Correlation between aggregated molecular cancer subtypes and selected clinical features
Breast Invasive Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1348HCJ
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1348HCJ
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 901 patients, 24 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' 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 'GENDER' and 'HISTOLOGICAL.TYPE'.

  • 6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

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

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'AGE',  'HISTOLOGICAL.TYPE', and 'NEOPLASM.DISEASESTAGE'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE' and 'NEOPLASM.DISEASESTAGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE' and 'NEOPLASM.DISEASESTAGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE' and 'NEOPLASM.DISEASESTAGE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE' and 'NEOPLASM.DISEASESTAGE'.

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, 24 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER HISTOLOGICAL
TYPE 		RADIATIONS
RADIATION
REGIMENINDICATION 		DISTANT
METASTASIS 		LYMPH
NODE
METASTASIS 		NUMBER
OF
LYMPH
NODES 		TUMOR
STAGECODE 		NEOPLASM
DISEASESTAGE
Statistical Tests logrank test ANOVA Fisher's exact test Chi-square test Fisher's exact test Chi-square test Chi-square test ANOVA ANOVA Chi-square test
mRNA CNMF subtypes 0.031
(1.00) 		2.59e-08
(2.59e-06) 		0.0459
(1.00) 		3.42e-06
(0.000335) 		0.803
(1.00) 		0.54
(1.00) 		0.106
(1.00) 		0.397
(1.00) 		0.0312
(1.00)
mRNA cHierClus subtypes 0.714
(1.00) 		0.000796
(0.0701) 		0.491
(1.00) 		8.12e-08
(8.04e-06) 		0.133
(1.00) 		0.143
(1.00) 		0.252
(1.00) 		0.646
(1.00) 		0.0727
(1.00)
Copy Number Ratio CNMF subtypes 0.256
(1.00) 		0.00751
(0.586) 		0.000118
(0.0112) 		5.64e-14
(5.76e-12) 		0.276
(1.00) 		0.0649
(1.00) 		0.343
(1.00) 		0.111
(1.00) 		0.236
(1.00)
METHLYATION CNMF 0.472
(1.00) 		0.00131
(0.113) 		0.191
(1.00) 		6.43e-09
(6.49e-07) 		0.196
(1.00) 		0.505
(1.00) 		0.311
(1.00) 		0.17
(1.00) 		0.101
(1.00)
RPPA CNMF subtypes 0.0477
(1.00) 		0.0287
(1.00) 		0.0564
(1.00) 		0.000153
(0.0142) 		0.981
(1.00) 		0.323
(1.00) 		0.0979
(1.00) 		0.0305
(1.00) 		0.0292
(1.00)
RPPA cHierClus subtypes 0.318
(1.00) 		0.00121
(0.105) 		0.154
(1.00) 		0.000648
(0.0583) 		0.917
(1.00) 		0.665
(1.00) 		0.194
(1.00) 		0.0553
(1.00) 		0.0496
(1.00)
RNAseq CNMF subtypes 0.00307
(0.258) 		0.000358
(0.0329) 		0.148
(1.00) 		5.3e-25
(5.56e-23) 		0.548
(1.00) 		0.405
(1.00) 		0.00714
(0.564) 		0.00365
(0.303) 		0.00065
(0.0583)
RNAseq cHierClus subtypes 0.0655
(1.00) 		0.000108
(0.0104) 		0.0719
(1.00) 		2.59e-26
(2.77e-24) 		0.474
(1.00) 		0.207
(1.00) 		0.0807
(1.00) 		0.564
(1.00) 		0.00555
(0.449)
MIRSEQ CNMF 0.115
(1.00) 		0.0749
(1.00) 		0.189
(1.00) 		5.6e-24
(5.82e-22) 		0.261
(1.00) 		0.46
(1.00) 		0.026
(1.00) 		0.024
(1.00) 		4.98e-05
(0.00483)
MIRSEQ CHIERARCHICAL 0.0759
(1.00) 		0.0835
(1.00) 		0.913
(1.00) 		4.9e-34
(5.29e-32) 		0.918
(1.00) 		0.00845
(0.651) 		0.00593
(0.474) 		0.185
(1.00) 		0.000505
(0.0459)
MIRseq Mature CNMF subtypes 0.0734
(1.00) 		0.78
(1.00) 		0.295
(1.00) 		6.99e-23
(7.2e-21) 		0.338
(1.00) 		0.271
(1.00) 		0.324
(1.00) 		0.0927
(1.00) 		0.00137
(0.116)
MIRseq Mature cHierClus subtypes 0.0971
(1.00) 		0.0401
(1.00) 		0.676
(1.00) 		3.92e-25
(4.15e-23) 		0.068
(1.00) 		0.074
(1.00) 		0.113
(1.00) 		0.00541
(0.444) 		0.000142
(0.0134)
Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1.  Get Full Table 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 = 0.031 (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 494 65 0.1 - 223.4 (24.2)
subtype1 19 3 0.3 - 92.0 (14.4)
subtype2 38 3 0.1 - 157.4 (40.6)
subtype3 111 17 0.1 - 177.4 (24.5)
subtype4 96 12 0.1 - 211.5 (22.3)
subtype5 107 10 0.3 - 223.4 (19.1)
subtype6 65 14 0.1 - 189.0 (24.6)
subtype7 19 2 0.2 - 97.5 (36.3)
subtype8 39 4 0.3 - 112.4 (20.0)

