Correlation between aggregated molecular cancer subtypes and selected clinical features
Breast Invasive Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1DZ07GM
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/C1DZ07GM
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 1098 patients, 87 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 'Time to Death',  'YEARS_TO_BIRTH',  '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',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • 5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • 6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'GENDER', and 'HISTOLOGICAL_TYPE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  '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',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • 6 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 87 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		PATHOLOGIC
STAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER RADIATION
THERAPY 		HISTOLOGICAL
TYPE 		NUMBER
OF
LYMPH
NODES 		RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.017
(0.032) 		2.82e-08
(1.35e-06) 		0.11
(0.164) 		0.038
(0.0667) 		0.00088
(0.00256) 		0.673
(0.734) 		0.0271
(0.0493) 		0.55
(0.634) 		1e-05
(4.65e-05) 		0.00776
(0.016) 		0.00238
(0.00591) 		0.287
(0.373)
mRNA cHierClus subtypes 0.0579
(0.0926) 		4.76e-10
(3.43e-08) 		0.0321
(0.0578) 		0.00559
(0.012) 		0.00429
(0.00965) 		0.63
(0.714) 		0.1
(0.15) 		0.282
(0.369) 		1e-05
(4.65e-05) 		0.0137
(0.0266) 		0.00112
(0.0031) 		0.762
(0.807)
Copy Number Ratio CNMF subtypes 0.223
(0.303) 		0.000236
(0.000811) 		0.0432
(0.0735) 		1e-05
(4.65e-05) 		0.00423
(0.00965) 		0.681
(0.734) 		4e-05
(0.000165) 		0.68
(0.734) 		1e-05
(4.65e-05) 		0.00107
(0.00304) 		9e-05
(0.000341) 		0.994
(1.00)
METHLYATION CNMF 0.0171
(0.032) 		1.93e-06
(4.65e-05) 		0.00062
(0.00186) 		0.00231
(0.00584) 		0.00129
(0.0035) 		0.359
(0.449) 		0.0737
(0.117) 		0.635
(0.714) 		1e-05
(4.65e-05) 		0.00089
(0.00256) 		1e-05
(4.65e-05) 		0.883
(0.896)
RPPA CNMF subtypes 0.311
(0.4) 		0.00197
(0.00507) 		0.0121
(0.0239) 		0.0006
(0.00184) 		0.41
(0.496) 		0.481
(0.577) 		0.0413
(0.0717) 		0.15
(0.218) 		0.00012
(0.000443) 		0.236
(0.318) 		0.055
(0.09) 		1
(1.00)
RPPA cHierClus subtypes 0.499
(0.591) 		0.000127
(0.000459) 		0.00521
(0.0114) 		0.00343
(0.00797) 		0.117
(0.172) 		0.373
(0.461) 		0.334
(0.426) 		0.081
(0.125) 		0.00026
(0.000871) 		0.0476
(0.0798) 		0.00273
(0.00655) 		0.683
(0.734)
RNAseq CNMF subtypes 0.0992
(0.15) 		1.73e-05
(7.79e-05) 		1e-05
(4.65e-05) 		1e-05
(4.65e-05) 		0.00015
(0.000527) 		0.88
(0.896) 		0.00261
(0.00637) 		0.0807
(0.125) 		1e-05
(4.65e-05) 		0.00328
(0.00774) 		1e-05
(4.65e-05) 		0.188
(0.262)
RNAseq cHierClus subtypes 7.89e-06
(4.65e-05) 		1.37e-10
(1.97e-08) 		1e-05
(4.65e-05) 		1e-05
(4.65e-05) 		2e-05
(8.73e-05) 		0.279
(0.369) 		0.0188
(0.0348) 		0.178
(0.251) 		1e-05
(4.65e-05) 		3.58e-05
(0.000152) 		1e-05
(4.65e-05) 		0.549
(0.634)
MIRSEQ CNMF 0.352
(0.445) 		0.00663
(0.0138) 		1e-05
(4.65e-05) 		1e-05
(4.65e-05) 		0.0147
(0.0282) 		0.854
(0.884) 		0.163
(0.232) 		0.823
(0.859) 		1e-05
(4.65e-05) 		0.0376
(0.0667) 		1e-05
(4.65e-05) 		0.089
(0.136)
MIRSEQ CHIERARCHICAL 0.153
(0.221) 		4.98e-05
(0.000199) 		0.00033
(0.00108) 		0.00143
(0.00381) 		0.00039
(0.00125) 		0.219
(0.301) 		0.0493
(0.0816) 		0.41
(0.496) 		1e-05
(4.65e-05) 		0.00149
(0.0039) 		1e-05
(4.65e-05) 		0.374
(0.461)
MIRseq Mature CNMF subtypes 0.884
(0.896) 		0.0434
(0.0735) 		0.00942
(0.0188) 		0.00052
(0.00163) 		0.00473
(0.0105) 		0.199
(0.276) 		0.241
(0.321) 		0.621
(0.71) 		1e-05
(4.65e-05) 		0.0574
(0.0926) 		1e-05
(4.65e-05) 		0.534
(0.625)
MIRseq Mature cHierClus subtypes 0.501
(0.591) 		0.00639
(0.0135) 		1e-05
(4.65e-05) 		1e-05
(4.65e-05) 		8e-05
(0.000311) 		0.71
(0.757) 		0.668
(0.734) 		0.776
(0.815) 		1e-05
(4.65e-05) 		0.0084
(0.017) 		1e-05
(4.65e-05) 		0.67
(0.734)
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 = 0.017 (logrank test), Q value = 0.032

