Correlation between aggregated molecular cancer subtypes and selected clinical features
Breast Invasive Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1VQ3169
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1VQ3169
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 975 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 3 subtypes that correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE', and 'HISTOLOGICAL.TYPE'.

  • 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.N.STAGE',  'PATHOLOGY.M.STAGE', and 'HISTOLOGICAL.TYPE'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.N.STAGE',  'PATHOLOGY.M.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 0.0464
(1.00) 		2.59e-08
(2.9e-06) 		0.0268
(1.00) 		0.0413
(1.00) 		0.000806
(0.0693) 		0.54
(1.00) 		0.0459
(1.00) 		1.04e-05
(0.00107) 		0.307
(1.00) 		0.397
(1.00)
mRNA cHierClus subtypes 0.899
(1.00) 		0.000796
(0.0693) 		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) 		0.0214
(1.00) 		0.646
(1.00)
Copy Number Ratio CNMF subtypes 0.231
(1.00) 		0.0363
(1.00) 		0.0202
(1.00) 		3.59e-08
(3.99e-06) 		0.00699
(0.489) 		0.324
(1.00) 		0.000153
(0.0144) 		5.38e-18
(6.13e-16) 		0.236
(1.00) 		0.259
(1.00)
METHLYATION CNMF 0.443
(1.00) 		2.26e-05
(0.0023) 		0.034
(1.00) 		0.00304
(0.237) 		0.00563
(0.411) 		0.35
(1.00) 		0.0107
(0.709) 		4.18e-12
(4.72e-10) 		0.0254
(1.00) 		0.022
(1.00)
RPPA CNMF subtypes 0.0474
(1.00) 		0.0287
(1.00) 		0.0315
(1.00) 		0.00137
(0.113) 		0.163
(1.00) 		0.323
(1.00) 		0.0564
(1.00) 		0.000131
(0.0126) 		0.65
(1.00) 		0.0305
(1.00)
RPPA cHierClus subtypes 0.378
(1.00) 		0.00121
(0.1) 		0.0442
(1.00) 		0.00703
(0.489) 		0.738
(1.00) 		0.665
(1.00) 		0.154
(1.00) 		0.000479
(0.0426) 		0.421
(1.00) 		0.0553
(1.00)
RNAseq CNMF subtypes 0.047
(1.00) 		0.000373
(0.0336) 		3.87e-07
(4.22e-05) 		0.000232
(0.0216) 		0.00105
(0.0884) 		0.127
(1.00) 		0.0133
(0.863) 		1.07e-33
(1.26e-31) 		0.794
(1.00) 		0.0318
(1.00)
RNAseq cHierClus subtypes 0.00863
(0.578) 		7.11e-05
(0.00697) 		3.89e-06
(0.000404) 		8.37e-07
(8.96e-05) 		0.018
(1.00) 		0.0283
(1.00) 		0.0717
(1.00) 		9.92e-36
(1.18e-33) 		0.444
(1.00) 		0.282
(1.00)
MIRSEQ CNMF 0.0197
(1.00) 		0.187
(1.00) 		1.74e-06
(0.000183) 		9.76e-05
(0.00946) 		0.00272
(0.215) 		0.000336
(0.0309) 		0.474
(1.00) 		5.22e-44
(6.27e-42) 		0.00424
(0.327) 		0.0275
(1.00)
MIRSEQ CHIERARCHICAL 0.115
(1.00) 		0.0289
(1.00) 		0.00224
(0.179) 		0.00797
(0.542) 		3.54e-05
(0.0035) 		0.0162
(1.00) 		0.192
(1.00) 		5.58e-31
(6.41e-29) 		0.0045
(0.342) 		0.275
(1.00)
MIRseq Mature CNMF subtypes 0.00668
(0.475) 		0.19
(1.00) 		5.79e-07
(6.25e-05) 		8.69e-07
(9.21e-05) 		2.31e-05
(0.00234) 		0.00156
(0.127) 		0.348
(1.00) 		1.44e-31
(1.67e-29) 		0.000339
(0.0309) 		0.00538
(0.398)
MIRseq Mature cHierClus subtypes 0.00473
(0.355) 		0.0921
(1.00) 		0.000136
(0.0129) 		0.0066
(0.475) 		0.000966
(0.0821) 		0.000542
(0.0477) 		0.358
(1.00) 		8.86e-34
(1.05e-31) 		3.15e-05
(0.00315) 		0.0187
(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 = 0.0464 (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 496 72 0.1 - 234.2 (28.5)
subtype1 19 3 0.3 - 92.0 (16.3)
subtype2 38 3 0.1 - 157.4 (43.5)
subtype3 112 18 0.1 - 188.7 (27.6)
subtype4 96 13 0.2 - 211.5 (28.3)
subtype5 107 11 0.3 - 234.2 (26.0)
subtype6 66 15 0.2 - 189.0 (31.9)
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 = 0.069