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 = 2.6e-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 'GENDER'

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

Table S4.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: '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 S3.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.42e-06 (Chi-square test), Q value = 0.00033

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

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 449 41 1 11 2 22
subtype1 20 0 0 0 0 1
subtype2 28 8 0 3 0 0
subtype3 113 1 0 0 1 6
subtype4 93 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 S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.803 (Chi-square test), Q value = 1

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

nPatients NO YES
ALL 148 378
subtype1 3 18
subtype2 11 28
subtype3 32 89
subtype4 34 67
subtype5 31 79
subtype6 21 53
subtype7 5 15
subtype8 11 29

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'DISTANT.METASTASIS'

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

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

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: 'DISTANT.METASTASIS'

'mRNA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.106 (Chi-square test), Q value = 1

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

nPatients N0 N0 (I+) N0 (I-) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3C NX
ALL 138 16 101 58 77 20 2 13 32 29 10 18 1 11
subtype1 3 0 3 3 3 0 0 0 4 3 0 1 0 1
subtype2 3 4 7 3 9 2 0 2 4 1 1 2 0 1
subtype3 25 1 23 17 22 5 0 6 7 7 1 3 0 4
subtype4 35 3 26 12 6 4 1 1 5 3 1 3 1 0
subtype5 37 7 22 5 18 5 1 2 4 3 2 3 0 1
subtype6 14 0 10 13 10 2 0 1 6 8 4 4 0 2
subtype7 6 0 5 1 3 0 0 1 1 2 0 0 0 1
subtype8 15 1 5 4 6 2 0 0 1 2 1 2 0 1

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

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

P value = 0.397 (ANOVA), Q value = 1

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: '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 S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0312 (Chi-square test), Q value = 1

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: '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 42 41 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 6 8 4 36 34 20 6 3 1 0 3
subtype4 7 11 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 3 2 0 6 4 4 0 0 0 0 1
subtype8 6 1 2 14 6 6 0 2 1 0 2

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S11.  Get Full Table 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 = 0.714 (logrank test), Q value = 1

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 494 65 0.1 - 223.4 (24.2)
subtype1 122 12 0.3 - 157.4 (25.6)
subtype2 120 18 0.1 - 211.5 (23.8)
subtype3 252 35 0.1 - 223.4 (23.1)

Figure S10.  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 = 0.07

Table S13.  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 S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

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

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.12e-08 (Chi-square test), Q value = 8e-06

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 449 41 1 11 2 22
subtype1 88 26 0 6 0 4
subtype2 120 4 1 0 0 6
subtype3 241 11 0 5 2 12

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.133 (Fisher's exact test), Q value = 1

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 148 378
subtype1 36 88
subtype2 45 86
subtype3 67 204

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.143 (Chi-square test), Q value = 1

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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 S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'mRNA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.252 (Chi-square test), Q value = 1

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3C NX
ALL 138 16 101 58 77 20 2 13 32 29 10 18 1 11
subtype1 31 7 24 11 21 4 0 1 7 7 3 5 0 3
subtype2 38 3 33 18 10 4 1 1 8 6 3 5 1 0
subtype3 69 6 44 29 46 12 1 11 17 16 4 8 0 8

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.646 (ANOVA), Q value = 1

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: '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 S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0727 (Chi-square test), Q value = 1

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: '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 42 41 6 185 110 77 15 19 14 1 16
subtype1 17 10 2 38 22 21 1 5 2 1 5
subtype2 8 12 0 56 25 13 2 8 4 0 3
subtype3 17 19 4 91 63 43 12 6 8 0 8

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

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

Table S21.  Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 331 207 65 231 51
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.256 (logrank test), Q value = 1

Table S22.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 826 94 0.0 - 223.4 (18.0)
subtype1 310 30 0.0 - 223.4 (20.0)
subtype2 191 19 0.1 - 162.0 (16.3)
subtype3 64 6 0.0 - 189.0 (16.5)
subtype4 213 29 0.0 - 211.5 (17.7)
subtype5 48 10 0.7 - 220.9 (18.0)