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

nPatients nDeath Duration Range (Median), Month
ALL 512 82 0.2 - 282.9 (33.1)
subtype1 20 3 0.3 - 92.0 (15.3)
subtype2 39 3 1.9 - 157.4 (50.7)
subtype3 116 20 0.2 - 188.7 (33.1)
subtype4 100 14 0.2 - 282.9 (33.9)
subtype5 108 14 0.3 - 281.3 (32.6)
subtype6 71 16 0.3 - 212.2 (32.8)
subtype7 19 4 1.0 - 109.0 (49.0)
subtype8 39 8 0.3 - 112.4 (32.9)

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 = 2.82e-08 (Kruskal-Wallis (anova)), Q value = 1.4e-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 53.8 (12.6)
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 'PATHOLOGIC_STAGE'

P value = 0.11 (Fisher's exact test), Q value = 0.16

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE TIS STAGE X
ALL 50 36 3 1 183 112 77 15 19 13 1 13
subtype1 1 0 0 0 8 3 5 1 0 2 0 0
subtype2 2 2 0 0 12 10 8 0 2 1 1 1
subtype3 8 7 3 0 35 35 20 6 3 1 0 3
subtype4 9 9 0 0 45 20 9 1 4 2 0 1
subtype5 16 12 0 1 42 15 10 5 2 3 0 4
subtype6 4 2 0 0 21 19 15 2 6 3 0 1
subtype7 4 1 0 0 6 4 4 0 0 0 0 1
subtype8 6 3 0 0 14 6 6 0 2 1 0 2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.038 (Fisher's exact test), Q value = 0.067

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

nPatients T1 T2 T3 T4
ALL 132 312 59 20
subtype1 5 14 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 = 0.00088 (Fisher's exact test), Q value = 0.0026

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

nPatients N0 N1 N2 N3
ALL 255 171 60 29
subtype1 6 6 7 1
subtype2 14 16 5 3
subtype3 49 50 14 4
subtype4 64 25 7 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 = 0.73

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 = 0.0271 (Fisher's exact test), Q value = 0.049

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 'RADIATION_THERAPY'

P value = 0.55 (Fisher's exact test), Q value = 0.63

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

nPatients NO YES
ALL 203 236
subtype1 7 6
subtype2 15 19
subtype3 55 49
subtype4 36 51
subtype5 45 46
subtype6 27 32
subtype7 6 10
subtype8 12 23

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 446 43 1 11 2 22
subtype1 20 0 0 0 0 1
subtype2 28 8 0 3 0 0
subtype3 113 1 0 0 1 6
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 22 13 0 3 0 2

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

'mRNA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00776 (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 = 0.00238 (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 = 0.37

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 = 0.0579 (logrank test), Q value = 0.093

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

nPatients nDeath Duration Range (Median), Month
ALL 512 82 0.2 - 282.9 (33.1)
subtype1 98 17 0.2 - 234.3 (33.0)
subtype2 155 22 0.3 - 281.3 (42.3)
subtype3 113 22 0.2 - 212.2 (27.1)
subtype4 91 13 0.3 - 282.9 (35.3)
subtype5 55 8 0.3 - 206.9 (28.3)

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

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 4.76e-10 (Kruskal-Wallis (anova)), Q value = 3.4e-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 53.4 (12.7)
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 'PATHOLOGIC_STAGE'

P value = 0.0321 (Fisher's exact test), Q value = 0.058

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE TIS STAGE X
ALL 50 36 3 1 183 112 77 15 19 13 1 13
subtype1 6 6 1 0 32 30 16 5 2 1 0 3
subtype2 21 13 2 0 56 24 23 1 6 3 1 5
subtype3 6 4 0 0 36 27 24 5 8 6 0 1
subtype4 9 8 0 0 40 19 8 1 3 2 0 1
subtype5 8 5 0 1 19 12 6 3 0 1 0 3

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00559 (Fisher's exact test), Q value = 0.012

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

nPatients T1 T2 T3 T4
ALL 132 312 59 20
subtype1 19 63 14 5
subtype2 54 79 19 3
subtype3 18 78 15 7
subtype4 22 60 8 1
subtype5 19 32 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 = 0.00429 (Fisher's exact test), Q value = 0.0097

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

nPatients N0 N1 N2 N3
ALL 255 171 60 29
subtype1 44 43 9 4
subtype2 81 47 15 9
subtype3 41 39 25 10
subtype4 58 24 6 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 = 0.63 (Fisher's exact test), Q value = 0.71

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 = 0.1 (Fisher's exact test), Q value = 0.15

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 'RADIATION_THERAPY'

P value = 0.282 (Fisher's exact test), Q value = 0.37

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

nPatients NO YES
ALL 203 236
subtype1 44 43
subtype2 63 66
subtype3 47 47
subtype4 30 49
subtype5 19 31

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 446 43 1 11 2 22
subtype1 97 1 0 0 1 3
subtype2 111 31 0 7 0 7
subtype3 110 6 0 0 0 2
subtype4 84 1 1 0 0 5
subtype5 44 4 0 4 1 5

