nPatients	Mean (Std.Dev)
ALL	690	2.6 (4.9)
subtype1	135	2.9 (4.8)
subtype2	101	3.1 (5.8)
subtype3	169	2.6 (4.7)
subtype4	117	2.1 (4.6)
subtype5	121	1.5 (2.9)
subtype6	47	4.9 (7.0)

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 = 1e-05 (Fisher's exact test), Q value = 4.1e-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	37	143	554
subtype1	0	14	26	110
subtype2	0	4	22	82
subtype3	0	4	15	156
subtype4	1	9	26	89
subtype5	0	4	47	73
subtype6	0	2	7	44

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.785 (Fisher's exact test), Q value = 0.81

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	37	647
subtype1	10	129
subtype2	7	97
subtype3	7	151
subtype4	7	112
subtype5	4	110
subtype6	2	48

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	153	137	120

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.198 (logrank test), Q value = 0.25

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	398	54	0.2 - 189.0 (31.8)
subtype1	147	25	0.2 - 186.5 (31.0)
subtype2	135	14	0.2 - 146.5 (27.2)
subtype3	116	15	0.3 - 189.0 (35.0)

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 = 0.0119 (Kruskal-Wallis (anova)), Q value = 0.023

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

	nPatients	Mean (Std.Dev)
ALL	408	57.9 (13.1)
subtype1	153	56.0 (13.4)
subtype2	136	60.3 (13.7)
subtype3	119	57.6 (11.6)

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

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0164 (Fisher's exact test), Q value = 0.03

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	36	27	4	136	95	62	13	15	14	6
subtype1	9	6	0	55	36	24	2	10	8	1
subtype2	13	12	1	45	39	14	6	1	3	3
subtype3	14	9	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 = 0.00109 (Fisher's exact test), Q value = 0.0028

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

nPatients	T1	T2	T3	T4
ALL	95	246	50	18
subtype1	21	106	22	4
subtype2	36	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 = 0.155 (Fisher's exact test), Q value = 0.2

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

nPatients	N0	N1	N2	N3
ALL	194	132	52	25
subtype1	71	45	22	12
subtype2	66	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 = 0.427 (Fisher's exact test), Q value = 0.49

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

nPatients	0	1
ALL	387	15
subtype1	144	8
subtype2	130	3
subtype3	113	4

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 = 0.0562 (Fisher's exact test), Q value = 0.082

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

nPatients	FEMALE	MALE
ALL	405	5
subtype1	153	0
subtype2	133	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 = 5e-05 (Fisher's exact test), Q value = 0.00018

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	355	30	1	8	2	14
subtype1	140	4	1	1	0	7
subtype2	121	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 = 0.746 (Fisher's exact test), Q value = 0.78

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

nPatients	NO	YES
ALL	145	265
subtype1	57	96
subtype2	45	92
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 = 0.486 (Kruskal-Wallis (anova)), Q value = 0.55

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

	nPatients	Mean (Std.Dev)
ALL	334	1.8 (3.5)
subtype1	117	2.0 (3.6)
subtype2	114	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 = 0.0508 (Fisher's exact test), Q value = 0.075

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	295
subtype1	1	16	14	97
subtype2	0	7	9	103
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	298
subtype1	2	113
subtype2	2	100
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	6
Number of samples	64	49	93	54	118	32

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.587 (logrank test), Q value = 0.65

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	398	54	0.2 - 189.0 (31.8)
subtype1	62	9	0.3 - 129.7 (31.8)
subtype2	48	5	0.3 - 162.1 (31.1)
subtype3	91	13	0.3 - 189.0 (32.8)
subtype4	54	7	0.2 - 173.0 (41.0)
subtype5	112	15	0.2 - 129.6 (28.4)
subtype6	31	5	0.3 - 102.0 (28.6)

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 = 0.000142 (Kruskal-Wallis (anova)), Q value = 0.00047

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

	nPatients	Mean (Std.Dev)
ALL	408	57.9 (13.1)
subtype1	63	61.9 (13.8)
subtype2	49	61.2 (11.7)
subtype3	93	54.2 (12.9)
subtype4	54	54.3 (11.1)
subtype5	117	59.4 (13.0)
subtype6	32	56.5 (13.9)