Figure S19.  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 = 0.00751 (ANOVA), Q value = 0.59

Table S23.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 884 58.6 (13.2)
subtype1 330 58.5 (13.3)
subtype2 207 58.6 (14.3)
subtype3 65 61.5 (11.4)
subtype4 231 56.7 (12.6)
subtype5 51 63.2 (11.6)

Figure S20.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.000118 (Chi-square test), Q value = 0.011

Table S24.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 876 9
subtype1 331 0
subtype2 199 8
subtype3 64 1
subtype4 231 0
subtype5 51 0

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 5.64e-14 (Chi-square test), Q value = 5.8e-12

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 682 121 4 26 11 41
subtype1 213 76 0 13 8 21
subtype2 177 21 0 2 1 6
subtype3 42 17 0 5 0 1
subtype4 209 3 3 5 0 11
subtype5 41 4 1 1 2 2

Figure S22.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.276 (Chi-square test), Q value = 1

Table S26.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 212 673
subtype1 70 261
subtype2 45 162
subtype3 19 46
subtype4 64 167
subtype5 14 37

Figure S23.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0649 (Chi-square test), Q value = 1

Table S27.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 781 15 87
subtype1 1 283 4 43
subtype2 0 188 4 15
subtype3 0 61 2 2
subtype4 0 206 5 20
subtype5 1 43 0 7

Figure S24.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.343 (Chi-square test), Q value = 1

Table S28.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B N3C NX
ALL 262 22 132 1 105 132 32 2 23 51 55 19 32 2 1 14
subtype1 97 10 60 1 36 47 11 2 11 14 16 7 12 1 0 6
subtype2 52 3 24 0 30 31 6 0 8 16 17 7 8 0 0 5
subtype3 17 1 9 0 5 16 3 0 0 2 4 4 3 0 0 1
subtype4 85 6 34 0 27 26 8 0 3 17 14 1 7 1 1 1
subtype5 11 2 5 0 7 12 4 0 1 2 4 0 2 0 0 1

Figure S25.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.111 (ANOVA), Q value = 1

Table S29.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 741 2.2 (4.4)
subtype1 294 1.9 (3.9)
subtype2 152 2.6 (4.2)
subtype3 51 3.5 (6.2)
subtype4 200 2.0 (4.6)
subtype5 44 2.4 (4.0)

Figure S26.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

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

P value = 0.236 (Chi-square test), Q value = 1

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: '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 72 67 6 8 300 199 2 129 26 43 15 1 16
subtype1 38 37 3 5 98 70 1 43 8 17 4 0 7
subtype2 11 11 2 1 71 45 1 37 12 11 4 0 1
subtype3 3 1 0 1 21 20 0 8 3 4 2 0 2
subtype4 16 15 1 1 89 54 0 32 3 9 5 1 4
subtype5 4 3 0 0 21 10 0 9 0 2 0 0 2

Figure S27.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S31.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6
Number of samples 97 144 94 120 36 86
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.472 (logrank test), Q value = 1

Table S32.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 548 61 0.0 - 223.4 (17.7)
subtype1 92 13 0.2 - 211.5 (20.1)
subtype2 136 13 0.3 - 223.4 (13.7)
subtype3 92 13 0.0 - 173.0 (14.0)
subtype4 113 12 0.1 - 194.3 (19.6)
subtype5 34 2 0.1 - 157.4 (22.8)
subtype6 81 8 0.0 - 130.2 (20.9)

Figure S28.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00131 (ANOVA), Q value = 0.11

Table S33.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 576 57.9 (13.1)
subtype1 97 56.2 (12.9)
subtype2 144 59.2 (12.5)
subtype3 94 62.3 (12.0)
subtype4 120 55.3 (14.4)
subtype5 36 55.5 (13.7)
subtype6 85 57.3 (11.9)

Figure S29.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.191 (Chi-square test), Q value = 1

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 571 6
subtype1 97 0
subtype2 143 1
subtype3 91 3
subtype4 119 1
subtype5 35 1
subtype6 86 0

Figure S30.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 6.43e-09 (Chi-square test), Q value = 6.5e-07

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 420 95 4 22 8 28
subtype1 81 2 3 1 0 10
subtype2 83 35 0 12 5 9
subtype3 66 23 0 3 0 2
subtype4 103 6 0 3 2 6
subtype5 29 6 0 1 0 0
subtype6 58 23 1 2 1 1

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.196 (Chi-square test), Q value = 1

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 147 430
subtype1 31 66
subtype2 35 109
subtype3 20 74
subtype4 37 83
subtype5 6 30
subtype6 18 68

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.505 (Chi-square test), Q value = 1

