Correlation between aggregated molecular cancer subtypes and selected clinical features
Breast Invasive Carcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C12N50J9
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12N50J9
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 10 clinical features across 966 patients, 43 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 8 subtypes that correlate to 'AGE',  'PATHOLOGY.N.STAGE', and 'HISTOLOGICAL.TYPE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

  • 5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.T.STAGE',  'GENDER', and 'HISTOLOGICAL.TYPE'.

  • 6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE',  'PATHOLOGY.T.STAGE', and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY.T.STAGE' and 'HISTOLOGICAL.TYPE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death',  'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE',  'HISTOLOGICAL.TYPE', and 'NUMBER.OF.LYMPH.NODES'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.M.STAGE', and 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY.N.STAGE',  'PATHOLOGY.M.STAGE', and 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE',  'HISTOLOGICAL.TYPE', and 'NUMBER.OF.LYMPH.NODES'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 43 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE NEOPLASM
DISEASESTAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER HISTOLOGICAL
TYPE 		RADIATIONS
RADIATION
REGIMENINDICATION 		NUMBER
OF
LYMPH
NODES
Statistical Tests logrank test ANOVA Chi-square test Chi-square test Chi-square test Chi-square test Fisher's exact test Chi-square test Fisher's exact test ANOVA
mRNA CNMF subtypes 0.0408
(1.00) 		2.59e-08
(2.87e-06) 		0.0268
(1.00) 		0.0413
(1.00) 		0.000806
(0.0715) 		0.54
(1.00) 		0.0459
(1.00) 		1.04e-05
(0.0011) 		0.542
(1.00) 		0.397
(1.00)
mRNA cHierClus subtypes 0.879
(1.00) 		0.000796
(0.0715) 		0.0805
(1.00) 		0.0433
(1.00) 		0.27
(1.00) 		0.143
(1.00) 		0.491
(1.00) 		1.22e-07
(1.35e-05) 		0.0297
(1.00) 		0.646
(1.00)
Copy Number Ratio CNMF subtypes 0.189
(1.00) 		0.0523
(1.00) 		0.0136
(0.914) 		1.83e-09
(2.05e-07) 		0.00898
(0.629) 		0.0397
(1.00) 		4.19e-05
(0.00422) 		1.05e-17
(1.2e-15) 		0.275
(1.00) 		0.193
(1.00)
METHLYATION CNMF 0.223
(1.00) 		0.000288
(0.0273) 		0.0198
(1.00) 		0.00214
(0.174) 		0.0173
(1.00) 		0.516
(1.00) 		0.0406
(1.00) 		9.73e-13
(1.1e-10) 		0.0851
(1.00) 		0.044
(1.00)
RPPA CNMF subtypes 0.0399
(1.00) 		0.0287
(1.00) 		0.0315
(1.00) 		0.00137
(0.115) 		0.163
(1.00) 		0.323
(1.00) 		0.0564
(1.00) 		0.000131
(0.0128) 		0.982
(1.00) 		0.0305
(1.00)
RPPA cHierClus subtypes 0.35
(1.00) 		0.00121
(0.102) 		0.0442
(1.00) 		0.00703
(0.513) 		0.738
(1.00) 		0.665
(1.00) 		0.154
(1.00) 		0.000479
(0.0446) 		0.704
(1.00) 		0.0553
(1.00)
RNAseq CNMF subtypes 0.00307
(0.243) 		1.26e-06
(0.000137) 		0.000429
(0.0404) 		0.000731
(0.0665) 		0.00015
(0.0146) 		0.408
(1.00) 		0.0362
(1.00) 		3.08e-31
(3.54e-29) 		0.286
(1.00) 		0.000904
(0.0787)
RNAseq cHierClus subtypes 0.0074
(0.533) 		3.82e-05
(0.00389) 		4.18e-05
(0.00422) 		1.17e-05
(0.00122) 		0.0199
(1.00) 		0.289
(1.00) 		0.0704
(1.00) 		1.08e-35
(1.28e-33) 		0.472
(1.00) 		0.0932
(1.00)
MIRSEQ CNMF 0.0808
(1.00) 		0.202
(1.00) 		1.45e-05
(0.00149) 		9.17e-06
(0.000981) 		0.00521
(0.386) 		0.00216
(0.174) 		0.513
(1.00) 		6.89e-41
(8.26e-39) 		0.106
(1.00) 		0.0298
(1.00)
MIRSEQ CHIERARCHICAL 0.0884
(1.00) 		0.0265
(1.00) 		0.00445
(0.336) 		0.0107
(0.739) 		0.00168
(0.14) 		0.00171
(0.14) 		0.473
(1.00) 		5.63e-38
(6.71e-36) 		0.287
(1.00) 		0.0992
(1.00)
MIRseq Mature CNMF subtypes 0.0115
(0.781) 		0.219
(1.00) 		1.43e-05
(0.00148) 		7.33e-06
(0.000791) 		0.000166
(0.0159) 		0.00347
(0.267) 		0.576
(1.00) 		1.53e-33
(1.77e-31) 		0.0366
(1.00) 		0.00312
(0.243)
MIRseq Mature cHierClus subtypes 0.000645
(0.0593) 		0.208
(1.00) 		0.000106
(0.0105) 		0.000794
(0.0715) 		0.00767
(0.545) 		0.00442
(0.336) 		0.612
(1.00) 		9.97e-35
(1.17e-32) 		0.000996
(0.0856) 		0.158
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3 4 5 6 7 8
Number of samples 21 39 121 101 110 74 20 40
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0408 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 495 72 0.1 - 234.2 (28.2)
subtype1 19 3 0.3 - 92.0 (16.3)
subtype2 38 3 0.1 - 157.4 (43.5)
subtype3 111 18 0.1 - 188.7 (28.0)
subtype4 96 13 0.2 - 211.5 (26.4)
subtype5 107 11 0.3 - 234.2 (26.0)
subtype6 66 15 0.1 - 189.0 (30.7)
subtype7 19 3 0.2 - 97.5 (41.8)
subtype8 39 6 0.3 - 112.4 (27.5)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 2.59e-08 (ANOVA), Q value = 2.9e-06