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

'mRNA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0137 (Kruskal-Wallis (anova)), Q value = 0.027

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 = 0.0031

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 = 0.81

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 275 407 98 257 43
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.223 (logrank test), Q value = 0.3

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

nPatients nDeath Duration Range (Median), Month
ALL 1064 147 0.0 - 282.9 (27.9)
subtype1 271 37 0.2 - 162.1 (24.8)
subtype2 399 50 0.0 - 281.3 (32.5)
subtype3 98 14 0.3 - 212.2 (24.2)
subtype4 254 37 0.2 - 282.9 (25.9)
subtype5 42 9 2.6 - 275.9 (26.0)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000236 (Kruskal-Wallis (anova)), Q value = 0.00081

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

nPatients Mean (Std.Dev)
ALL 1065 58.6 (13.2)
subtype1 268 59.1 (13.9)
subtype2 403 58.4 (13.0)
subtype3 97 62.1 (12.5)
subtype4 256 56.3 (12.8)
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 'PATHOLOGIC_STAGE'

P value = 0.0432 (Fisher's exact test), Q value = 0.074

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE TIS STAGE X
ALL 89 85 6 6 353 252 2 154 29 65 20 1 13
subtype1 13 18 2 1 88 66 1 48 13 14 8 0 3
subtype2 48 44 4 3 112 87 1 58 6 29 7 0 7
subtype3 6 4 0 1 32 29 0 9 4 8 2 0 2
subtype4 19 16 0 1 104 59 0 33 6 11 3 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: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients T1 T2 T3 T4
ALL 276 624 136 40
subtype1 57 177 26 14
subtype2 144 185 68 9
subtype3 12 64 15 7
subtype4 50 173 23 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 = 0.00423 (Fisher's exact test), Q value = 0.0097

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

nPatients N0 N1 N2 N3
ALL 507 358 119 77
subtype1 104 102 42 18
subtype2 199 133 35 32
subtype3 44 35 6 11
subtype4 143 70 31 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 = 0.681 (Fisher's exact test), Q value = 0.73

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

nPatients 0 1
ALL 889 22
subtype1 234 9
subtype2 320 7
subtype3 80 2
subtype4 221 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 = 4e-05 (Fisher's exact test), Q value = 0.00016

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

nPatients FEMALE MALE
ALL 1068 12
subtype1 264 11
subtype2 407 0
subtype3 97 1
subtype4 257 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 'RADIATION_THERAPY'

P value = 0.68 (Fisher's exact test), Q value = 0.73

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

nPatients NO YES
ALL 429 518
subtype1 115 130
subtype2 161 192
subtype3 32 53
subtype4 105 124
subtype5 16 19

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA METAPLASTIC CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 770 200 6 9 29 17 47
subtype1 0 230 26 0 0 6 4 9
subtype2 0 229 123 0 6 14 12 23
subtype3 0 53 37 0 0 5 0 3
subtype4 1 229 6 5 3 4 0 8
subtype5 0 29 8 1 0 0 1 4

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00107 (Kruskal-Wallis (anova)), Q value = 0.003

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

nPatients Mean (Std.Dev)
ALL 914 2.4 (4.7)
subtype1 210 2.6 (4.0)
subtype2 359 2.3 (4.7)
subtype3 82 3.0 (5.1)
subtype4 224 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 = 9e-05 (Fisher's exact test), Q value = 0.00034

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 60 181 745
subtype1 0 22 41 172
subtype2 1 18 54 312
subtype3 0 3 9 74
subtype4 0 15 70 155
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 = 0.994 (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 39 875
subtype1 9 214
subtype2 17 339
subtype3 3 73
subtype4 9 213
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 147 101 216 123 137 59
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0171 (logrank test), Q value = 0.032

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

nPatients nDeath Duration Range (Median), Month
ALL 781 102 0.0 - 282.9 (27.1)
subtype1 147 18 0.0 - 162.1 (23.9)
subtype2 101 20 0.3 - 160.9 (27.1)
subtype3 215 21 0.2 - 263.3 (25.2)
subtype4 123 16 1.1 - 216.8 (32.5)
subtype5 136 25 0.0 - 282.9 (29.9)
subtype6 59 2 0.3 - 157.4 (28.9)

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 = 1.93e-06 (Kruskal-Wallis (anova)), Q value = 4.6e-05

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

nPatients Mean (Std.Dev)
ALL 770 58.2 (13.1)
subtype1 142 57.1 (14.7)
subtype2 101 63.7 (11.9)
subtype3 213 59.8 (13.0)
subtype4 121 56.8 (12.1)
subtype5 136 55.7 (12.2)
subtype6 57 53.8 (12.4)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00062 (Fisher's exact test), Q value = 0.0019

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 61 62 5 6 244 187 2 126 19 53 11 5
subtype1 7 6 1 0 44 39 0 28 9 9 2 1
subtype2 7 9 0 2 27 22 1 20 1 9 2 1
subtype3 17 23 2 2 58 56 0 37 5 13 1 2
subtype4 18 9 0 2 43 21 1 18 2 7 2 0
subtype5 6 8 1 0 61 37 0 14 2 3 3 1
subtype6 6 7 1 0 11 12 0 9 0 12 1 0

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00231 (Fisher's exact test), Q value = 0.0058