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

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0108 (Fisher's exact test), Q value = 0.021

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	36	27	4	136	95	62	13	15	14	6
subtype1	9	6	1	21	12	5	5	0	2	3
subtype2	7	5	0	17	8	8	2	2	0	0
subtype3	5	5	0	43	20	11	1	3	3	0
subtype4	5	6	2	10	12	12	1	1	3	2
subtype5	9	5	1	35	33	22	4	4	4	1
subtype6	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 = 0.00233 (Fisher's exact test), Q value = 0.0057

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

nPatients	T1	T2	T3	T4
ALL	95	246	50	18
subtype1	22	32	3	6
subtype2	16	24	7	2
subtype3	14	68	10	1
subtype4	18	25	7	4
subtype5	20	77	18	3
subtype6	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 = 0.11 (Fisher's exact test), Q value = 0.15

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

nPatients	N0	N1	N2	N3
ALL	194	132	52	25
subtype1	35	19	5	3
subtype2	26	14	6	2
subtype3	53	25	11	4
subtype4	21	18	11	3
subtype5	51	42	16	7
subtype6	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 = 0.371 (Fisher's exact test), Q value = 0.44

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

nPatients	0	1
ALL	387	15
subtype1	58	2
subtype2	49	0
subtype3	89	3
subtype4	47	4
subtype5	114	4
subtype6	30	2

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 = 0.333 (Fisher's exact test), Q value = 0.4

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

nPatients	FEMALE	MALE
ALL	405	5
subtype1	63	1
subtype2	49	0
subtype3	93	0
subtype4	54	0
subtype5	114	4
subtype6	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 = 0.00046 (Fisher's exact test), Q value = 0.0014

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	355	30	1	8	2	14
subtype1	59	1	0	3	0	1
subtype2	39	9	0	1	0	0
subtype3	86	0	1	0	0	6
subtype4	43	8	0	2	0	1
subtype5	99	10	0	2	2	5
subtype6	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 = 0.0811 (Fisher's exact test), Q value = 0.11

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

nPatients	NO	YES
ALL	145	265
subtype1	24	40
subtype2	22	27
subtype3	41	52
subtype4	17	37
subtype5	33	85
subtype6	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 = 0.0476 (Kruskal-Wallis (anova)), Q value = 0.072

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

	nPatients	Mean (Std.Dev)
ALL	334	1.8 (3.5)
subtype1	49	0.9 (1.9)
subtype2	46	2.1 (4.4)
subtype3	82	1.8 (3.6)
subtype4	45	2.6 (4.6)
subtype5	90	1.6 (2.6)
subtype6	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 = 0.00271 (Fisher's exact test), Q value = 0.0063

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	295
subtype1	0	3	4	46
subtype2	0	1	1	44
subtype3	0	7	13	65
subtype4	0	0	3	44
subtype5	0	10	7	79
subtype6	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 = 0.684 (Fisher's exact test), Q value = 0.73

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	6	298
subtype1	1	47
subtype2	0	36
subtype3	1	77
subtype4	2	36
subtype5	2	78
subtype6	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
Number of samples	558	305	213

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0736 (logrank test), Q value = 0.1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	1058	131	0.0 - 234.3 (24.6)
subtype1	545	70	0.2 - 234.3 (23.6)
subtype2	301	43	0.0 - 211.6 (23.7)
subtype3	212	18	0.0 - 216.8 (29.2)

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 = 2.46e-05 (Kruskal-Wallis (anova)), Q value = 9.9e-05

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

	nPatients	Mean (Std.Dev)
ALL	1061	58.6 (13.1)
subtype1	547	60.3 (13.5)
subtype2	302	57.1 (12.7)
subtype3	212	56.4 (12.3)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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	89	83	9	3	359	248	2	151	29	63	20	1	14
subtype1	46	42	6	0	178	126	1	91	22	23	12	0	10
subtype2	19	22	0	1	132	70	0	31	6	14	5	0	1
subtype3	24	19	3	2	49	52	1	29	1	26	3	1	3

