Correlation between aggregated molecular cancer subtypes and selected clinical features
Breast Invasive Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1VX0F3V
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/C1VX0F3V
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 988 patients, 51 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',  'PATHOLOGY.N.STAGE',  'PATHOLOGY.M.STAGE',  'GENDER', and 'HISTOLOGICAL.TYPE'.

  • 6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.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 6 subtypes that correlate to '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'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE',  'PATHOLOGY.M.STAGE',  'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

  • 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.T.STAGE',  'PATHOLOGY.N.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, 51 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 0.0477
(1.00) 		3.35e-08
(3.66e-06) 		0.0268
(1.00) 		0.0413
(1.00) 		0.000806
(0.0645) 		0.54
(1.00) 		0.0459
(1.00) 		1.04e-05
(0.00105) 		0.307
(1.00) 		0.388
(1.00)
mRNA cHierClus subtypes 0.91
(1.00) 		0.000328
(0.0289) 		0.0805
(1.00) 		0.0433
(1.00) 		0.27
(1.00) 		0.143
(1.00) 		0.491
(1.00) 		1.22e-07
(1.32e-05) 		0.0214
(1.00) 		0.634
(1.00)
Copy Number Ratio CNMF subtypes 0.171
(1.00) 		0.0253
(1.00) 		0.00575
(0.385) 		1.95e-09
(2.18e-07) 		0.00219
(0.16) 		0.00297
(0.214) 		9.8e-05
(0.00912) 		3.29e-19
(3.75e-17) 		0.108
(1.00) 		0.43
(1.00)
METHLYATION CNMF 0.366
(1.00) 		2.95e-06
(0.000307) 		0.0021
(0.155) 		0.000412
(0.0355) 		0.000552
(0.046) 		0.175
(1.00) 		0.00995
(0.597) 		3.97e-12
(4.49e-10) 		0.0337
(1.00) 		0.00791
(0.49)
RPPA CNMF subtypes 0.0534
(1.00) 		0.0239
(1.00) 		0.0315
(1.00) 		0.00137
(0.103) 		0.163
(1.00) 		0.323
(1.00) 		0.0564
(1.00) 		0.000131
(0.012) 		0.65
(1.00) 		0.0309
(1.00)
RPPA cHierClus subtypes 0.392
(1.00) 		0.0012
(0.0927) 		0.0442
(1.00) 		0.00703
(0.45) 		0.738
(1.00) 		0.665
(1.00) 		0.154
(1.00) 		0.000479
(0.0407) 		0.421
(1.00) 		0.0563
(1.00)
RNAseq CNMF subtypes 0.0554
(1.00) 		3.82e-05
(0.00374) 		7.49e-05
(0.00712) 		0.000747
(0.0613) 		1.56e-05
(0.00156) 		0.214
(1.00) 		0.00726
(0.457) 		6.6e-33
(7.65e-31) 		0.15
(1.00) 		0.000234
(0.0211)
RNAseq cHierClus subtypes 0.00936
(0.571) 		1.98e-08
(2.17e-06) 		1.23e-06
(0.000131) 		7.16e-07
(7.66e-05) 		0.014
(0.828) 		0.0229
(1.00) 		0.0635
(1.00) 		1.39e-34
(1.63e-32) 		0.41
(1.00) 		0.272
(1.00)
MIRSEQ CNMF 0.19
(1.00) 		0.0571
(1.00) 		0.000403
(0.0351) 		8e-05
(0.00752) 		0.161
(1.00) 		0.233
(1.00) 		0.358
(1.00) 		5.1e-28
(5.86e-26) 		0.00127
(0.0967) 		0.147
(1.00)
MIRSEQ CHIERARCHICAL 0.209
(1.00) 		0.00311
(0.221) 		3.5e-09
(3.88e-07) 		0.000547
(0.046) 		0.000944
(0.0746) 		4.17e-05
(0.00404) 		0.0969
(1.00) 		2.49e-50
(2.99e-48) 		0.0011
(0.0858) 		0.0247
(1.00)
MIRseq Mature CNMF subtypes 0.00587
(0.385) 		0.0409
(1.00) 		2.24e-06
(0.000235) 		4.01e-06
(0.00041) 		1.62e-05
(0.0016) 		0.00343
(0.24) 		0.507
(1.00) 		1.99e-35
(2.35e-33) 		0.000307
(0.0273) 		0.00584
(0.385)
MIRseq Mature cHierClus subtypes 0.00476
(0.328) 		0.138
(1.00) 		3.11e-06
(0.00032) 		0.00075
(0.0613) 		5.96e-05
(0.00572) 		0.00523
(0.355) 		0.89
(1.00) 		1.95e-40
(2.32e-38) 		0.000183
(0.0167) 		0.0368
(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 = 0.0477 (logrank test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 3.35e-08 (ANOVA), Q value = 3.7e-06