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 = 0.307 (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 168 358
subtype1 3 18
subtype2 14 25
subtype3 35 86
subtype4 41 60
subtype5 33 77
subtype6 22 52
subtype7 8 12
subtype8 12 28

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

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

P value = 0.397 (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 = 0.899 (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 496 72 0.1 - 234.2 (28.5)
subtype1 122 15 0.3 - 157.4 (31.5)
subtype2 121 20 0.3 - 211.5 (31.8)
subtype3 253 37 0.1 - 234.2 (25.2)

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 = 0.069

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 = 0.0214 (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 168 358
subtype1 42 82
subtype2 53 78
subtype3 73 198

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 355 248 87 224 43
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.231 (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 920 110 0.0 - 234.2 (21.6)
subtype1 342 36 0.0 - 234.2 (24.1)
subtype2 234 29 0.1 - 162.0 (21.3)
subtype3 86 8 0.0 - 189.0 (18.2)
subtype4 216 28 0.2 - 211.5 (22.8)
subtype5 42 9 0.2 - 220.9 (21.9)

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 = 0.0363 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 954 58.6 (13.2)
subtype1 353 58.9 (13.1)
subtype2 248 58.7 (14.0)
subtype3 87 60.6 (12.4)
subtype4 224 56.5 (12.7)
subtype5 42 61.7 (12.6)

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 = 0.0202 (Chi-square test), Q value = 1

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 73 9 9 321 217 2 135 27 54 15 1 16
subtype1 38 42 6 7 100 74 1 43 8 23 4 0 9
subtype2 12 13 2 1 83 56 1 46 13 12 6 0 3
subtype3 5 2 0 1 28 25 0 12 3 8 2 0 1
subtype4 18 14 1 0 92 52 0 27 3 9 3 1 3
subtype5 4 2 0 0 18 10 0 7 0 2 0 0 0

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 = 3.59e-08 (Chi-square test), Q value = 4e-06

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

nPatients T1 T2 T3 T4
ALL 248 556 114 36
subtype1 131 159 52 12
subtype2 48 163 24 13
subtype3 10 59 13 5
subtype4 45 151 21 5
subtype5 14 24 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 = 0.00699 (Chi-square test), Q value = 0.49

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

nPatients N0 N1 N2 N3
ALL 448 320 109 65
subtype1 174 118 29 27
subtype2 94 91 40 16
subtype3 36 32 8 10
subtype4 126 62 26 10
subtype5 18 17 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 = 0.324 (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 2 825 15 115
subtype1 1 293 4 57
subtype2 1 218 6 23
subtype3 0 74 2 11
subtype4 0 200 3 21
subtype5 0 40 0 3

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 = 0.000153 (Chi-square test), Q value = 0.014

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

nPatients FEMALE MALE
ALL 947 10
subtype1 355 0
subtype2 239 9
subtype3 86 1
subtype4 224 0
subtype5 43 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 = 5.38e-18 (Chi-square test), Q value = 6.1e-16

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 1 710 154 5 28 14 44
subtype1 0 214 94 0 15 10 22
subtype2 0 213 22 0 3 2 8
subtype3 0 54 27 0 5 0 1
subtype4 1 200 4 4 4 0 10
subtype5 0 29 7 1 1 2 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 = 0.236 (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 281 676
subtype1 94 261
subtype2 75 173
subtype3 21 66
subtype4 76 148
subtype5 15 28

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 = 0.259 (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 797 2.3 (4.6)
subtype1 307 2.2 (4.5)
subtype2 183 2.6 (4.1)
subtype3 71 3.3 (5.6)
subtype4 197 1.9 (4.9)
subtype5 39 2.5 (4.4)

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 108 153 101 135 50 105
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.443 (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 644 72 0.0 - 234.2 (21.2)
subtype1 108 17 0.2 - 211.5 (21.9)
subtype2 150 14 0.3 - 234.2 (18.1)
subtype3 101 13 0.0 - 130.1 (18.2)
subtype4 134 14 0.1 - 194.3 (23.8)
subtype5 47 5 0.1 - 157.4 (21.0)
subtype6 104 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 = 2.26e-05 (ANOVA), Q value = 0.0023