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

nPatients Mean (Std.Dev)
ALL 526 57.9 (13.2)
subtype1 21 59.9 (13.8)
subtype2 39 48.3 (10.6)
subtype3 121 58.1 (14.2)
subtype4 101 53.9 (12.5)
subtype5 110 62.6 (12.5)
subtype6 74 58.6 (12.5)
subtype7 20 60.4 (9.9)
subtype8 40 60.6 (12.0)

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0268 (Chi-square test), Q value = 1

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

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

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

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

P value = 0.0413 (Chi-square test), Q value = 1

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

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

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.000806 (Chi-square test), Q value = 0.071

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 = 0.54 (Chi-square test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0459 (Chi-square test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.04e-05 (Chi-square test), Q value = 0.0011

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 = 0.542 (Chi-square test), Q value = 1

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

nPatients NO YES
ALL 154 372
subtype1 3 18
subtype2 13 26
subtype3 32 89
subtype4 37 64
subtype5 31 79
subtype6 21 53
subtype7 5 15
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 = 0.397 (ANOVA), Q value = 1

Table S11.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 406 1.8 (3.5)
subtype1 14 1.4 (2.2)
subtype2 34 1.9 (2.4)
subtype3 86 1.6 (2.3)
subtype4 92 1.4 (3.2)
subtype5 84 1.8 (3.9)
subtype6 46 2.8 (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'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S12.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 124 131 271
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.879 (logrank test), Q value = 1

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 495 72 0.1 - 234.2 (28.2)
subtype1 122 15 0.3 - 157.4 (31.5)
subtype2 121 20 0.3 - 211.5 (31.0)
subtype3 252 37 0.1 - 234.2 (25.6)

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.000796 (ANOVA), Q value = 0.071

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 526 57.9 (13.2)
subtype1 124 56.4 (13.0)
subtype2 131 55.1 (13.1)
subtype3 271 60.0 (13.1)

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0805 (Chi-square test), Q value = 1

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE TIS STAGE X
ALL 46 37 6 185 110 77 15 19 14 1 16
subtype1 18 9 2 38 22 21 1 5 2 1 5
subtype2 9 11 0 56 25 13 2 8 4 0 3
subtype3 19 17 4 91 63 43 12 6 8 0 8

Figure S13.  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 = 0.0433 (Chi-square test), Q value = 1

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 133 311 59 20
subtype1 43 60 17 3
subtype2 30 85 12 3
subtype3 60 166 30 14

Figure S14.  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 = 0.27 (Chi-square test), Q value = 1

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 255 170 61 29
subtype1 62 37 14 8
subtype2 74 34 14 9
subtype3 119 99 33 12

Figure S15.  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 = 0.143 (Chi-square test), Q value = 1

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 2 496 14 14
subtype1 2 116 2 4
subtype2 0 126 4 1
subtype3 0 254 8 9

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.491 (Fisher's exact test), Q value = 1

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 520 6
subtype1 122 2
subtype2 131 0
subtype3 267 4

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.22e-07 (Chi-square test), Q value = 1.3e-05

Table S20.  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 88 26 0 6 0 4
subtype2 119 4 1 0 0 6
subtype3 241 11 0 6 2 11

Figure S18.  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 = 0.0297 (Fisher's exact test), Q value = 1

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 154 372
subtype1 38 86
subtype2 49 82
subtype3 67 204