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

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

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0268 (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 = 0.064

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 = 0.001

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 = 0.388 (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 408 1.8 (3.5)
subtype1 14 1.4 (2.2)
subtype2 34 1.9 (2.4)
subtype3 86 1.6 (2.3)
subtype4 93 1.4 (3.2)
subtype5 84 1.8 (3.9)
subtype6 47 2.7 (3.5)
subtype7 15 1.2 (2.5)
subtype8 35 2.7 (6.3)

Figure S10.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

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 = 0.91 (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 511 72 0.1 - 234.3 (27.2)
subtype1 123 15 0.3 - 157.4 (31.2)
subtype2 128 20 0.1 - 211.6 (31.4)
subtype3 260 37 0.2 - 234.3 (24.6)

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 = 0.000328 (ANOVA), Q value = 0.029

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

nPatients Mean (Std.Dev)
ALL 520 58.1 (13.2)
subtype1 124 56.4 (13.0)
subtype2 130 55.2 (13.1)
subtype3 266 60.3 (13.0)

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 = 0.634 (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 408 1.8 (3.5)
subtype1 110 2.1 (4.2)
subtype2 115 1.7 (3.3)
subtype3 183 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 355 244 90 238 44
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.171 (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 950 112 0.0 - 234.3 (21.5)
subtype1 346 36 0.0 - 234.3 (23.5)
subtype2 236 30 0.1 - 162.1 (22.1)
subtype3 88 8 0.0 - 189.0 (19.7)
subtype4 236 29 0.0 - 211.6 (21.2)
subtype5 44 9 0.2 - 220.9 (20.5)

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 = 0.0253 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 958 58.7 (13.1)
subtype1 352 58.9 (13.1)
subtype2 239 59.2 (13.8)
subtype3 88 60.5 (12.5)
subtype4 237 56.5 (12.8)
subtype5 42 61.8 (11.7)

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 = 0.00575 (Chi-square test), Q value = 0.38

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 74 9 9 327 221 2 136 27 56 15 1 16
subtype1 39 41 7 7 94 80 1 43 7 24 4 0 8
subtype2 12 14 1 1 81 54 1 46 13 12 6 0 3
subtype3 5 3 0 1 31 24 0 11 4 8 2 0 1
subtype4 18 14 1 0 101 54 0 29 3 10 3 1 3
subtype5 3 2 0 0 20 9 0 7 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 = 1.95e-09 (Chi-square test), Q value = 2.2e-07

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

nPatients T1 T2 T3 T4
ALL 249 568 115 36
subtype1 131 159 54 10
subtype2 47 159 24 14
subtype3 11 60 13 6
subtype4 46 164 21 5
subtype5 14 26 3 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 = 0.00219 (Chi-square test), Q value = 0.16

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

nPatients N0 N1 N2 N3
ALL 455 325 109 67
subtype1 169 124 28 27
subtype2 94 86 40 16
subtype3 40 32 7 11
subtype4 135 64 28 11
subtype5 17 19 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 = 0.00297 (Chi-square test), Q value = 0.21

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

nPatients CM0 (I+) M0 M1 MX
ALL 2 836 15 118
subtype1 1 288 4 62
subtype2 0 221 6 17
subtype3 0 77 2 11
subtype4 0 211 3 24
subtype5 1 39 0 4

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 = 9.8e-05 (Chi-square test), Q value = 0.0091

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

nPatients FEMALE MALE
ALL 961 10
subtype1 355 0
subtype2 235 9
subtype3 89 1
subtype4 238 0
subtype5 44 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 = 3.29e-19 (Chi-square test), Q value = 3.7e-17

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 1 719 158 5 29 14 44
subtype1 0 208 100 0 15 10 22
subtype2 0 212 17 0 4 3 8
subtype3 0 56 28 0 5 0 1
subtype4 1 210 7 4 4 0 11
subtype5 0 33 6 1 1 1 2