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

nPatients Mean (Std.Dev)
ALL 649 58.0 (13.1)
subtype1 107 55.4 (12.7)
subtype2 152 58.7 (12.3)
subtype3 101 63.8 (11.5)
subtype4 135 56.0 (14.5)
subtype5 50 57.6 (14.1)
subtype6 104 56.6 (12.2)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.034 (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 54 5 8 206 152 2 106 17 41 6 5
subtype1 5 6 0 0 49 28 0 12 1 3 2 1
subtype2 10 13 1 3 43 45 0 21 4 11 0 2
subtype3 7 5 0 2 30 22 1 21 2 8 2 1
subtype4 8 11 2 0 36 36 0 27 7 6 1 1
subtype5 3 7 0 1 14 5 0 10 2 7 1 0
subtype6 16 12 2 2 34 16 1 15 1 6 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 = 0.00304 (Chi-square test), Q value = 0.24

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

nPatients T1 T2 T3 T4
ALL 173 372 85 20
subtype1 18 74 12 3
subtype2 42 86 21 3
subtype3 18 62 18 3
subtype4 34 78 15 8
subtype5 15 25 8 2
subtype6 46 47 11 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 = 0.00563 (Chi-square test), Q value = 0.41

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

nPatients N0 N1 N2 N3
ALL 285 231 84 44
subtype1 62 31 11 4
subtype2 58 66 15 11
subtype3 47 25 19 8
subtype4 46 59 21 7
subtype5 19 16 7 7
subtype6 53 34 11 7

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 = 0.35 (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 1 541 6 104
subtype1 0 94 2 12
subtype2 1 127 0 25
subtype3 0 76 2 23
subtype4 0 118 1 16
subtype5 0 41 1 8
subtype6 0 85 0 20

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0107 (Chi-square test), Q value = 0.71

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

nPatients FEMALE MALE
ALL 645 7
subtype1 108 0
subtype2 152 1
subtype3 99 2
subtype4 134 1
subtype5 47 3
subtype6 105 0

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 4.18e-12 (Chi-square test), Q value = 4.7e-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 1 447 131 5 23 12 32
subtype1 1 87 3 3 1 0 12
subtype2 0 79 48 0 12 5 9
subtype3 0 64 32 0 3 0 2
subtype4 0 115 7 0 3 4 6
subtype5 0 35 10 0 2 2 1
subtype6 0 67 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 = 0.0254 (Chi-square test), Q value = 1

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

nPatients NO YES
ALL 214 438
subtype1 42 66
subtype2 42 111
subtype3 29 72
subtype4 58 77
subtype5 13 37
subtype6 30 75

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 = 0.022 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 599 2.6 (4.8)
subtype1 103 1.7 (3.1)
subtype2 147 2.6 (4.7)
subtype3 91 3.2 (6.0)
subtype4 116 2.7 (4.4)
subtype5 44 4.5 (6.8)
subtype6 98 2.0 (4.4)

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 = 0.0474 (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 (29.9)
subtype1 136 25 0.2 - 186.4 (30.0)
subtype2 132 13 0.2 - 146.5 (25.8)
subtype3 113 13 0.3 - 189.0 (34.2)

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 = 0.11

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 = 0.65 (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 144 264
subtype1 57 95
subtype2 44 92
subtype3 43 77

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 = 0.378 (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 (29.9)
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 (31.7)

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 = 0.49

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 = 0.043

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 = 0.421 (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 144 264
subtype1 52 111
subtype2 41 74
subtype3 51 79

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
Number of samples 264 181 524
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.047 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 932 113 0.0 - 234.2 (21.8)
subtype1 252 40 0.0 - 211.5 (23.2)
subtype2 180 14 0.0 - 194.3 (28.0)
subtype3 500 59 0.0 - 234.2 (20.5)

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 = 0.000373 (ANOVA), Q value = 0.034

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

nPatients Mean (Std.Dev)
ALL 966 58.5 (13.2)
subtype1 263 56.5 (12.4)
subtype2 180 57.0 (12.5)
subtype3 523 60.1 (13.6)

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 = 3.87e-07 (Chi-square test), Q value = 4.2e-05