Figure S19.  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 = 0.646 (ANOVA), Q value = 1

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 406 1.8 (3.5)
subtype1 110 2.1 (4.2)
subtype2 114 1.7 (3.3)
subtype3 182 1.7 (3.2)

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

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

Table S23.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 359 227 75 241 47
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.189 (logrank test), Q value = 1

Table S24.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 895 109 0.0 - 234.2 (21.4)
subtype1 341 34 0.0 - 234.2 (22.4)
subtype2 212 25 0.1 - 162.0 (21.5)
subtype3 72 9 0.0 - 129.6 (18.6)
subtype4 225 32 0.0 - 211.5 (21.1)
subtype5 45 9 0.2 - 220.9 (21.6)

Figure S21.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.0523 (ANOVA), Q value = 1

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 946 58.5 (13.2)
subtype1 357 58.9 (13.0)
subtype2 227 58.6 (14.1)
subtype3 75 60.6 (12.1)
subtype4 241 56.7 (12.8)
subtype5 46 61.5 (12.1)

Figure S22.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0136 (Chi-square test), Q value = 0.91

Table S26.  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 77 70 10 9 321 214 2 134 26 53 15 1 16
subtype1 41 41 6 7 104 74 1 42 7 25 4 0 7
subtype2 12 12 3 1 78 48 1 42 12 11 5 0 2
subtype3 3 1 0 1 22 23 0 12 3 5 2 0 3
subtype4 18 13 1 0 97 59 0 30 4 10 4 1 3
subtype5 3 3 0 0 20 10 0 8 0 2 0 0 1

Figure S23.  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 = 1.83e-09 (Chi-square test), Q value = 2.1e-07

Table S27.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 247 551 113 35
subtype1 132 163 53 10
subtype2 46 147 22 12
subtype3 6 51 12 6
subtype4 46 165 22 6
subtype5 17 25 4 1

Figure S24.  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 = 0.00898 (Chi-square test), Q value = 0.63

Table S28.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 445 317 109 64
subtype1 179 117 29 28
subtype2 89 81 36 15
subtype3 28 30 8 8
subtype4 132 68 30 11
subtype5 17 21 6 2

Figure S25.  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 = 0.0397 (Chi-square test), Q value = 1

Table S29.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 2 823 15 109
subtype1 1 299 4 55
subtype2 0 206 5 16
subtype3 0 64 2 9
subtype4 0 213 4 24
subtype5 1 41 0 5

Figure S26.  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 = 4.19e-05 (Chi-square test), Q value = 0.0042

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 939 10
subtype1 359 0
subtype2 218 9
subtype3 74 1
subtype4 241 0
subtype5 47 0

Figure S27.  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 = 1.05e-17 (Chi-square test), Q value = 1.2e-15

Table S31.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 706 151 5 28 14 43
subtype1 0 213 99 0 16 10 21
subtype2 0 195 20 0 2 3 7
subtype3 0 48 21 0 5 0 1
subtype4 1 215 5 4 4 0 11
subtype5 0 35 6 1 1 1 3

Figure S28.  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 = 0.275 (Chi-square test), Q value = 1

Table S32.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 231 718
subtype1 76 283
subtype2 57 170
subtype3 16 59
subtype4 68 173
subtype5 14 33

Figure S29.  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 = 0.193 (ANOVA), Q value = 1

Table S33.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 794 2.4 (4.6)
subtype1 316 2.2 (4.5)
subtype2 166 2.6 (4.1)
subtype3 59 3.6 (5.9)
subtype4 211 2.0 (4.8)
subtype5 42 2.5 (4.2)

Figure S30.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'METHLYATION CNMF'

Table S34.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6
Number of samples 103 153 103 137 40 107
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.223 (logrank test), Q value = 1

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 618 71 0.0 - 234.2 (20.8)
subtype1 100 17 0.2 - 211.5 (21.0)
subtype2 146 15 0.3 - 234.2 (18.3)
subtype3 101 14 0.0 - 130.1 (17.7)
subtype4 130 13 0.1 - 194.3 (23.3)
subtype5 39 3 0.1 - 157.4 (20.4)
subtype6 102 9 0.0 - 130.2 (27.6)

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000288 (ANOVA), Q value = 0.027

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 640 57.9 (13.0)
subtype1 102 55.4 (12.3)
subtype2 152 58.9 (12.4)
subtype3 103 62.9 (11.7)
subtype4 137 56.3 (14.5)
subtype5 40 57.0 (14.0)
subtype6 106 56.5 (12.3)

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0198 (Chi-square test), Q value = 1