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 = 0.108 (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 282 689
subtype1 88 267
subtype2 78 166
subtype3 22 68
subtype4 79 159
subtype5 15 29

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 = 0.43 (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 814 2.3 (4.6)
subtype1 309 2.3 (4.5)
subtype2 185 2.7 (4.4)
subtype3 73 2.9 (5.2)
subtype4 209 1.9 (4.7)
subtype5 38 2.5 (4.3)

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 114 168 98 130 51 104
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.366 (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 659 74 0.0 - 234.3 (21.4)
subtype1 114 18 0.2 - 211.6 (22.0)
subtype2 164 16 0.2 - 234.3 (18.7)
subtype3 98 13 0.0 - 130.1 (18.1)
subtype4 129 13 0.2 - 194.3 (23.9)
subtype5 51 5 0.6 - 157.4 (21.9)
subtype6 103 9 0.0 - 130.2 (25.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 2.95e-06 (ANOVA), Q value = 0.00031

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

nPatients Mean (Std.Dev)
ALL 654 58.1 (13.0)
subtype1 113 55.1 (12.0)
subtype2 167 59.4 (12.1)
subtype3 98 63.9 (11.7)
subtype4 124 56.1 (14.7)
subtype5 49 57.7 (13.8)
subtype6 103 56.1 (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 = 0.0021 (Chi-square test), Q value = 0.16

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 55 5 8 212 155 2 107 17 43 6 5
subtype1 5 6 0 0 53 30 0 12 1 3 2 1
subtype2 11 18 2 3 44 49 0 24 4 11 0 2
subtype3 6 6 0 2 28 23 1 20 2 7 2 1
subtype4 8 7 1 0 38 33 0 27 7 7 1 1
subtype5 3 7 0 1 13 5 0 9 2 10 1 0
subtype6 16 11 2 2 36 15 1 15 1 5 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 = 0.000412 (Chi-square test), Q value = 0.035

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

nPatients T1 T2 T3 T4
ALL 174 383 86 20
subtype1 18 80 12 3
subtype2 51 92 21 3
subtype3 18 59 18 3
subtype4 28 79 15 8
subtype5 13 27 9 2
subtype6 46 46 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 = 0.000552 (Chi-square test), Q value = 0.046

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

nPatients N0 N1 N2 N3
ALL 292 235 84 46
subtype1 66 33 11 4
subtype2 64 73 17 11
subtype3 45 26 18 7
subtype4 45 54 21 8
subtype5 19 15 6 10
subtype6 53 34 11 6

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 = 0.175 (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 1 552 6 106
subtype1 0 101 2 11
subtype2 1 140 0 27
subtype3 0 73 2 23
subtype4 0 114 1 15
subtype5 0 42 1 8
subtype6 0 82 0 22

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00995 (Chi-square test), Q value = 0.6

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

nPatients FEMALE MALE
ALL 658 7
subtype1 114 0
subtype2 167 1
subtype3 96 2
subtype4 129 1
subtype5 48 3
subtype6 104 0

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 3.97e-12 (Chi-square test), Q value = 4.5e-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 1 456 134 5 24 12 32
subtype1 1 93 3 3 1 0 12
subtype2 0 87 49 0 13 7 12
subtype3 0 62 31 0 3 0 2
subtype4 0 111 8 0 4 3 4
subtype5 0 35 14 0 1 1 0
subtype6 0 68 29 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 = 0.0337 (Chi-square test), Q value = 1

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

nPatients NO YES
ALL 215 450
subtype1 44 70
subtype2 48 120
subtype3 28 70
subtype4 54 76
subtype5 11 40
subtype6 30 74

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 = 0.00791 (ANOVA), Q value = 0.49

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

nPatients Mean (Std.Dev)
ALL 613 2.6 (4.8)
subtype1 111 1.6 (3.0)
subtype2 160 2.5 (4.6)
subtype3 88 3.2 (6.0)
subtype4 113 2.8 (4.5)
subtype5 44 4.6 (6.8)
subtype6 97 2.0 (4.5)

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 = 0.0534 (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 395 51 0.1 - 189.0 (28.6)
subtype1 145 25 0.1 - 186.5 (28.0)
subtype2 134 13 0.2 - 146.5 (25.2)
subtype3 116 13 0.3 - 189.0 (33.0)

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 = 0.0239 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 406 57.9 (13.1)
subtype1 152 56.1 (13.4)
subtype2 135 60.3 (13.7)
subtype3 119 57.6 (11.6)