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 78 75 10 9 326 221 2 134 27 53 15 1 17
subtype1 15 19 1 1 113 59 1 30 4 12 4 0 4
subtype2 21 18 3 7 40 42 0 22 1 21 2 1 3
subtype3 42 38 6 1 173 120 1 82 22 20 9 0 10

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 = 0.000232 (Chi-square test), Q value = 0.022

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

nPatients T1 T2 T3 T4
ALL 254 561 115 36
subtype1 58 168 29 8
subtype2 60 84 34 2
subtype3 136 309 52 26

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 = 0.00105 (Chi-square test), Q value = 0.088

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

nPatients N0 N1 N2 N3
ALL 456 325 108 64
subtype1 141 83 24 14
subtype2 82 61 13 23
subtype3 233 181 71 27

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 = 0.127 (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 2 837 15 115
subtype1 0 236 4 24
subtype2 1 146 2 32
subtype3 1 455 9 59

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 = 0.0133 (Fisher's exact test), Q value = 0.86

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

nPatients FEMALE MALE
ALL 959 10
subtype1 264 0
subtype2 181 0
subtype3 514 10

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 = 1.07e-33 (Chi-square test), Q value = 1.3e-31

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 1 722 155 5 28 14 43
subtype1 1 231 7 5 2 0 17
subtype2 0 88 81 0 9 1 2
subtype3 0 403 67 0 17 13 24

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 = 0.794 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 282 687
subtype1 81 183
subtype2 51 130
subtype3 150 374

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 = 0.0318 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 807 2.3 (4.6)
subtype1 224 2.2 (4.9)
subtype2 166 3.1 (5.9)
subtype3 417 2.1 (3.7)

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 222 433 314
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00863 (logrank test), Q value = 0.58

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

nPatients nDeath Duration Range (Median), Month
ALL 932 113 0.0 - 234.2 (21.8)
subtype1 213 32 0.0 - 211.5 (22.0)
subtype2 407 56 0.0 - 234.2 (20.4)
subtype3 312 25 0.0 - 194.3 (27.2)

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 = 7.11e-05 (ANOVA), Q value = 0.007

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

nPatients Mean (Std.Dev)
ALL 966 58.5 (13.2)
subtype1 221 56.4 (12.6)
subtype2 433 60.6 (13.8)
subtype3 312 57.2 (12.3)

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 = 3.89e-06 (Chi-square test), Q value = 4e-04

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 78 75 10 9 326 221 2 134 27 53 15 1 17
subtype1 14 18 1 0 100 46 1 22 2 11 3 0 3
subtype2 29 26 5 2 142 105 1 65 24 17 9 0 8
subtype3 35 31 4 7 84 70 0 47 1 25 3 1 6

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 = 8.37e-07 (Chi-square test), Q value = 9e-05

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

nPatients T1 T2 T3 T4
ALL 254 561 115 36
subtype1 54 140 22 5
subtype2 96 266 42 28
subtype3 104 155 51 3

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 = 0.018 (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 456 325 108 64
subtype1 127 62 20 13
subtype2 184 156 57 24
subtype3 145 107 31 27

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 = 0.0283 (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 2 837 15 115
subtype1 0 201 3 18
subtype2 0 377 9 47
subtype3 2 259 3 50

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 = 0.0717 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 959 10
subtype1 222 0
subtype2 425 8
subtype3 312 2

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 = 9.92e-36 (Chi-square test), Q value = 1.2e-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 1 722 155 5 28 14 43
subtype1 1 193 5 4 2 0 16
subtype2 0 355 32 0 14 13 19
subtype3 0 174 118 1 12 1 8

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 = 0.444 (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 282 687
subtype1 72 150
subtype2 120 313
subtype3 90 224

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 = 0.282 (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 807 2.3 (4.6)
subtype1 192 2.0 (4.7)
subtype2 328 2.2 (3.8)
subtype3 287 2.6 (5.2)

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 407 266 281
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0197 (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 917 110 0.0 - 234.2 (21.6)
subtype1 384 49 0.0 - 234.2 (22.6)
subtype2 264 20 0.0 - 194.3 (20.7)
subtype3 269 41 0.0 - 211.5 (21.5)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.187 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 951 58.6 (13.2)
subtype1 407 59.2 (13.6)
subtype2 264 58.8 (12.5)
subtype3 280 57.4 (13.3)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.74e-06 (Chi-square test), Q value = 0.00018