Table S37.  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 49 51 6 8 206 148 2 105 16 40 6 5
subtype1 5 6 0 0 45 28 0 11 1 3 2 1
subtype2 11 15 1 3 42 43 0 21 4 11 0 2
subtype3 6 5 1 2 32 22 1 22 0 8 2 2
subtype4 8 8 2 0 40 35 0 29 8 6 1 0
subtype5 3 6 0 1 10 4 0 8 2 5 1 0
subtype6 16 11 2 2 37 16 1 14 1 7 0 0

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00214 (Chi-square test), Q value = 0.17

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 172 366 84 19
subtype1 17 70 12 3
subtype2 46 83 20 3
subtype3 19 63 19 2
subtype4 32 81 16 8
subtype5 11 22 5 2
subtype6 47 47 12 1

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.0173 (Chi-square test), Q value = 1

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 282 227 84 43
subtype1 59 30 10 4
subtype2 60 64 15 11
subtype3 46 28 19 8
subtype4 46 60 23 7
subtype5 17 11 6 5
subtype6 54 34 11 8

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.516 (Chi-square test), Q value = 1

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 1 539 6 97
subtype1 0 90 2 11
subtype2 1 131 0 21
subtype3 0 81 2 20
subtype4 0 118 1 18
subtype5 0 31 1 8
subtype6 0 88 0 19

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0406 (Chi-square test), Q value = 1

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 636 7
subtype1 103 0
subtype2 152 1
subtype3 100 3
subtype4 136 1
subtype5 38 2
subtype6 107 0

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 9.73e-13 (Chi-square test), Q value = 1.1e-10

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 443 127 5 23 12 31
subtype1 1 82 3 3 1 0 12
subtype2 0 80 46 0 13 6 8
subtype3 0 65 33 0 3 0 2
subtype4 0 118 6 0 3 4 6
subtype5 0 29 8 0 1 1 1
subtype6 0 69 31 2 2 1 2

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0851 (Chi-square test), Q value = 1

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 166 477
subtype1 34 69
subtype2 37 116
subtype3 21 82
subtype4 44 93
subtype5 8 32
subtype6 22 85

Figure S39.  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 = 0.044 (ANOVA), Q value = 1

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 595 2.6 (4.8)
subtype1 100 1.7 (3.2)
subtype2 147 2.6 (4.8)
subtype3 94 3.3 (6.0)
subtype4 119 2.7 (4.4)
subtype5 34 4.4 (7.1)
subtype6 101 2.1 (4.6)

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S45.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 152 136 120
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0399 (logrank test), Q value = 1

Table S46.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 381 51 0.2 - 189.0 (29.0)
subtype1 136 25 0.2 - 186.4 (28.3)
subtype2 132 13 0.2 - 146.5 (25.8)
subtype3 113 13 0.3 - 189.0 (33.3)

Figure S41.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0287 (ANOVA), Q value = 1

Table S47.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 408 57.9 (13.1)
subtype1 152 56.1 (13.4)
subtype2 136 60.1 (13.7)
subtype3 120 57.5 (11.7)

Figure S42.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0315 (Chi-square test), Q value = 1

Table S48.  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 31 30 4 137 94 62 13 15 14 8
subtype1 6 8 0 55 36 24 2 10 8 3
subtype2 12 12 1 46 38 14 6 1 3 3
subtype3 13 10 3 36 20 24 5 4 3 2

Figure S43.  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 = 0.00137 (Chi-square test), Q value = 0.12

Table S49.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 93 246 50 18
subtype1 20 106 22 4
subtype2 35 83 11 6
subtype3 38 57 17 8

Figure S44.  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 = 0.163 (Chi-square test), Q value = 1

Table S50.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 192 132 52 25
subtype1 70 45 22 12
subtype2 65 53 10 6
subtype3 57 34 20 7

Figure S45.  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 = 0.323 (Chi-square test), Q value = 1

Table S51.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 1 386 14 7
subtype1 0 143 8 1
subtype2 0 129 3 4
subtype3 1 114 3 2

Figure S46.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0564 (Fisher's exact test), Q value = 1

Table S52.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 403 5
subtype1 152 0
subtype2 132 4
subtype3 119 1

Figure S47.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000131 (Chi-square test), Q value = 0.013

Table S53.  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 353 30 1 8 2 14
subtype1 139 4 1 1 0 7
subtype2 120 5 0 4 2 5
subtype3 94 21 0 3 0 2

Figure S48.  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 = 0.982 (Fisher's exact test), Q value = 1

Table S54.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 129 279
subtype1 49 103
subtype2 42 94
subtype3 38 82

Figure S49.  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 = 0.0305 (ANOVA), Q value = 1

Table S55.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 330 1.9 (3.5)
subtype1 114 2.1 (3.6)
subtype2 113 1.2 (2.0)
subtype3 103 2.4 (4.5)

Figure S50.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S56.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 163 115 130
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.35 (logrank test), Q value = 1