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 = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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

nPatients	T1	T2	T3	T4
ALL	277	624	131	40
subtype1	146	323	60	27
subtype2	64	202	27	11
subtype3	67	99	44	2

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-04 (Fisher's exact test), Q value = 0.00034

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

nPatients	N0	N1	N2	N3
ALL	509	354	118	75
subtype1	248	192	75	30
subtype2	167	91	27	16
subtype3	94	71	16	29

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 = 0.797 (Fisher's exact test), Q value = 0.81

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

nPatients	0	1
ALL	892	21
subtype1	467	13
subtype2	262	5
subtype3	163	3

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 = 0.00399 (Fisher's exact test), Q value = 0.0088

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

nPatients	FEMALE	MALE
ALL	1065	11
subtype1	547	11
subtype2	305	0
subtype3	213	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 = 4.1e-05

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	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	776	194	6	4	29	17	48
subtype1	0	410	85	0	0	19	17	27
subtype2	1	266	7	6	4	2	0	18
subtype3	0	100	102	0	0	8	0	3

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 = 0.732 (Fisher's exact test), Q value = 0.77

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

nPatients	NO	YES
ALL	290	786
subtype1	156	402
subtype2	80	225
subtype3	54	159

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 = 0.00247 (Kruskal-Wallis (anova)), Q value = 0.0059

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

	nPatients	Mean (Std.Dev)
ALL	909	2.4 (4.6)
subtype1	452	2.1 (3.8)
subtype2	259	1.9 (4.3)
subtype3	198	3.5 (6.3)

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 = 4.1e-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	172	745
subtype1	0	33	66	392
subtype2	1	23	83	177
subtype3	0	5	23	176

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.117 (Fisher's exact test), Q value = 0.16

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	864
subtype1	25	430
subtype2	6	256
subtype3	7	178

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	291	163	90	289	184	59

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 9.37e-06 (logrank test), Q value = 4.1e-05

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	1058	131	0.0 - 234.3 (24.6)
subtype1	282	37	0.2 - 234.3 (23.2)
subtype2	160	31	0.0 - 189.0 (22.1)
subtype3	90	6	0.0 - 157.4 (35.3)
subtype4	287	23	0.2 - 216.8 (26.5)
subtype5	182	22	0.2 - 211.6 (24.3)
subtype6	57	12	0.0 - 100.7 (18.8)

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 = 4.28e-10 (Kruskal-Wallis (anova)), Q value = 3.9e-08

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

	nPatients	Mean (Std.Dev)
ALL	1061	58.6 (13.1)
subtype1	284	61.8 (13.9)
subtype2	159	61.8 (12.6)
subtype3	89	56.2 (12.4)
subtype4	288	56.4 (12.6)
subtype5	184	55.2 (12.1)
subtype6	57	59.9 (12.7)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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	89	83	9	3	359	248	2	151	29	63	20	1	14
subtype1	20	22	5	0	97	72	1	42	15	7	4	0	6
subtype2	7	8	0	1	50	37	0	31	8	11	7	0	2
subtype3	7	10	1	1	17	26	0	14	1	12	1	0	0
subtype4	41	23	3	1	86	61	0	42	0	22	3	1	5
subtype5	13	15	0	0	86	40	0	15	4	5	3	0	1
subtype6	1	5	0	0	23	12	1	7	1	6	2	0	0

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 = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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

nPatients	T1	T2	T3	T4
ALL	277	624	131	40
subtype1	76	168	27	18
subtype2	24	109	20	10
subtype3	26	36	27	1
subtype4	94	155	37	2
subtype5	39	122	16	6
subtype6	18	34	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 = 4.1e-05

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

nPatients	N0	N1	N2	N3
ALL	509	354	118	75
subtype1	124	115	32	12
subtype2	66	46	31	13
subtype3	38	35	5	12
subtype4	144	88	29	24
subtype5	116	49	13	6
subtype6	21	21	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 = 0.253 (Fisher's exact test), Q value = 0.31