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 = 0.1

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 = 0.012

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 = 0.0309 (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 332 1.9 (3.5)
subtype1 116 2.0 (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 = 0.392 (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 395 51 0.1 - 189.0 (28.6)
subtype1 157 20 0.2 - 129.7 (27.2)
subtype2 113 12 0.2 - 173.0 (31.0)
subtype3 125 19 0.1 - 189.0 (31.0)

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 = 0.0012 (ANOVA), Q value = 0.093

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

nPatients Mean (Std.Dev)
ALL 406 57.9 (13.1)
subtype1 162 60.5 (13.6)
subtype2 115 57.6 (11.8)
subtype3 129 54.9 (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 = 0.45

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 = 0.041

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 = 0.0563 (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 332 1.9 (3.5)
subtype1 126 1.3 (2.1)
subtype2 99 2.4 (4.5)
subtype3 107 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
Number of samples 183 94 241 166 255 45
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0554 (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 963 115 0.0 - 234.3 (21.6)
subtype1 182 24 0.2 - 211.6 (22.2)
subtype2 94 9 0.0 - 157.4 (25.4)
subtype3 237 22 0.0 - 234.3 (19.0)
subtype4 160 26 0.0 - 146.5 (21.9)
subtype5 246 30 0.2 - 188.7 (22.9)
subtype6 44 4 0.3 - 194.3 (34.5)

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 = 3.82e-05 (ANOVA), Q value = 0.0037

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

nPatients Mean (Std.Dev)
ALL 971 58.6 (13.1)
subtype1 183 55.2 (12.2)
subtype2 92 55.0 (12.8)
subtype3 239 60.4 (12.2)
subtype4 162 59.7 (13.0)
subtype5 250 59.7 (14.4)
subtype6 45 60.3 (12.2)

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 = 7.49e-05 (Chi-square test), Q value = 0.0071

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 77 10 9 332 224 2 135 27 56 15 1 17
subtype1 12 13 0 0 85 40 0 19 2 6 3 0 2
subtype2 6 7 2 4 20 24 1 12 1 14 1 1 1
subtype3 25 28 1 2 83 45 0 28 7 11 4 0 7
subtype4 12 10 1 1 54 40 0 28 4 9 4 0 3
subtype5 15 18 5 1 76 65 1 43 13 11 3 0 4
subtype6 8 1 1 1 14 10 0 5 0 5 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 = 0.000747 (Chi-square test), Q value = 0.061

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

nPatients T1 T2 T3 T4
ALL 256 573 116 36
subtype1 37 121 19 5
subtype2 23 48 21 1
subtype3 85 119 27 10
subtype4 36 110 14 6
subtype5 63 150 27 14
subtype6 12 25 8 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 = 1.56e-05 (Chi-square test), Q value = 0.0016

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

nPatients N0 N1 N2 N3
ALL 464 329 108 67
subtype1 112 49 15 7
subtype2 35 37 6 15
subtype3 125 77 21 15
subtype4 65 58 27 12
subtype5 102 97 35 13
subtype6 25 11 4 5

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 = 0.214 (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 2 848 15 119
subtype1 0 165 3 15
subtype2 1 79 1 13
subtype3 1 194 4 42
subtype4 0 142 4 20
subtype5 0 226 3 26
subtype6 0 42 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 = 0.00726 (Chi-square test), Q value = 0.46

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

nPatients FEMALE MALE
ALL 974 10
subtype1 183 0
subtype2 94 0
subtype3 240 1
subtype4 165 1
subtype5 247 8
subtype6 45 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 = 6.6e-33 (Chi-square test), Q value = 7.7e-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 1 733 158 5 29 14 43
subtype1 1 159 1 4 1 2 14
subtype2 0 42 42 0 8 1 1
subtype3 0 152 66 0 9 3 11
subtype4 0 153 8 1 1 0 3
subtype5 0 204 20 0 9 8 14
subtype6 0 23 21 0 1 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 = 0.15 (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 285 699
subtype1 60 123
subtype2 23 71
subtype3 59 182
subtype4 43 123
subtype5 85 170
subtype6 15 30

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 = 0.000234 (ANOVA), Q value = 0.021

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

nPatients Mean (Std.Dev)
ALL 825 2.3 (4.5)
subtype1 169 1.4 (2.9)
subtype2 87 4.0 (7.1)
subtype3 204 2.0 (4.1)
subtype4 121 3.0 (5.4)
subtype5 201 2.2 (3.8)
subtype6 43 2.4 (4.7)