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

nPatients T1 T2 T3 T4
ALL 201 446 109 24
subtype1 26 91 19 10
subtype2 22 58 19 2
subtype3 63 115 32 5
subtype4 46 61 14 2
subtype5 24 92 15 5
subtype6 20 29 10 0

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 = 0.00129 (Fisher's exact test), Q value = 0.0035

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

nPatients N0 N1 N2 N3
ALL 350 269 95 57
subtype1 52 61 21 10
subtype2 47 25 17 9
subtype3 89 85 24 13
subtype4 59 41 15 8
subtype5 81 38 12 5
subtype6 22 19 6 12

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 = 0.359 (Fisher's exact test), Q value = 0.45

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

nPatients 0 1
ALL 609 13
subtype1 121 2
subtype2 70 3
subtype3 168 1
subtype4 92 2
subtype5 111 4
subtype6 47 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 = 0.0737 (Fisher's exact test), Q value = 0.12

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

nPatients FEMALE MALE
ALL 774 9
subtype1 145 2
subtype2 98 3
subtype3 214 2
subtype4 123 0
subtype5 137 0
subtype6 57 2

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.635 (Fisher's exact test), Q value = 0.71

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

nPatients NO YES
ALL 309 391
subtype1 61 74
subtype2 38 53
subtype3 90 98
subtype4 49 64
subtype5 54 69
subtype6 17 33

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA METAPLASTIC CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 513 178 6 9 25 15 35
subtype1 0 123 11 0 0 5 4 4
subtype2 0 59 35 0 0 4 0 3
subtype3 0 109 70 0 0 11 10 16
subtype4 0 76 38 2 2 3 0 2
subtype5 1 110 4 4 7 1 0 9
subtype6 0 36 20 0 0 1 1 1

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

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00089 (Kruskal-Wallis (anova)), Q value = 0.0026

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

nPatients Mean (Std.Dev)
ALL 718 2.6 (4.9)
subtype1 131 2.9 (4.9)
subtype2 90 3.4 (6.1)
subtype3 203 2.4 (4.5)
subtype4 113 2.3 (4.8)
subtype5 129 1.5 (2.9)
subtype6 52 4.7 (6.8)

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 = 4.6e-05

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 38 160 569
subtype1 0 14 34 97
subtype2 0 4 20 74
subtype3 0 5 20 188
subtype4 1 8 25 86
subtype5 0 4 52 78
subtype6 0 3 9 46

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.883 (Fisher's exact test), Q value = 0.9

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 38 676
subtype1 8 127
subtype2 7 88
subtype3 8 185
subtype4 5 107
subtype5 7 117
subtype6 3 52

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 156 126 128
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.311 (logrank test), Q value = 0.4

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

nPatients nDeath Duration Range (Median), Month
ALL 398 59 0.2 - 282.9 (34.0)
subtype1 150 27 0.2 - 282.9 (32.7)
subtype2 122 16 0.2 - 263.3 (41.0)
subtype3 126 16 0.3 - 165.8 (33.1)

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 = 0.00197 (Kruskal-Wallis (anova)), Q value = 0.0051

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 155 56.1 (13.2)
subtype2 125 56.8 (12.1)
subtype3 128 61.1 (13.4)

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

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0121 (Fisher's exact test), Q value = 0.024

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

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 36 29 2 135 97 62 13 15 13 6
subtype1 8 7 0 56 40 23 2 10 7 1
subtype2 16 11 2 34 22 26 6 4 3 2
subtype3 12 11 0 45 35 13 5 1 3 3

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 6e-04 (Fisher's exact test), Q value = 0.0018

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

nPatients T1 T2 T3 T4
ALL 94 247 50 18
subtype1 22 108 22 4
subtype2 41 58 18 9
subtype3 31 81 10 5

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 = 0.41 (Fisher's exact test), Q value = 0.5

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

nPatients N0 N1 N2 N3
ALL 194 133 51 25
subtype1 71 50 20 12
subtype2 58 38 21 7
subtype3 65 45 10 6

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.481 (Fisher's exact test), Q value = 0.58

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

nPatients 0 1
ALL 387 15
subtype1 147 8
subtype2 119 4
subtype3 121 3

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 = 0.0413 (Fisher's exact test), Q value = 0.072

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

nPatients FEMALE MALE
ALL 405 5
subtype1 156 0
subtype2 125 1
subtype3 124 4

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.15 (Fisher's exact test), Q value = 0.22

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

nPatients NO YES
ALL 161 188
subtype1 59 75
subtype2 43 62
subtype3 59 51

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00012 (Fisher's exact test), Q value = 0.00044

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

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 354 31 1 7 2 15
subtype1 143 4 1 1 0 7
subtype2 101 21 0 2 0 2
subtype3 110 6 0 4 2 6

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

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.236 (Kruskal-Wallis (anova)), Q value = 0.32

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 119 2.0 (3.5)
subtype2 108 2.4 (4.4)
subtype3 107 1.1 (2.1)

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 = 0.055 (Fisher's exact test), Q value = 0.09

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 17 13 100
subtype2 0 4 8 98
subtype3 0 6 8 97

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 2 116
subtype2 2 89
subtype3 2 93

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 = 0.499 (logrank test), Q value = 0.59