Table S57.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 381 51 0.2 - 189.0 (29.0)
subtype1 152 20 0.2 - 129.7 (28.2)
subtype2 112 12 0.2 - 173.0 (31.4)
subtype3 117 19 0.2 - 189.0 (28.6)

Figure S51.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00121 (ANOVA), Q value = 0.1

Table S58.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 408 57.9 (13.1)
subtype1 163 60.4 (13.6)
subtype2 115 57.6 (11.8)
subtype3 130 54.8 (13.0)

Figure S52.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0442 (Chi-square test), Q value = 1

Table S59.  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 31 30 4 137 94 62 13 15 14 8
subtype1 15 11 1 52 42 23 7 2 6 4
subtype2 11 12 3 31 21 23 5 4 3 2
subtype3 5 7 0 54 31 16 1 9 5 2

Figure S53.  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 = 0.00703 (Chi-square test), Q value = 0.51

Table S60.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 93 246 50 18
subtype1 36 101 18 7
subtype2 37 53 17 8
subtype3 20 92 15 3

Figure S54.  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 = 0.738 (Chi-square test), Q value = 1

Table S61.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 192 132 52 25
subtype1 78 55 19 7
subtype2 51 37 18 7
subtype3 63 40 15 11

Figure S55.  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 = 0.665 (Chi-square test), Q value = 1

Table S62.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 1 386 14 7
subtype1 0 153 6 4
subtype2 1 109 3 2
subtype3 0 124 5 1

Figure S56.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.154 (Fisher's exact test), Q value = 1

Table S63.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 403 5
subtype1 159 4
subtype2 114 1
subtype3 130 0

Figure S57.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000479 (Chi-square test), Q value = 0.045

Table S64.  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 353 30 1 8 2 14
subtype1 143 8 0 5 2 5
subtype2 91 19 0 3 0 2
subtype3 119 3 1 0 0 7

Figure S58.  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 = 0.704 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 129 279
subtype1 48 115
subtype2 37 78
subtype3 44 86

Figure S59.  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 = 0.0553 (ANOVA), Q value = 1

Table S66.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 330 1.9 (3.5)
subtype1 126 1.3 (2.1)
subtype2 99 2.4 (4.5)
subtype3 105 2.1 (3.7)

Figure S60.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S67.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 172 139 206 152 224 45 15
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00307 (logrank test), Q value = 0.24

Table S68.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 900 112 0.0 - 234.2 (21.6)
subtype1 167 24 0.2 - 211.5 (22.8)
subtype2 136 11 0.1 - 162.0 (29.0)
subtype3 199 17 0.2 - 234.2 (18.9)
subtype4 136 28 0.0 - 189.0 (20.6)
subtype5 205 25 0.1 - 188.7 (23.3)
subtype6 43 4 0.3 - 194.3 (21.3)
subtype7 14 3 0.2 - 130.2 (30.6)

Figure S61.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 1.26e-06 (ANOVA), Q value = 0.00014

Table S69.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 950 58.4 (13.1)
subtype1 172 55.1 (12.2)
subtype2 138 54.8 (12.2)
subtype3 205 61.2 (12.4)
subtype4 151 60.3 (12.8)
subtype5 224 58.5 (14.3)
subtype6 45 61.6 (12.4)
subtype7 15 62.1 (14.7)

Figure S62.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000429 (Chi-square test), Q value = 0.04

Table S70.  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 77 72 11 9 324 217 2 130 25 52 15 1 17
subtype1 12 12 0 0 82 36 0 16 2 6 3 0 2
subtype2 14 14 2 5 28 37 1 19 1 13 2 1 2
subtype3 20 24 2 1 75 38 0 24 6 7 3 0 6
subtype4 9 7 1 1 47 34 0 27 5 11 5 0 5
subtype5 14 11 6 0 74 59 1 37 11 8 1 0 2
subtype6 5 3 0 2 12 10 0 6 0 6 1 0 0
subtype7 3 1 0 0 6 3 0 1 0 1 0 0 0

Figure S63.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.000731 (Chi-square test), Q value = 0.066

Table S71.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 251 552 113 34
subtype1 37 112 17 5
subtype2 45 65 27 1
subtype3 74 103 21 8
subtype4 31 97 16 8
subtype5 51 140 21 11
subtype6 9 25 10 1
subtype7 4 10 1 0

Figure S64.  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 (Chi-square test), Q value = 0.015

Table S72.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 450 319 106 63
subtype1 107 45 13 7
subtype2 56 53 13 15
subtype3 107 67 18 11
subtype4 55 52 26 14
subtype5 92 86 32 9
subtype6 23 13 3 6
subtype7 10 3 1 1

Figure S65.  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 = 0.408 (Chi-square test), Q value = 1

Table S73.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 2 828 15 108
subtype1 0 155 3 14
subtype2 1 115 2 21
subtype3 1 174 3 28
subtype4 0 133 5 14
subtype5 0 203 1 20
subtype6 0 36 1 8
subtype7 0 12 0 3