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 319 428 237
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00936 (logrank test), Q value = 0.57

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

nPatients nDeath Duration Range (Median), Month
ALL 963 115 0.0 - 234.3 (21.6)
subtype1 315 25 0.0 - 194.3 (25.0)
subtype2 414 54 0.0 - 234.3 (20.7)
subtype3 234 36 0.0 - 211.6 (21.3)

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 = 1.98e-08 (ANOVA), Q value = 2.2e-06

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

nPatients Mean (Std.Dev)
ALL 971 58.6 (13.1)
subtype1 316 56.4 (12.6)
subtype2 420 61.5 (13.4)
subtype3 235 56.5 (12.5)

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 = 1.23e-06 (Chi-square test), Q value = 0.00013

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 77 10 9 332 224 2 135 27 56 15 1 17
subtype1 40 30 5 7 83 68 0 47 2 27 3 1 6
subtype2 25 28 4 1 143 106 1 65 22 17 8 0 8
subtype3 13 19 1 1 106 50 1 23 3 12 4 0 3

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 = 7.16e-07 (Chi-square test), Q value = 7.7e-05

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

nPatients T1 T2 T3 T4
ALL 256 573 116 36
subtype1 107 155 52 4
subtype2 95 264 42 26
subtype3 54 154 22 6

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 = 0.014 (Chi-square test), Q value = 0.83

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

nPatients N0 N1 N2 N3
ALL 464 329 108 67
subtype1 150 105 31 29
subtype2 180 157 55 24
subtype3 134 67 22 14

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 = 0.0229 (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 2 848 15 119
subtype1 2 261 3 53
subtype2 0 375 8 45
subtype3 0 212 4 21

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 = 0.0635 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 974 10
subtype1 317 2
subtype2 420 8
subtype3 237 0

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 = 1.39e-34 (Chi-square test), Q value = 1.6e-32

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 1 733 158 5 29 14 43
subtype1 0 176 119 1 13 2 8
subtype2 0 351 32 0 14 12 19
subtype3 1 206 7 4 2 0 16

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 = 0.41 (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 285 699
subtype1 84 235
subtype2 127 301
subtype3 74 163

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 = 0.272 (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 825 2.3 (4.5)
subtype1 289 2.6 (5.2)
subtype2 329 2.2 (3.8)
subtype3 207 2.0 (4.6)

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 4 5 6
Number of samples 200 151 215 239 133 30
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.19 (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 947 112 0.0 - 234.3 (21.5)
subtype1 195 21 0.1 - 234.3 (23.1)
subtype2 151 17 0.0 - 170.2 (20.9)
subtype3 207 19 0.0 - 188.7 (21.3)
subtype4 235 33 0.2 - 211.6 (20.1)
subtype5 129 19 0.0 - 220.9 (22.3)
subtype6 30 3 1.8 - 189.0 (25.1)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0571 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 955 58.6 (13.2)
subtype1 197 59.1 (12.7)
subtype2 148 58.2 (13.1)
subtype3 212 60.8 (13.6)
subtype4 239 58.0 (13.0)
subtype5 129 57.0 (13.7)
subtype6 30 55.1 (11.3)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000403 (Chi-square test), Q value = 0.035

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 76 10 9 327 220 2 133 26 55 14 1 17
subtype1 23 20 1 1 68 37 0 27 7 8 4 0 4
subtype2 16 13 2 7 33 42 1 19 3 14 1 0 0
subtype3 13 19 5 0 71 51 0 27 7 13 2 0 7
subtype4 9 15 1 0 105 51 0 34 4 9 4 1 5
subtype5 12 7 0 1 42 29 1 24 3 11 2 0 1
subtype6 4 2 1 0 8 10 0 2 2 0 1 0 0

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 = 8e-05 (Chi-square test), Q value = 0.0075

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

nPatients T1 T2 T3 T4
ALL 254 562 113 36
subtype1 74 104 13 9
subtype2 51 72 25 3
subtype3 54 123 27 10
subtype4 39 166 25 7
subtype5 28 82 18 5
subtype6 8 15 5 2

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 = 0.161 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 457 324 106 65
subtype1 94 70 23 10
subtype2 61 64 11 15
subtype3 102 70 23 15
subtype4 129 67 28 11
subtype5 56 42 19 12
subtype6 15 11 2 2