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

nPatients nDeath Duration Range (Median), Month
ALL 398 59 0.2 - 282.9 (34.0)
subtype1 63 10 0.3 - 129.7 (37.2)
subtype2 48 6 0.3 - 165.8 (32.7)
subtype3 91 15 0.3 - 282.9 (33.7)
subtype4 54 7 0.2 - 263.3 (43.0)
subtype5 111 16 0.2 - 134.1 (32.0)
subtype6 31 5 0.3 - 102.0 (37.0)

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 = 0.000127 (Kruskal-Wallis (anova)), Q value = 0.00046

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 54.1 (13.0)
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 'PATHOLOGIC_STAGE'

P value = 0.00521 (Fisher's exact test), Q value = 0.011

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

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 36 29 2 135 97 62 13 15 13 6
subtype1 9 7 0 21 12 5 5 0 2 3
subtype2 7 5 0 17 8 8 2 2 0 0
subtype3 5 5 0 43 21 11 1 3 2 0
subtype4 5 6 2 10 12 12 1 1 3 2
subtype5 9 6 0 34 34 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: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00343 (Fisher's exact test), Q value = 0.008

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

nPatients T1 T2 T3 T4
ALL 94 247 50 18
subtype1 21 33 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 = 0.117 (Fisher's exact test), Q value = 0.17

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

nPatients N0 N1 N2 N3
ALL 194 133 51 25
subtype1 35 19 5 3
subtype2 26 14 6 2
subtype3 53 26 10 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 = 0.373 (Fisher's exact test), Q value = 0.46

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 = 0.334 (Fisher's exact test), Q value = 0.43

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 'RADIATION_THERAPY'

P value = 0.081 (Fisher's exact test), Q value = 0.13

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

nPatients NO YES
ALL 161 188
subtype1 26 30
subtype2 14 25
subtype3 33 50
subtype4 17 28
subtype5 56 41
subtype6 15 14

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00026 (Fisher's exact test), Q value = 0.00087

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

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 354 31 1 7 2 15
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 98 11 0 1 2 6
subtype6 29 2 0 0 0 1

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

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0476 (Kruskal-Wallis (anova)), Q value = 0.08

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 = 0.00273 (Fisher's exact test), Q value = 0.0066

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 = 0.683 (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 567 311 215
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0992 (logrank test), Q value = 0.15

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

nPatients nDeath Duration Range (Median), Month
ALL 1077 150 0.0 - 282.9 (28.0)
subtype1 556 76 0.2 - 281.3 (25.4)
subtype2 307 51 0.0 - 282.9 (26.9)
subtype3 214 23 0.3 - 216.8 (34.1)

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 = 1.73e-05 (Kruskal-Wallis (anova)), Q value = 7.8e-05

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

nPatients Mean (Std.Dev)
ALL 1078 58.6 (13.2)
subtype1 556 60.3 (13.5)
subtype2 308 57.0 (12.8)
subtype3 214 56.5 (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 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE TIS STAGE X
ALL 89 87 6 6 359 255 2 155 29 65 20 1 14
subtype1 46 44 5 2 178 129 1 93 22 24 12 0 10
subtype2 19 22 0 2 132 73 0 33 6 14 5 0 1
subtype3 24 21 1 2 49 53 1 29 1 27 3 1 3

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients T1 T2 T3 T4
ALL 280 632 137 40
subtype1 148 326 64 27
subtype2 65 206 28 11
subtype3 67 100 45 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 = 0.00015 (Fisher's exact test), Q value = 0.00053

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

nPatients N0 N1 N2 N3
ALL 515 361 120 77
subtype1 253 194 76 31
subtype2 168 95 28 16
subtype3 94 72 16 30

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 = 0.88 (Fisher's exact test), Q value = 0.9

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

nPatients 0 1
ALL 902 22
subtype1 473 13
subtype2 266 6
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 = 0.00261 (Fisher's exact test), Q value = 0.0064

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

nPatients FEMALE MALE
ALL 1081 12
subtype1 555 12
subtype2 311 0
subtype3 215 0

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0807 (Fisher's exact test), Q value = 0.13

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

nPatients NO YES
ALL 435 523
subtype1 233 263
subtype2 129 142
subtype3 73 118

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA METAPLASTIC CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 783 202 6 9 28 17 46
subtype1 0 414 90 0 0 17 17 29
subtype2 1 271 8 6 9 3 0 12
subtype3 0 98 104 0 0 8 0 5

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

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00328 (Kruskal-Wallis (anova)), Q value = 0.0077

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

nPatients Mean (Std.Dev)
ALL 925 2.4 (4.6)
subtype1 460 2.1 (3.8)
subtype2 265 1.9 (4.3)
subtype3 200 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 = 4.6e-05

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 61 183 753
subtype1 0 33 71 396
subtype2 1 23 89 178
subtype3 0 5 23 179

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.188 (Fisher's exact test), Q value = 0.26

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 39 880
subtype1 25 439
subtype2 7 261
subtype3 7 180

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3 4 5 6
Number of samples 294 166 91 294 188 60
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 7.89e-06 (logrank test), Q value = 4.6e-05