Figure S66.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0362 (Chi-square test), Q value = 1

Table S74.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 943 10
subtype1 172 0
subtype2 139 0
subtype3 204 2
subtype4 151 1
subtype5 217 7
subtype6 45 0
subtype7 15 0

Figure S67.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.08e-31 (Chi-square test), Q value = 3.5e-29

Table S75.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 711 152 5 27 14 42
subtype1 1 149 1 4 0 2 14
subtype2 0 71 53 0 11 1 3
subtype3 0 139 44 0 8 3 12
subtype4 0 137 10 0 1 0 4
subtype5 0 184 18 0 6 8 8
subtype6 0 18 25 0 1 0 1
subtype7 0 13 1 1 0 0 0

Figure S68.  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 = 0.286 (Chi-square test), Q value = 1

Table S76.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 231 722
subtype1 52 120
subtype2 35 104
subtype3 49 157
subtype4 29 123
subtype5 53 171
subtype6 8 37
subtype7 5 10

Figure S69.  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 = 0.000904 (ANOVA), Q value = 0.079

Table S77.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 799 2.3 (4.6)
subtype1 157 1.4 (3.0)
subtype2 127 3.3 (6.3)
subtype3 174 1.9 (3.9)
subtype4 105 3.5 (5.9)
subtype5 181 2.0 (3.4)
subtype6 41 2.9 (4.9)
subtype7 14 2.4 (6.1)

Figure S70.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S78.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 281 240 432
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0074 (logrank test), Q value = 0.53

Table S79.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 900 112 0.0 - 234.2 (21.6)
subtype1 276 22 0.0 - 194.3 (27.2)
subtype2 225 37 0.0 - 211.5 (21.1)
subtype3 399 53 0.0 - 234.2 (20.3)

Figure S71.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 3.82e-05 (ANOVA), Q value = 0.0039

Table S80.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 950 58.4 (13.1)
subtype1 280 56.9 (12.5)
subtype2 239 56.4 (12.6)
subtype3 431 60.5 (13.5)

Figure S72.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 4.18e-05 (Chi-square test), Q value = 0.0042

Table S81.  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 77 72 11 9 324 217 2 130 25 52 15 1 17
subtype1 31 28 4 7 70 66 0 42 2 23 3 1 4
subtype2 15 18 1 1 107 50 1 24 3 12 3 0 4
subtype3 31 26 6 1 147 101 1 64 20 17 9 0 9

Figure S73.  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 = 1.17e-05 (Chi-square test), Q value = 0.0012

Table S82.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 251 552 113 34
subtype1 90 135 51 4
subtype2 57 153 22 7
subtype3 104 264 40 23

Figure S74.  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 = 0.0199 (Chi-square test), Q value = 1

Table S83.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 450 319 106 63
subtype1 128 99 26 25
subtype2 134 67 22 14
subtype3 188 153 58 24

Figure S75.  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 = 0.289 (Chi-square test), Q value = 1

Table S84.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 2 828 15 108
subtype1 1 236 3 41
subtype2 0 217 3 20
subtype3 1 375 9 47

Figure S76.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0704 (Fisher's exact test), Q value = 1

Table S85.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 943 10
subtype1 279 2
subtype2 240 0
subtype3 424 8

Figure S77.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.08e-35 (Chi-square test), Q value = 1.3e-33

Table S86.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 711 152 5 27 14 42
subtype1 0 153 110 0 11 1 6
subtype2 1 211 6 4 1 0 16
subtype3 0 347 36 1 15 13 20

Figure S78.  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 = 0.472 (Fisher's exact test), Q value = 1

Table S87.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 231 722
subtype1 67 214
subtype2 65 175
subtype3 99 333

Figure S79.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0932 (ANOVA), Q value = 1

Table S88.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 799 2.3 (4.6)
subtype1 257 2.8 (5.5)
subtype2 205 2.1 (4.8)
subtype3 337 2.1 (3.6)

Figure S80.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S89.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 256 422 268
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0808 (logrank test), Q value = 1

Table S90.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 893 109 0.0 - 234.2 (21.5)
subtype1 250 21 0.0 - 194.3 (20.8)
subtype2 388 50 0.0 - 234.2 (22.1)
subtype3 255 38 0.0 - 211.5 (21.9)

Figure S81.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.202 (ANOVA), Q value = 1

Table S91.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 943 58.5 (13.2)
subtype1 254 58.4 (12.2)
subtype2 422 59.2 (13.5)
subtype3 267 57.4 (13.5)

Figure S82.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.45e-05 (Chi-square test), Q value = 0.0015