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 = 0.233 (Chi-square test), Q value = 1

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

nPatients CM0 (I+) M0 M1 MX
ALL 2 833 14 119
subtype1 0 179 4 17
subtype2 1 119 1 30
subtype3 1 183 2 29
subtype4 0 211 4 24
subtype5 0 114 2 17
subtype6 0 27 1 2

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.358 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 958 10
subtype1 196 4
subtype2 151 0
subtype3 212 3
subtype4 236 3
subtype5 133 0
subtype6 30 0

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 5.1e-28 (Chi-square test), Q value = 5.9e-26

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 1 718 157 5 28 14 44
subtype1 0 165 17 0 7 0 11
subtype2 0 67 74 0 4 2 4
subtype3 0 145 42 0 9 7 12
subtype4 1 213 4 4 3 1 12
subtype5 0 104 14 1 5 4 5
subtype6 0 24 6 0 0 0 0

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 = 0.00127 (Chi-square test), Q value = 0.097

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

nPatients NO YES
ALL 278 690
subtype1 71 129
subtype2 26 125
subtype3 62 153
subtype4 69 170
subtype5 46 87
subtype6 4 26

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 = 0.147 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 815 2.3 (4.5)
subtype1 171 1.8 (3.2)
subtype2 145 3.0 (5.7)
subtype3 170 2.2 (4.0)
subtype4 198 2.0 (4.3)
subtype5 108 2.9 (5.5)
subtype6 23 2.6 (5.3)

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 520 260 188
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.209 (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 947 112 0.0 - 234.3 (21.5)
subtype1 501 57 0.1 - 234.3 (21.4)
subtype2 258 39 0.0 - 211.6 (23.4)
subtype3 188 16 0.0 - 170.2 (20.4)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00311 (ANOVA), Q value = 0.22

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

nPatients Mean (Std.Dev)
ALL 955 58.6 (13.2)
subtype1 512 59.7 (13.5)
subtype2 259 56.3 (12.7)
subtype3 184 59.1 (12.5)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 3.5e-09 (Chi-square test), Q value = 3.9e-07

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 76 10 9 327 220 2 133 26 55 14 1 17
subtype1 42 41 7 0 177 116 0 78 18 18 8 1 14
subtype2 17 18 1 1 111 53 0 32 3 15 5 0 3
subtype3 18 17 2 8 39 51 2 23 5 22 1 0 0

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 = 0.000547 (Chi-square test), Q value = 0.046

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

nPatients T1 T2 T3 T4
ALL 254 562 113 36
subtype1 141 299 53 25
subtype2 59 171 23 6
subtype3 54 92 37 5

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 = 0.000944 (Chi-square test), Q value = 0.075

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

nPatients N0 N1 N2 N3
ALL 457 324 106 65
subtype1 234 186 62 26
subtype2 143 69 29 16
subtype3 80 69 15 23

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 = 4.17e-05 (Chi-square test), Q value = 0.004

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

nPatients CM0 (I+) M0 M1 MX
ALL 2 833 14 119
subtype1 2 463 8 47
subtype2 0 227 5 28
subtype3 0 143 1 44

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0969 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 958 10
subtype1 511 9
subtype2 259 1
subtype3 188 0

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 2.49e-50 (Chi-square test), Q value = 3e-48

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 1 718 157 5 28 14 44
subtype1 0 407 50 1 23 12 27
subtype2 1 229 7 4 2 0 16
subtype3 0 82 100 0 3 2 1

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 = 0.0011 (Fisher's exact test), Q value = 0.086

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

nPatients NO YES
ALL 278 690
subtype1 162 358
subtype2 82 178
subtype3 34 154

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 = 0.0247 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 815 2.3 (4.5)
subtype1 423 2.0 (3.8)
subtype2 221 2.2 (4.9)
subtype3 171 3.1 (5.6)

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 249 202 196
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00587 (logrank test), Q value = 0.39

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

nPatients nDeath Duration Range (Median), Month
ALL 635 74 0.0 - 194.3 (20.1)
subtype1 243 33 0.1 - 189.0 (21.0)
subtype2 199 14 0.0 - 194.3 (18.1)
subtype3 193 27 0.0 - 130.2 (20.4)

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 = 0.0409 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 634 58.4 (13.0)
subtype1 245 57.6 (13.7)
subtype2 197 60.3 (12.3)
subtype3 192 57.4 (12.6)