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

nPatients nDeath Duration Range (Median), Month
ALL 1077 150 0.0 - 282.9 (28.0)
subtype1 287 41 0.2 - 275.9 (25.1)
subtype2 163 35 0.0 - 212.2 (23.1)
subtype3 91 9 0.3 - 157.4 (37.2)
subtype4 292 26 0.3 - 281.3 (32.2)
subtype5 186 27 0.2 - 282.9 (28.4)
subtype6 58 12 0.0 - 100.7 (20.2)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE TIS STAGE X
ALL 89 87 6 6 359 255 2 155 29 65 20 1 14
subtype1 20 23 4 2 95 74 1 43 15 7 4 0 6
subtype2 7 8 0 1 51 38 0 31 8 12 7 0 2
subtype3 7 10 1 1 17 27 0 14 1 12 1 0 0
subtype4 41 26 1 1 87 62 0 43 0 23 3 1 5
subtype5 13 15 0 1 86 41 0 17 4 5 3 0 1
subtype6 1 5 0 0 23 13 1 7 1 6 2 0 0

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients T1 T2 T3 T4
ALL 280 632 137 40
subtype1 76 170 28 18
subtype2 25 110 21 10
subtype3 26 37 27 1
subtype4 95 156 40 2
subtype5 40 124 17 6
subtype6 18 35 4 3

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.279 (Fisher's exact test), Q value = 0.37

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

nPatients 0 1
ALL 902 22
subtype1 250 5
subtype2 134 7
subtype3 69 1
subtype4 235 3
subtype5 162 4
subtype6 52 2

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0188 (Fisher's exact test), Q value = 0.035

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

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

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

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.178 (Fisher's exact test), Q value = 0.25

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

nPatients NO YES
ALL 435 523
subtype1 127 136
subtype2 68 70
subtype3 41 39
subtype4 103 156
subtype5 70 97
subtype6 26 25

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA METAPLASTIC CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 783 202 6 9 28 17 46
subtype1 0 224 24 0 0 10 15 21
subtype2 0 138 16 0 3 4 1 4
subtype3 0 28 55 0 0 5 1 2
subtype4 0 176 100 1 0 7 0 10
subtype5 1 163 2 5 5 2 0 9
subtype6 0 54 5 0 1 0 0 0

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

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 3.58e-05 (Kruskal-Wallis (anova)), Q value = 0.00015

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

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

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

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

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.549 (Fisher's exact test), Q value = 0.63

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

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

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

Clustering Approach #9: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 309 214 274 281
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.352 (logrank test), Q value = 0.44

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

nPatients nDeath Duration Range (Median), Month
ALL 1062 146 0.0 - 282.9 (27.7)
subtype1 304 47 0.0 - 255.7 (25.0)
subtype2 214 28 0.0 - 282.9 (32.6)
subtype3 269 32 0.2 - 281.3 (26.0)
subtype4 275 39 0.3 - 275.9 (29.1)

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 = 0.00663 (Kruskal-Wallis (anova)), Q value = 0.014

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

nPatients Mean (Std.Dev)
ALL 1063 58.6 (13.2)
subtype1 308 57.8 (13.1)
subtype2 211 57.2 (12.8)
subtype3 270 60.7 (13.4)
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 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE TIS STAGE X
ALL 89 87 7 6 354 249 2 152 28 64 20 1 14
subtype1 14 17 0 2 132 69 0 40 7 13 8 0 3
subtype2 24 21 2 2 45 58 1 31 3 23 3 1 0
subtype3 16 27 4 1 89 57 1 37 11 20 3 0 8
subtype4 35 22 1 1 88 65 0 44 7 8 6 0 3

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

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients T1 T2 T3 T4
ALL 281 619 134 40
subtype1 49 215 32 11
subtype2 72 95 43 3
subtype3 67 155 36 15
subtype4 93 154 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 = 0.0147 (Fisher's exact test), Q value = 0.028

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

nPatients N0 N1 N2 N3
ALL 509 356 118 75
subtype1 165 90 33 16
subtype2 89 83 17 24
subtype3 128 86 30 22
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 = 0.854 (Fisher's exact test), Q value = 0.88

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

nPatients 0 1
ALL 887 21
subtype1 259 8
subtype2 160 3
subtype3 220 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 = 0.163 (Fisher's exact test), Q value = 0.23

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

nPatients FEMALE MALE
ALL 1066 12
subtype1 303 6
subtype2 214 0
subtype3 272 2
subtype4 277 4

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.823 (Fisher's exact test), Q value = 0.86

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

nPatients NO YES
ALL 431 513
subtype1 127 144
subtype2 86 104
subtype3 106 139
subtype4 112 126

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA METAPLASTIC CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 770 199 6 9 28 17 47
subtype1 1 269 9 5 5 4 5 10
subtype2 0 89 106 0 4 3 3 9
subtype3 0 177 66 0 0 10 8 13
subtype4 0 235 18 1 0 11 1 15

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

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0376 (Kruskal-Wallis (anova)), Q value = 0.067

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

nPatients Mean (Std.Dev)
ALL 916 2.4 (4.6)
subtype1 258 2.0 (4.2)
subtype2 201 3.2 (6.0)
subtype3 220 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 = 4.6e-05

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 61 182 746
subtype1 0 20 91 176
subtype2 0 11 30 169
subtype3 0 9 43 189
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 = 0.089 (Fisher's exact test), Q value = 0.14

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 39 874
subtype1 5 261
subtype2 11 187
subtype3 10 212
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 300 155 248 141 98 136
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.153 (logrank test), Q value = 0.22