Table S92.  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 77 72 11 9 321 213 2 131 25 52 14 1 17
subtype1 26 25 6 7 62 60 1 36 5 24 1 0 3
subtype2 36 31 4 1 137 99 1 63 15 15 10 0 10
subtype3 15 16 1 1 122 54 0 32 5 13 3 1 4

Figure S83.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 9.17e-06 (Chi-square test), Q value = 0.00098

Table S93.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 252 545 111 35
subtype1 83 120 46 6
subtype2 115 245 40 22
subtype3 54 180 25 7

Figure S84.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.00521 (Chi-square test), Q value = 0.39

Table S94.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 447 316 106 62
subtype1 113 90 25 26
subtype2 184 152 54 21
subtype3 150 74 27 15

Figure S85.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.00216 (Chi-square test), Q value = 0.17

Table S95.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 2 820 14 110
subtype1 1 207 1 47
subtype2 1 373 10 38
subtype3 0 240 3 25

Figure S86.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.513 (Fisher's exact test), Q value = 1

Table S96.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 936 10
subtype1 255 1
subtype2 416 6
subtype3 265 3

Figure S87.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 6.89e-41 (Chi-square test), Q value = 8.3e-39

Table S97.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 705 150 5 27 14 43
subtype1 0 127 112 0 7 5 5
subtype2 0 349 28 1 17 5 22
subtype3 1 229 10 4 3 4 16

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.106 (Fisher's exact test), Q value = 1

Table S98.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 229 717
subtype1 50 206
subtype2 112 310
subtype3 67 201

Figure S89.  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 = 0.0298 (ANOVA), Q value = 1

Table S99.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 795 2.3 (4.6)
subtype1 233 3.0 (5.8)
subtype2 334 2.0 (3.4)
subtype3 228 2.1 (4.5)

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S100.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 218 359 369
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0884 (logrank test), Q value = 1

Table S101.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 893 109 0.0 - 234.2 (21.5)
subtype1 207 36 0.1 - 211.5 (24.8)
subtype2 350 33 0.0 - 189.0 (20.5)
subtype3 336 40 0.0 - 234.2 (22.6)

Figure S91.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0265 (ANOVA), Q value = 1

Table S102.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 943 58.5 (13.2)
subtype1 217 56.5 (12.7)
subtype2 357 58.8 (12.5)
subtype3 369 59.5 (13.9)

Figure S92.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00445 (Chi-square test), Q value = 0.34

Table S103.  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 77 72 11 9 321 213 2 131 25 52 14 1 17
subtype1 15 15 1 1 94 44 0 26 3 11 4 0 3
subtype2 35 34 5 8 101 82 2 44 9 29 4 1 5
subtype3 27 23 5 0 126 87 0 61 13 12 6 0 9

Figure S93.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0107 (Chi-square test), Q value = 0.74

Table S104.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 252 545 111 35
subtype1 52 140 19 6
subtype2 111 181 54 12
subtype3 89 224 38 17

Figure S94.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.00168 (Chi-square test), Q value = 0.14

Table S105.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 447 316 106 62
subtype1 123 58 22 12
subtype2 167 124 33 34
subtype3 157 134 51 16

Figure S95.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.00171 (Chi-square test), Q value = 0.14

Table S106.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 2 820 14 110
subtype1 0 194 4 20
subtype2 1 291 4 63
subtype3 1 335 6 27

Figure S96.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.473 (Fisher's exact test), Q value = 1

Table S107.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 936 10
subtype1 217 1
subtype2 356 3
subtype3 363 6

Figure S97.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 5.63e-38 (Chi-square test), Q value = 6.7e-36

Table S108.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 705 150 5 27 14 43
subtype1 1 191 4 4 2 0 15
subtype2 0 203 130 1 12 3 10
subtype3 0 311 16 0 13 11 18

Figure S98.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.287 (Fisher's exact test), Q value = 1

Table S109.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 229 717
subtype1 55 163
subtype2 77 282
subtype3 97 272

Figure S99.  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 = 0.0992 (ANOVA), Q value = 1

Table S110.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 795 2.3 (4.6)
subtype1 189 2.1 (4.8)
subtype2 320 2.8 (5.3)
subtype3 286 2.0 (3.4)

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S111.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 252 190 183
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0115 (logrank test), Q value = 0.78

Table S112.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 581 71 0.0 - 194.3 (20.2)
subtype1 228 32 0.1 - 189.0 (21.2)
subtype2 186 14 0.0 - 194.3 (17.3)
subtype3 167 25 0.0 - 139.2 (20.6)

Figure S101.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.219 (ANOVA), Q value = 1

Table S113.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 622 58.2 (13.0)
subtype1 252 57.5 (13.6)
subtype2 188 59.6 (12.4)
subtype3 182 57.8 (12.7)

Figure S102.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1.43e-05 (Chi-square test), Q value = 0.0015