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 = 2.24e-06 (Chi-square test), Q value = 0.00024

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 48 5 9 218 150 2 86 13 40 3 5
subtype1 34 23 1 2 80 59 0 34 6 6 2 2
subtype2 18 16 4 7 47 53 1 26 4 25 1 0
subtype3 15 9 0 0 91 38 1 26 3 9 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 = 4.01e-06 (Chi-square test), Q value = 0.00041

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

nPatients T1 T2 T3 T4
ALL 188 365 75 19
subtype1 95 127 18 9
subtype2 53 106 39 4
subtype3 40 132 18 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 = 1.62e-05 (Chi-square test), Q value = 0.0016

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

nPatients N0 N1 N2 N3
ALL 307 226 65 42
subtype1 114 98 26 7
subtype2 83 76 15 26
subtype3 110 52 24 9

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 = 0.00343 (Chi-square test), Q value = 0.24

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

nPatients CM0 (I+) M0 M1 MX
ALL 2 534 3 108
subtype1 2 217 2 28
subtype2 0 151 1 50
subtype3 0 166 0 30

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 = 0.507 (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 640 7
subtype1 245 4
subtype2 201 1
subtype3 194 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 = 1.99e-35 (Chi-square test), Q value = 2.4e-33

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 1 448 131 5 19 14 29
subtype1 0 197 20 0 13 5 14
subtype2 0 85 102 0 5 7 3
subtype3 1 166 9 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 = 0.000307 (Fisher's exact test), Q value = 0.027

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

nPatients NO YES
ALL 174 473
subtype1 81 168
subtype2 34 168
subtype3 59 137

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 = 0.00584 (ANOVA), Q value = 0.39

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

nPatients Mean (Std.Dev)
ALL 579 2.5 (4.9)
subtype1 228 1.9 (3.7)
subtype2 183 3.4 (6.1)
subtype3 168 2.3 (4.9)

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 149 344 154
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.00476 (logrank test), Q value = 0.33

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

nPatients nDeath Duration Range (Median), Month
ALL 635 74 0.0 - 194.3 (20.1)
subtype1 148 23 0.2 - 130.2 (21.6)
subtype2 334 42 0.0 - 189.0 (20.9)
subtype3 153 9 0.0 - 194.3 (17.1)

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 = 0.138 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 634 58.4 (13.0)
subtype1 148 58.2 (13.1)
subtype2 335 57.7 (13.2)
subtype3 151 60.2 (12.6)

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 = 3.11e-06 (Chi-square test), Q value = 0.00032

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 48 5 9 218 150 2 86 13 40 3 5
subtype1 14 6 1 0 71 31 0 15 3 6 0 1
subtype2 39 31 3 2 112 78 0 52 7 13 3 4
subtype3 14 11 1 7 35 41 2 19 3 21 0 0

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 = 0.00075 (Chi-square test), Q value = 0.061

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

nPatients T1 T2 T3 T4
ALL 188 365 75 19
subtype1 35 98 11 5
subtype2 114 187 32 11
subtype3 39 80 32 3

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 = 5.96e-05 (Chi-square test), Q value = 0.0057

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

nPatients N0 N1 N2 N3
ALL 307 226 65 42
subtype1 88 42 13 6
subtype2 154 130 40 14
subtype3 65 54 12 22

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 = 0.00523 (Chi-square test), Q value = 0.36

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

nPatients CM0 (I+) M0 M1 MX
ALL 2 534 3 108
subtype1 0 128 0 21
subtype2 2 293 3 46
subtype3 0 113 0 41

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 = 0.89 (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 640 7
subtype1 147 2
subtype2 340 4
subtype3 153 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 = 1.95e-40 (Chi-square test), Q value = 2.3e-38

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 1 448 131 5 19 14 29
subtype1 1 127 5 5 0 0 11
subtype2 0 265 36 0 16 11 16
subtype3 0 56 90 0 3 3 2

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 = 0.000183 (Fisher's exact test), Q value = 0.017

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

nPatients NO YES
ALL 174 473
subtype1 40 109
subtype2 111 233
subtype3 23 131

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 = 0.0368 (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 579 2.5 (4.9)
subtype1 135 2.0 (4.9)
subtype2 305 2.3 (4.2)
subtype3 139 3.4 (6.2)

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 = 988

  • 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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