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

nPatients nDeath Duration Range (Median), Month
ALL 1062 146 0.0 - 282.9 (27.7)
subtype1 294 46 0.0 - 263.3 (26.3)
subtype2 152 24 0.0 - 216.8 (21.8)
subtype3 248 29 0.3 - 170.2 (25.5)
subtype4 140 17 0.3 - 281.3 (36.4)
subtype5 94 10 0.2 - 275.9 (26.6)
subtype6 134 20 0.2 - 282.9 (31.0)

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 = 4.98e-05 (Kruskal-Wallis (anova)), Q value = 2e-04

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

nPatients Mean (Std.Dev)
ALL 1063 58.6 (13.2)
subtype1 295 58.3 (13.6)
subtype2 151 58.8 (12.8)
subtype3 243 59.5 (13.0)
subtype4 141 58.2 (12.0)
subtype5 97 63.4 (14.7)
subtype6 136 54.5 (12.1)

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.00033 (Fisher's exact test), Q value = 0.0011

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE TIS STAGE X
ALL 89 87 7 6 354 249 2 152 28 64 20 1 14
subtype1 18 23 2 2 96 67 1 48 8 17 7 1 8
subtype2 12 9 1 2 56 33 0 23 3 9 6 0 0
subtype3 16 29 2 1 62 67 1 40 3 24 1 0 2
subtype4 26 8 1 0 44 32 0 11 6 9 3 0 1
subtype5 9 6 1 0 32 23 0 18 6 1 1 0 1
subtype6 8 12 0 1 64 27 0 12 2 4 2 0 2

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00143 (Fisher's exact test), Q value = 0.0038

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

nPatients T1 T2 T3 T4
ALL 281 619 134 40
subtype1 70 182 34 12
subtype2 40 95 15 5
subtype3 66 128 49 4
subtype4 51 69 13 8
subtype5 26 53 12 7
subtype6 28 92 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 = 0.00039 (Fisher's exact test), Q value = 0.0012

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

nPatients N0 N1 N2 N3
ALL 509 356 118 75
subtype1 121 106 40 22
subtype2 74 44 23 11
subtype3 111 85 23 26
subtype4 73 47 10 11
subtype5 43 40 12 1
subtype6 87 34 10 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 = 0.219 (Fisher's exact test), Q value = 0.3

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

nPatients 0 1
ALL 887 21
subtype1 251 8
subtype2 126 6
subtype3 186 1
subtype4 123 3
subtype5 85 1
subtype6 116 2

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0493 (Fisher's exact test), Q value = 0.082

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

nPatients FEMALE MALE
ALL 1066 12
subtype1 296 4
subtype2 152 3
subtype3 248 0
subtype4 139 2
subtype5 95 3
subtype6 136 0

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.41 (Fisher's exact test), Q value = 0.5

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

nPatients NO YES
ALL 431 513
subtype1 112 152
subtype2 60 75
subtype3 106 125
subtype4 60 51
subtype5 41 42
subtype6 52 68

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA METAPLASTIC CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 770 199 6 9 28 17 47
subtype1 0 252 22 1 0 10 1 14
subtype2 0 131 11 0 6 1 0 6
subtype3 0 100 134 0 1 3 3 7
subtype4 0 99 28 0 0 9 0 5
subtype5 0 70 2 0 0 3 13 10
subtype6 1 118 2 5 2 2 0 5

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00149 (Kruskal-Wallis (anova)), Q value = 0.0039

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

nPatients Mean (Std.Dev)
ALL 916 2.4 (4.6)
subtype1 233 2.4 (4.1)
subtype2 131 2.7 (5.3)
subtype3 225 3.1 (5.8)
subtype4 130 2.2 (4.6)
subtype5 76 1.8 (3.1)
subtype6 121 1.2 (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 = 4.6e-05

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 61 182 746
subtype1 1 22 44 197
subtype2 0 13 27 102
subtype3 0 12 39 194
subtype4 0 5 8 119
subtype5 0 4 10 64
subtype6 0 5 54 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 = 0.46

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 39 874
subtype1 7 230
subtype2 6 127
subtype3 10 224
subtype4 8 110
subtype5 5 65
subtype6 3 118

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 182 140 102 111 133
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.884 (logrank test), Q value = 0.9

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

nPatients nDeath Duration Range (Median), Month
ALL 663 86 0.0 - 216.8 (26.0)
subtype1 181 29 0.5 - 212.2 (26.1)
subtype2 138 18 0.0 - 131.7 (29.9)
subtype3 101 7 0.2 - 109.3 (23.5)
subtype4 111 15 0.3 - 120.6 (30.4)
subtype5 132 17 0.0 - 216.8 (25.5)

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 = 0.0434 (Kruskal-Wallis (anova)), Q value = 0.074

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

nPatients Mean (Std.Dev)
ALL 653 58.3 (13.2)
subtype1 174 58.4 (14.1)
subtype2 140 57.0 (12.9)
subtype3 100 61.1 (12.7)
subtype4 110 56.3 (12.6)
subtype5 129 59.1 (12.9)

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00942 (Fisher's exact test), Q value = 0.019