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

nPatients	0	1
ALL	892	21
subtype1	249	5
subtype2	132	7
subtype3	69	1
subtype4	232	3
subtype5	159	3
subtype6	51	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 = 0.0397 (Fisher's exact test), Q value = 0.063

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

nPatients	FEMALE	MALE
ALL	1065	11
subtype1	283	8
subtype2	161	2
subtype3	90	0
subtype4	288	1
subtype5	184	0
subtype6	59	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 = 4.1e-05

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	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	776	194	6	4	29	17	48
subtype1	0	221	24	0	0	12	15	19
subtype2	0	136	15	0	1	4	1	6
subtype3	0	28	56	0	0	5	1	0
subtype4	0	178	93	1	0	7	0	10
subtype5	1	160	1	5	2	1	0	13
subtype6	0	53	5	0	1	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 = 0.0623 (Fisher's exact test), Q value = 0.09

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

nPatients	NO	YES
ALL	290	786
subtype1	91	200
subtype2	33	130
subtype3	18	72
subtype4	77	212
subtype5	57	127
subtype6	14	45

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 = 3.12e-05 (Kruskal-Wallis (anova)), Q value = 0.00012

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

	nPatients	Mean (Std.Dev)
ALL	909	2.4 (4.6)
subtype1	231	1.9 (3.3)
subtype2	118	3.1 (5.0)
subtype3	86	3.7 (6.5)
subtype4	263	2.4 (4.7)
subtype5	169	1.3 (2.9)
subtype6	42	4.3 (7.9)

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 = 4.1e-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	172	745
subtype1	0	19	39	197
subtype2	0	14	18	105
subtype3	0	3	9	75
subtype4	0	8	33	231
subtype5	0	5	61	109
subtype6	1	12	12	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 = 0.318 (Fisher's exact test), Q value = 0.38

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	864
subtype1	12	226
subtype2	3	126
subtype3	2	76
subtype4	15	231
subtype5	6	155
subtype6	0	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
Number of samples	302	207	270	281

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.134 (logrank test), Q value = 0.18

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	1042	128	0.0 - 234.3 (24.5)
subtype1	298	45	0.0 - 211.6 (22.6)
subtype2	207	22	0.0 - 170.2 (26.0)
subtype3	264	28	0.2 - 216.8 (23.9)
subtype4	273	33	0.3 - 234.3 (24.7)

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 = 0.00804 (Kruskal-Wallis (anova)), Q value = 0.016

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

	nPatients	Mean (Std.Dev)
ALL	1045	58.6 (13.2)
subtype1	301	58.0 (13.0)
subtype2	204	57.1 (12.8)
subtype3	266	60.6 (13.3)
subtype4	274	58.5 (13.3)

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

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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	89	83	9	3	354	243	2	148	28	62	19	1	14
subtype1	14	16	1	0	132	67	0	38	7	13	7	0	3
subtype2	24	20	1	2	44	56	1	30	3	22	3	1	0
subtype3	16	25	6	0	88	57	1	36	11	19	3	0	8
subtype4	35	22	1	1	90	63	0	44	7	8	6	0	3

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 = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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

nPatients	T1	T2	T3	T4
ALL	277	611	128	40
subtype1	48	210	31	11
subtype2	70	93	40	3
subtype3	65	155	34	15
subtype4	94	153	23	11

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 = 0.0187 (Fisher's exact test), Q value = 0.033

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

nPatients	N0	N1	N2	N3
ALL	503	349	115	73
subtype1	163	87	31	16
subtype2	86	80	17	23
subtype3	127	85	29	21
subtype4	127	97	38	13

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 = 0.939 (Fisher's exact test), Q value = 0.95

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

nPatients	0	1
ALL	877	20
subtype1	255	7
subtype2	157	3
subtype3	217	4
subtype4	248	6

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.234 (Fisher's exact test), Q value = 0.29

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

nPatients	FEMALE	MALE
ALL	1049	11
subtype1	297	5
subtype2	207	0
subtype3	268	2
subtype4	277	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 = 4.1e-05