Table S114.  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 67 44 6 9 212 143 2 84 12 37 3 5
subtype1 34 22 2 2 85 57 0 34 5 6 2 3
subtype2 18 16 4 7 44 49 1 24 3 23 1 0
subtype3 15 6 0 0 83 37 1 26 4 8 0 2

Figure S103.  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 = 7.33e-06 (Chi-square test), Q value = 0.00079

Table S115.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 186 348 73 18
subtype1 96 130 17 9
subtype2 53 97 37 3
subtype3 37 121 19 6

Figure S104.  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 = 0.000166 (Chi-square test), Q value = 0.016

Table S116.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 297 218 65 39
subtype1 118 95 27 7
subtype2 79 71 15 24
subtype3 100 52 23 8

Figure S105.  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 = 0.00347 (Chi-square test), Q value = 0.27

Table S117.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 2 521 3 99
subtype1 2 221 2 27
subtype2 0 143 1 46
subtype3 0 157 0 26

Figure S106.  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 = 0.576 (Fisher's exact test), Q value = 1

Table S118.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 618 7
subtype1 248 4
subtype2 189 1
subtype3 181 2

Figure S107.  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 = 1.53e-33 (Chi-square test), Q value = 1.8e-31

Table S119.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 435 124 5 18 14 28
subtype1 0 199 21 0 13 5 14
subtype2 0 82 95 0 4 7 2
subtype3 1 154 8 5 1 2 12

Figure S108.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0366 (Fisher's exact test), Q value = 1

Table S120.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 131 494
subtype1 60 192
subtype2 28 162
subtype3 43 140

Figure S109.  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 = 0.00312 (ANOVA), Q value = 0.24

Table S121.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 559 2.5 (5.0)
subtype1 229 1.8 (3.4)
subtype2 174 3.5 (6.2)
subtype3 156 2.5 (5.3)

Figure S110.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S122.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 144 307 174
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.000645 (logrank test), Q value = 0.059

Table S123.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 581 71 0.0 - 194.3 (20.2)
subtype1 140 7 0.0 - 194.3 (14.3)
subtype2 279 36 0.1 - 189.0 (22.1)
subtype3 162 28 0.0 - 114.1 (22.1)

Figure S111.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.208 (ANOVA), Q value = 1

Table S124.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 622 58.2 (13.0)
subtype1 143 59.8 (12.9)
subtype2 306 57.9 (13.3)
subtype3 173 57.3 (12.7)

Figure S112.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.000106 (Chi-square test), Q value = 0.011

Table S125.  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 67 44 6 9 212 143 2 84 12 37 3 5
subtype1 11 11 1 7 37 38 2 16 3 17 1 0
subtype2 39 27 4 1 99 67 0 47 7 11 2 3
subtype3 17 6 1 1 76 38 0 21 2 9 0 2

Figure S113.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.000794 (Chi-square test), Q value = 0.071

Table S126.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 186 348 73 18
subtype1 37 73 30 4
subtype2 105 161 31 10
subtype3 44 114 12 4

Figure S114.  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 = 0.00767 (Chi-square test), Q value = 0.54

Table S127.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 297 218 65 39
subtype1 63 50 12 18
subtype2 140 117 33 12
subtype3 94 51 20 9

Figure S115.  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 = 0.00442 (Chi-square test), Q value = 0.34

Table S128.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients CM0 (I+) M0 M1 MX
ALL 2 521 3 99
subtype1 0 105 1 38
subtype2 2 264 2 39
subtype3 0 152 0 22

Figure S116.  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 = 0.612 (Fisher's exact test), Q value = 1

Table S129.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 618 7
subtype1 143 1
subtype2 302 5
subtype3 173 1

Figure S117.  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 = 9.97e-35 (Chi-square test), Q value = 1.2e-32

Table S130.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING CARCINOMA NOS INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 1 435 124 5 18 14 28
subtype1 0 55 82 0 2 3 2
subtype2 0 228 37 1 15 11 15
subtype3 1 152 5 4 1 0 11

Figure S118.  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 = 0.000996 (Fisher's exact test), Q value = 0.086

Table S131.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 131 494
subtype1 15 129
subtype2 75 232
subtype3 41 133

Figure S119.  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 = 0.158 (ANOVA), Q value = 1

Table S132.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 559 2.5 (5.0)
subtype1 127 3.2 (6.0)
subtype2 277 2.2 (4.2)
subtype3 155 2.4 (5.3)

Figure S120.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Methods & Data
Input

  • Cluster data file = BRCA-TP.mergedcluster.txt

  • Clinical data file = BRCA-TP.clin.merged.picked.txt

  • Number of patients = 966

  • Number of clustering approaches = 12

  • Number of selected clinical features = 10

  • 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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' 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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] 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)
[7] 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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