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 75 10 9 321 216 2 132 26 53 14 1 17
subtype1 37 32 3 1 131 96 1 60 14 14 9 0 9
subtype2 23 28 6 8 65 61 1 36 7 27 1 0 3
subtype3 17 15 1 0 125 59 0 36 5 12 4 1 5

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 = 9.76e-05 (Chi-square test), Q value = 0.0095

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

nPatients T1 T2 T3 T4
ALL 253 550 112 36
subtype1 117 232 39 19
subtype2 82 129 46 8
subtype3 54 189 27 9

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 = 0.00272 (Chi-square test), Q value = 0.22

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

nPatients N0 N1 N2 N3
ALL 450 319 106 63
subtype1 178 148 52 20
subtype2 119 91 24 29
subtype3 153 80 30 14

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 = 0.000336 (Chi-square test), Q value = 0.031

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

nPatients CM0 (I+) M0 M1 MX
ALL 2 822 14 116
subtype1 1 361 9 36
subtype2 1 211 1 53
subtype3 0 250 4 27

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.474 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 944 10
subtype1 401 6
subtype2 265 1
subtype3 278 3

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 5.22e-44 (Chi-square test), Q value = 6.3e-42

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 1 709 153 5 27 14 44
subtype1 0 335 29 0 16 4 23
subtype2 0 132 117 0 7 5 5
subtype3 1 242 7 5 4 5 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 = 0.00424 (Fisher's exact test), Q value = 0.33

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

nPatients NO YES
ALL 277 677
subtype1 133 274
subtype2 57 209
subtype3 87 194

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 = 0.0275 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 798 2.3 (4.6)
subtype1 326 2.1 (3.7)
subtype2 236 3.0 (5.7)
subtype3 236 2.0 (4.3)

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 242 302 410
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.115 (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 917 110 0.0 - 234.2 (21.6)
subtype1 233 38 0.0 - 211.5 (23.7)
subtype2 281 32 0.1 - 234.2 (21.9)
subtype3 403 40 0.0 - 189.0 (20.9)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0289 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 951 58.6 (13.2)
subtype1 241 56.6 (12.5)
subtype2 302 59.5 (14.4)
subtype3 408 59.0 (12.6)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00224 (Chi-square test), Q value = 0.18

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 75 10 9 321 216 2 132 26 53 14 1 17
subtype1 15 17 1 1 104 50 0 29 3 12 5 0 4
subtype2 20 20 3 0 95 75 0 56 14 11 3 0 5
subtype3 42 38 6 8 122 91 2 47 9 30 6 1 8

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 = 0.00797 (Chi-square test), Q value = 0.54

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

nPatients T1 T2 T3 T4
ALL 253 550 112 36
subtype1 53 159 23 6
subtype2 72 180 33 16
subtype3 128 211 56 14

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 = 3.54e-05 (Chi-square test), Q value = 0.0035

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

nPatients N0 N1 N2 N3
ALL 450 319 106 63
subtype1 137 62 27 13
subtype2 116 118 46 14
subtype3 197 139 33 36

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 = 0.0162 (Chi-square test), Q value = 1

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

nPatients CM0 (I+) M0 M1 MX
ALL 2 822 14 116
subtype1 0 213 5 24
subtype2 1 274 3 24
subtype3 1 335 6 68

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.192 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 944 10
subtype1 241 1
subtype2 296 6
subtype3 407 3

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 5.58e-31 (Chi-square test), Q value = 6.4e-29

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 1 709 153 5 27 14 44
subtype1 1 212 7 4 2 0 15
subtype2 0 252 13 0 12 11 14
subtype3 0 245 133 1 13 3 15

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 = 0.0045 (Fisher's exact test), Q value = 0.34

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

nPatients NO YES
ALL 277 677
subtype1 76 166
subtype2 104 198
subtype3 97 313

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 = 0.275 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 798 2.3 (4.6)
subtype1 206 2.0 (4.3)
subtype2 233 2.2 (3.5)
subtype3 359 2.6 (5.2)

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 252 198 183
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00668 (logrank test), Q value = 0.48

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

nPatients nDeath Duration Range (Median), Month
ALL 605 72 0.0 - 194.3 (20.4)
subtype1 239 33 0.1 - 189.0 (21.6)
subtype2 194 14 0.0 - 194.3 (18.3)
subtype3 172 25 0.0 - 130.2 (20.2)

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 = 0.19 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 630 58.3 (13.1)
subtype1 252 57.3 (13.6)
subtype2 196 59.6 (12.6)
subtype3 182 58.1 (12.9)