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	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	763	191	6	4	29	17	48
subtype1	1	263	8	5	1	3	5	15
subtype2	0	89	102	0	3	3	3	7
subtype3	0	176	63	0	0	11	8	12
subtype4	0	235	18	1	0	12	1	14

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 = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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

nPatients	NO	YES
ALL	284	776
subtype1	81	221
subtype2	34	173
subtype3	62	208
subtype4	107	174

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 = 0.0183 (Kruskal-Wallis (anova)), Q value = 0.033

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

	nPatients	Mean (Std.Dev)
ALL	899	2.4 (4.6)
subtype1	252	2.0 (4.2)
subtype2	194	3.3 (6.0)
subtype3	216	2.5 (5.0)
subtype4	237	1.9 (3.1)

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 = 4.1e-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	171	737
subtype1	0	20	84	176
subtype2	0	11	29	161
subtype3	0	9	40	188
subtype4	1	21	18	212

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0421 (Fisher's exact test), Q value = 0.066

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	857
subtype1	4	255
subtype2	11	180
subtype3	10	208
subtype4	13	214

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
Number of samples	297	151	244	139	97	132

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0121 (logrank test), Q value = 0.023

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	1042	128	0.0 - 234.3 (24.5)
subtype1	289	43	0.0 - 173.0 (23.8)
subtype2	149	23	0.0 - 216.8 (20.0)
subtype3	244	25	0.0 - 170.2 (23.0)
subtype4	137	11	0.3 - 189.0 (31.2)
subtype5	93	9	0.2 - 234.3 (24.6)
subtype6	130	17	0.2 - 211.6 (27.5)

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 = 0.000144 (Kruskal-Wallis (anova)), Q value = 0.00047

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

	nPatients	Mean (Std.Dev)
ALL	1045	58.6 (13.2)
subtype1	292	58.3 (13.6)
subtype2	147	58.7 (12.4)
subtype3	239	59.5 (13.0)
subtype4	139	58.0 (12.0)
subtype5	96	63.3 (14.7)
subtype6	132	54.8 (12.2)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 7e-05 (Fisher's exact test), Q value = 0.00025

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	89	83	9	3	354	243	2	148	28	62	19	1	14
subtype1	18	23	2	0	97	65	1	48	8	17	7	1	8
subtype2	12	8	1	2	55	31	0	23	3	9	6	0	0
subtype3	16	27	3	1	62	66	1	39	3	23	1	0	2
subtype4	26	7	2	0	44	31	0	11	6	8	3	0	1
subtype5	9	6	1	0	32	23	0	17	6	1	1	0	1
subtype6	8	12	0	0	64	27	0	10	2	4	1	0	2

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 = 0.00071 (Fisher's exact test), Q value = 0.0019

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

nPatients	T1	T2	T3	T4
ALL	277	611	128	40
subtype1	69	181	33	12
subtype2	39	92	15	5
subtype3	65	127	47	4
subtype4	52	68	11	8
subtype5	25	53	12	7
subtype6	27	90	10	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 = 0.00025 (Fisher's exact test), Q value = 8e-04

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

nPatients	N0	N1	N2	N3
ALL	503	349	115	73
subtype1	119	105	40	22
subtype2	73	41	23	11
subtype3	110	83	23	25
subtype4	72	47	10	10
subtype5	43	40	11	1
subtype6	86	33	8	4

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 = 0.149 (Fisher's exact test), Q value = 0.2

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

nPatients	0	1
ALL	877	20
subtype1	250	8
subtype2	124	6
subtype3	185	1
subtype4	121	3
subtype5	84	1
subtype6	113	1

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0424 (Fisher's exact test), Q value = 0.066

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

nPatients	FEMALE	MALE
ALL	1049	11
subtype1	294	3
subtype2	148	3
subtype3	244	0
subtype4	137	2
subtype5	94	3
subtype6	132	0