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 62 52 5 5 219 162 2 92 13 42 8 2
subtype1 20 17 1 1 63 41 0 26 4 6 1 1
subtype2 10 7 0 1 65 28 0 20 3 3 1 0
subtype3 5 12 3 2 25 24 0 14 1 13 3 0
subtype4 15 8 1 0 31 30 0 14 2 7 1 1
subtype5 12 8 0 1 35 39 2 18 3 13 2 0

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00052 (Fisher's exact test), Q value = 0.0016

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

nPatients T1 T2 T3 T4
ALL 185 378 85 19
subtype1 60 101 14 6
subtype2 28 97 10 5
subtype3 23 56 21 2
subtype4 42 53 14 2
subtype5 32 71 26 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 = 0.00473 (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 318 229 67 45
subtype1 85 67 19 7
subtype2 82 36 19 3
subtype3 45 34 7 13
subtype4 48 45 10 7
subtype5 58 47 12 15

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 = 0.199 (Fisher's exact test), Q value = 0.28

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

nPatients 0 1
ALL 515 8
subtype1 158 1
subtype2 112 1
subtype3 56 3
subtype4 84 1
subtype5 105 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 = 0.241 (Fisher's exact test), Q value = 0.32

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

nPatients FEMALE MALE
ALL 659 9
subtype1 178 4
subtype2 139 1
subtype3 99 3
subtype4 110 1
subtype5 133 0

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

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.621 (Fisher's exact test), Q value = 0.71

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

nPatients NO YES
ALL 275 327
subtype1 82 80
subtype2 53 74
subtype3 41 54
subtype4 40 49
subtype5 59 70

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA METAPLASTIC CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 437 160 5 9 15 15 26
subtype1 0 146 13 0 0 8 6 9
subtype2 1 123 3 5 4 2 0 2
subtype3 0 41 52 0 0 2 4 3
subtype4 0 63 34 0 3 2 1 8
subtype5 0 64 58 0 2 1 4 4

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

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0574 (Kruskal-Wallis (anova)), Q value = 0.093

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

nPatients Mean (Std.Dev)
ALL 600 2.5 (5.0)
subtype1 159 2.0 (4.0)
subtype2 131 1.5 (2.7)
subtype3 87 3.4 (6.1)
subtype4 104 2.6 (4.9)
subtype5 119 3.4 (7.0)

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 = 4.6e-05

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 51 145 466
subtype1 20 11 151
subtype2 10 54 74
subtype3 1 39 60
subtype4 4 14 92
subtype5 16 27 89

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 = 0.534 (Fisher's exact test), Q value = 0.62

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 29 608
subtype1 8 167
subtype2 3 130
subtype3 4 90
subtype4 7 97
subtype5 7 124

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 309 249 110
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.501 (logrank test), Q value = 0.59

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

nPatients nDeath Duration Range (Median), Month
ALL 663 86 0.0 - 216.8 (26.0)
subtype1 304 46 0.2 - 160.9 (29.2)
subtype2 249 25 0.0 - 216.8 (22.3)
subtype3 110 15 1.5 - 212.2 (33.4)

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 'YEARS_TO_BIRTH'

P value = 0.00639 (Kruskal-Wallis (anova)), Q value = 0.014

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

nPatients Mean (Std.Dev)
ALL 653 58.3 (13.2)
subtype1 301 57.6 (13.7)
subtype2 243 60.2 (12.7)
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 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 62 52 5 5 219 162 2 92 13 42 8 2
subtype1 27 22 1 3 124 68 0 44 7 7 3 1
subtype2 16 20 4 2 62 67 2 37 5 31 3 0
subtype3 19 10 0 0 33 27 0 11 1 4 2 1

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

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients T1 T2 T3 T4
ALL 185 378 85 19
subtype1 78 193 26 11
subtype2 61 130 53 5
subtype3 46 55 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 = 8e-05 (Fisher's exact test), Q value = 0.00031

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

nPatients N0 N1 N2 N3
ALL 318 229 67 45
subtype1 160 99 37 8
subtype2 107 85 22 32
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 = 0.71 (Fisher's exact test), Q value = 0.76

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

nPatients 0 1
ALL 515 8
subtype1 258 3
subtype2 167 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 = 0.668 (Fisher's exact test), Q value = 0.73

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

nPatients FEMALE MALE
ALL 659 9
subtype1 304 5
subtype2 247 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 'RADIATION_THERAPY'

P value = 0.776 (Fisher's exact test), Q value = 0.82

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

nPatients NO YES
ALL 275 327
subtype1 131 146
subtype2 104 131
subtype3 40 50

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 4.6e-05

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

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA METAPLASTIC CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 437 160 5 9 15 15 26
subtype1 1 253 16 5 2 9 9 14
subtype2 0 103 126 0 7 2 5 6
subtype3 0 81 18 0 0 4 1 6

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

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0084 (Kruskal-Wallis (anova)), Q value = 0.017

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

nPatients Mean (Std.Dev)
ALL 600 2.5 (5.0)
subtype1 275 1.8 (3.7)
subtype2 221 3.6 (6.5)
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 = 4.6e-05

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 51 145 466
subtype1 27 67 213
subtype2 18 72 156
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 = 0.67 (Fisher's exact test), Q value = 0.73

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 29 608
subtype1 14 280
subtype2 9 230
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/20125499/BRCA-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BRCA-TP/19775058/BRCA-TP.merged_data.txt

  • Number of patients = 1098

  • 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)
[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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