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 = 5.79e-07 (Chi-square test), Q value = 6.3e-05

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 47 5 9 212 146 2 85 13 38 3 5
subtype1 33 23 1 2 85 58 0 34 6 6 2 2
subtype2 19 17 4 7 42 52 2 26 4 24 1 0
subtype3 15 7 0 0 85 36 0 25 3 8 0 3

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 = 8.69e-07 (Chi-square test), Q value = 9.2e-05

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

nPatients T1 T2 T3 T4
ALL 187 353 74 19
subtype1 96 130 17 9
subtype2 55 99 40 4
subtype3 36 124 17 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 = 2.31e-05 (Chi-square test), Q value = 0.0023

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

nPatients N0 N1 N2 N3
ALL 300 221 65 40
subtype1 116 98 27 7
subtype2 80 75 16 25
subtype3 104 48 22 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 = 0.00156 (Chi-square test), Q value = 0.13

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

nPatients CM0 (I+) M0 M1 MX
ALL 2 523 3 105
subtype1 2 221 2 27
subtype2 0 147 1 50
subtype3 0 155 0 28

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 = 0.348 (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 626 7
subtype1 247 5
subtype2 197 1
subtype3 182 1

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 = 1.44e-31 (Chi-square test), Q value = 1.7e-29

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 1 439 127 5 18 14 29
subtype1 0 201 21 0 13 5 12
subtype2 0 87 97 0 4 6 4
subtype3 1 151 9 5 1 3 13

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 = 0.000339 (Fisher's exact test), Q value = 0.031

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

nPatients NO YES
ALL 173 460
subtype1 83 169
subtype2 34 164
subtype3 56 127

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 = 0.00538 (ANOVA), Q value = 0.4

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

nPatients Mean (Std.Dev)
ALL 562 2.5 (5.0)
subtype1 230 1.9 (3.7)
subtype2 177 3.5 (6.1)
subtype3 155 2.3 (5.0)

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 133 157 343
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.00473 (logrank test), Q value = 0.35

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

nPatients nDeath Duration Range (Median), Month
ALL 605 72 0.0 - 194.3 (20.4)
subtype1 129 20 0.2 - 114.1 (22.0)
subtype2 156 8 0.0 - 194.3 (16.9)
subtype3 320 44 0.0 - 189.0 (21.5)

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 = 0.0921 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 630 58.3 (13.1)
subtype1 132 57.6 (12.9)
subtype2 156 60.2 (13.2)
subtype3 342 57.6 (13.1)

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 = 0.000136 (Chi-square test), Q value = 0.013

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 47 5 9 212 146 2 85 13 38 3 5
subtype1 13 6 1 0 59 29 0 13 2 8 0 1
subtype2 11 13 2 7 37 40 2 21 4 19 1 0
subtype3 43 28 2 2 116 77 0 51 7 11 2 4

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 = 0.0066 (Chi-square test), Q value = 0.48

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

nPatients T1 T2 T3 T4
ALL 187 353 74 19
subtype1 32 85 12 4
subtype2 40 81 31 5
subtype3 115 187 31 10

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 = 0.000966 (Chi-square test), Q value = 0.082

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

nPatients N0 N1 N2 N3
ALL 300 221 65 40
subtype1 77 37 11 8
subtype2 66 53 16 20
subtype3 157 131 38 12

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 = 0.000542 (Chi-square test), Q value = 0.048

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

nPatients CM0 (I+) M0 M1 MX
ALL 2 523 3 105
subtype1 0 111 0 22
subtype2 0 112 1 44
subtype3 2 300 2 39

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 = 0.358 (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 626 7
subtype1 133 0
subtype2 156 1
subtype3 337 6

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 = 8.86e-34 (Chi-square test), Q value = 1e-31

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 1 439 127 5 18 14 29
subtype1 1 111 6 4 0 0 11
subtype2 0 63 86 0 2 4 2
subtype3 0 265 35 1 16 10 16

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 = 3.15e-05 (Fisher's exact test), Q value = 0.0032

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

nPatients NO YES
ALL 173 460
subtype1 39 94
subtype2 22 135
subtype3 112 231

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 = 0.0187 (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 562 2.5 (5.0)
subtype1 119 2.3 (5.3)
subtype2 135 3.5 (6.3)
subtype3 308 2.1 (4.1)

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.merged_data.txt

  • Number of patients = 975

  • 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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