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 = 4.1e-05

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	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	763	191	6	4	29	17	48
subtype1	0	249	22	1	0	10	1	14
subtype2	0	129	10	0	3	1	0	8
subtype3	0	101	131	0	1	3	3	5
subtype4	0	100	25	0	0	9	0	5
subtype5	0	69	2	0	0	5	13	8
subtype6	1	115	1	5	0	1	0	8

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 = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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

nPatients	NO	YES
ALL	284	776
subtype1	101	196
subtype2	41	110
subtype3	33	211
subtype4	42	97
subtype5	30	67
subtype6	37	95

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 = 0.000592 (Kruskal-Wallis (anova)), Q value = 0.0017

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

	nPatients	Mean (Std.Dev)
ALL	899	2.4 (4.6)
subtype1	231	2.5 (4.2)
subtype2	127	2.7 (5.4)
subtype3	221	3.0 (5.8)
subtype4	128	2.1 (4.6)
subtype5	75	1.8 (3.1)
subtype6	117	1.1 (2.5)

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 = 4.1e-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	171	737
subtype1	1	22	41	197
subtype2	0	13	24	100
subtype3	0	12	39	189
subtype4	0	5	8	117
subtype5	0	4	9	64
subtype6	0	5	50	70

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.374 (Fisher's exact test), Q value = 0.44

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	38	857
subtype1	7	227
subtype2	5	124
subtype3	10	220
subtype4	8	108
subtype5	5	64
subtype6	3	114

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
Number of samples	181	136	99	109	125

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

P value = 0.345 (logrank test), Q value = 0.41

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	642	71	0.0 - 216.8 (22.2)
subtype1	177	24	0.5 - 189.0 (22.1)
subtype2	134	16	0.0 - 131.7 (24.0)
subtype3	98	6	0.2 - 109.3 (21.6)
subtype4	109	14	0.3 - 120.6 (23.2)
subtype5	124	11	0.0 - 216.8 (20.3)

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 = 0.0378 (Kruskal-Wallis (anova)), Q value = 0.061

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

	nPatients	Mean (Std.Dev)
ALL	635	58.3 (13.1)
subtype1	173	58.3 (14.1)
subtype2	136	57.2 (12.9)
subtype3	97	60.9 (12.8)
subtype4	108	55.8 (12.2)
subtype5	121	59.4 (12.7)

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 'NEOPLASM_DISEASESTAGE'

P value = 0.0028 (Fisher's exact test), Q value = 0.0064

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	62	51	4	3	219	155	2	88	13	40	7	2
subtype1	20	17	1	1	65	39	0	26	4	5	1	1
subtype2	10	7	0	0	65	28	0	18	3	3	0	0
subtype3	5	12	3	1	25	22	0	14	1	13	3	0
subtype4	15	8	0	0	31	29	0	14	2	7	1	1
subtype5	12	7	0	1	33	37	2	16	3	12	2	0

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 = 0.00038 (Fisher's exact test), Q value = 0.0011

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

nPatients	T1	T2	T3	T4
ALL	181	370	79	19
subtype1	60	101	13	6
subtype2	27	95	9	5
subtype3	23	55	19	2
subtype4	42	51	14	2
subtype5	29	68	24	4

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 = 0.00487 (Fisher's exact test), Q value = 0.01

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

nPatients	N0	N1	N2	N3
ALL	312	222	64	43
subtype1	85	67	19	6
subtype2	81	35	17	3
subtype3	42	34	7	13
subtype4	48	43	10	7
subtype5	56	43	11	14

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 = 0.0683 (Fisher's exact test), Q value = 0.097

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

nPatients	0	1
ALL	505	7
subtype1	157	1
subtype2	109	0
subtype3	55	3
subtype4	84	1
subtype5	100	2

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 = 0.437 (Fisher's exact test), Q value = 0.5

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

nPatients	FEMALE	MALE
ALL	642	8
subtype1	177	4
subtype2	135	1
subtype3	97	2
subtype4	108	1
subtype5	125	0

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 = 4.1e-05

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	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	428	154	5	4	16	15	27
subtype1	0	146	12	0	0	10	6	7
subtype2	1	120	2	5	2	1	0	5
subtype3	0	39	52	0	0	2	4	2
subtype4	0	62	34	0	2	2	1	8
subtype5	0	61	54	0	0	1	4	5

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 = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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

nPatients	NO	YES
ALL	151	499
subtype1	66	115
subtype2	41	95
subtype3	4	95
subtype4	23	86
subtype5	17	108

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 = 0.0312 (Kruskal-Wallis (anova)), Q value = 0.052

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

	nPatients	Mean (Std.Dev)
ALL	583	2.5 (5.1)
subtype1	158	2.0 (3.9)
subtype2	127	1.4 (2.7)
subtype3	85	3.5 (6.2)
subtype4	102	2.7 (4.9)
subtype5	111	3.4 (7.1)

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 = 4.1e-05

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	51	134	457
subtype1	20	11	150
subtype2	10	50	74
subtype3	1	37	59
subtype4	4	13	90
subtype5	16	23	84

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 = 0.571 (Fisher's exact test), Q value = 0.64

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	28	591
subtype1	8	166
subtype2	3	126
subtype3	4	87
subtype4	7	95
subtype5	6	117

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	303	237	110

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

P value = 0.0502 (logrank test), Q value = 0.075

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	642	71	0.0 - 216.8 (22.2)
subtype1	295	41	0.2 - 160.9 (23.6)
subtype2	237	17	0.0 - 216.8 (20.2)
subtype3	110	13	1.5 - 189.0 (23.9)

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 = 0.0158 (Kruskal-Wallis (anova)), Q value = 0.029

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

	nPatients	Mean (Std.Dev)
ALL	635	58.3 (13.1)
subtype1	295	57.8 (13.7)
subtype2	231	60.0 (12.6)
subtype3	109	55.8 (12.3)

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 'NEOPLASM_DISEASESTAGE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00015

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	62	51	4	3	219	155	2	88	13	40	7	2
subtype1	27	22	1	1	125	66	0	42	7	7	2	1
subtype2	16	19	3	2	60	63	2	35	5	29	3	0
subtype3	19	10	0	0	34	26	0	11	1	4	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 = 1e-05 (Fisher's exact test), Q value = 4.1e-05

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

nPatients	T1	T2	T3	T4
ALL	181	370	79	19
subtype1	77	189	25	11
subtype2	57	127	48	5
subtype3	47	54	6	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 = 0.00029 (Fisher's exact test), Q value = 9e-04

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

nPatients	N0	N1	N2	N3
ALL	312	222	64	43
subtype1	158	97	35	8
subtype2	103	80	21	30
subtype3	51	45	8	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 = 0.403 (Fisher's exact test), Q value = 0.47

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

nPatients	0	1
ALL	505	7
subtype1	254	2
subtype2	161	3
subtype3	90	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 = 0.64 (Fisher's exact test), Q value = 0.69

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

nPatients	FEMALE	MALE
ALL	642	8
subtype1	299	4
subtype2	235	2
subtype3	108	2

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 = 4.1e-05

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	METAPLASTIC CARCINOMA	MIXED HISTOLOGY (PLEASE SPECIFY)	MUCINOUS CARCINOMA	OTHER SPECIFY
ALL	1	428	154	5	4	16	15	27
subtype1	1	248	15	5	0	10	9	15
subtype2	0	99	121	0	4	2	5	6
subtype3	0	81	18	0	0	4	1	6

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 = 4.1e-05

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

nPatients	NO	YES
ALL	151	499
subtype1	95	208
subtype2	19	218
subtype3	37	73

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 = 0.00588 (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	583	2.5 (5.1)
subtype1	270	1.8 (3.7)
subtype2	209	3.6 (6.6)
subtype3	104	2.1 (4.2)

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 = 4.1e-05

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	51	134	457
subtype1	27	61	213
subtype2	18	67	147
subtype3	6	6	97

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 = 0.734 (Fisher's exact test), Q value = 0.77

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	28	591
subtype1	13	275
subtype2	9	218
subtype3	6	98

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/15246568/BRCA-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BRCA-TP/15076769/BRCA-TP.merged_data.txt
Number of patients = 1080
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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