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 1 493 6 77
subtype1 0 86 2 9
subtype2 1 122 0 21
subtype3 0 76 2 16
subtype4 0 108 1 11
subtype5 0 30 1 5
subtype6 0 71 0 15

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.311 (Chi-square test), Q value = 1

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B NX
ALL 158 14 82 1 74 87 23 2 15 38 43 13 18 2 7
subtype1 39 2 16 0 10 11 3 0 1 5 6 1 2 1 0
subtype2 35 3 19 1 13 33 8 1 6 8 7 2 4 0 4
subtype3 27 2 13 0 10 11 4 0 0 9 9 3 4 0 2
subtype4 20 2 16 0 24 20 6 0 4 8 12 2 5 0 1
subtype5 11 1 4 0 3 5 1 0 0 4 3 2 2 0 0
subtype6 26 4 14 0 14 7 1 1 4 4 6 3 1 1 0

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.17 (ANOVA), Q value = 1

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 536 2.5 (4.6)
subtype1 92 1.8 (3.3)
subtype2 136 2.2 (4.0)
subtype3 88 3.2 (6.1)
subtype4 106 2.8 (4.6)
subtype5 32 3.4 (5.0)
subtype6 82 2.0 (4.7)

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.101 (Chi-square test), Q value = 1

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: '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 47 45 2 8 185 131 2 99 16 30 6 5
subtype1 5 6 0 1 42 23 0 12 1 3 2 1
subtype2 14 14 0 2 41 40 0 21 4 6 0 2
subtype3 6 5 1 2 28 19 1 20 1 7 2 2
subtype4 5 7 1 0 34 33 0 26 7 6 1 0
subtype5 3 5 0 1 9 3 0 8 2 4 1 0
subtype6 14 8 0 2 31 13 1 12 1 4 0 0

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S41.  Get Full Table 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 = 0.0477 (logrank test), Q value = 1

Table S42.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 380 44 0.1 - 189.0 (24.5)
subtype1 135 22 0.1 - 186.4 (22.8)
subtype2 132 11 0.2 - 146.5 (18.0)
subtype3 113 11 0.3 - 189.0 (28.5)

Figure S37.  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 S43.  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 S38.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

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

Table S44.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S39.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

Table S45.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 353 30 1 7 2 15
subtype1 139 4 1 1 0 7
subtype2 120 5 0 3 2 6
subtype3 94 21 0 3 0 2

Figure S40.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.981 (Fisher's exact test), Q value = 1

Table S46.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 125 283
subtype1 47 105
subtype2 42 94
subtype3 36 84

Figure S41.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.323 (Chi-square test), Q value = 1

Table S47.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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 S42.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RPPA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0979 (Chi-square test), Q value = 1

Table S48.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3C NX
ALL 100 13 79 48 63 8 1 12 27 25 9 15 1 7
subtype1 41 2 27 24 14 3 0 4 11 11 4 7 1 3
subtype2 31 6 28 10 35 4 1 3 7 3 2 4 0 2
subtype3 28 5 24 14 14 1 0 5 9 11 3 4 0 2

Figure S43.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0305 (ANOVA), Q value = 1

Table S49.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: '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 S44.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0292 (Chi-square test), Q value = 1

Table S50.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 30 31 4 137 94 62 13 15 14 8
subtype1 6 8 0 55 36 24 2 10 8 3
subtype2 11 13 1 46 38 14 6 1 3 3
subtype3 13 10 3 36 20 24 5 4 3 2

Figure S45.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S51.  Get Full Table 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 = 0.318 (logrank test), Q value = 1

Table S52.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 380 44 0.1 - 189.0 (24.5)
subtype1 152 17 0.2 - 129.7 (24.2)
subtype2 112 10 0.2 - 173.0 (25.5)
subtype3 116 17 0.1 - 189.0 (21.9)

Figure S46.  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 S53.  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 S47.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

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

Table S54.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S48.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000648 (Chi-square test), Q value = 0.058

Table S55.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 353 30 1 7 2 15
subtype1 143 8 0 4 2 6
subtype2 91 19 0 3 0 2
subtype3 119 3 1 0 0 7

Figure S49.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.917 (Fisher's exact test), Q value = 1

Table S56.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 125 283
subtype1 48 115
subtype2 36 79
subtype3 41 89

Figure S50.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.665 (Chi-square test), Q value = 1

Table S57.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

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 S51.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RPPA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.194 (Chi-square test), Q value = 1

Table S58.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3C NX
ALL 100 13 79 48 63 8 1 12 27 25 9 15 1 7
subtype1 42 5 31 14 33 3 1 4 13 6 2 5 0 4
subtype2 22 6 23 11 20 1 0 5 7 11 3 4 0 2
subtype3 36 2 25 23 10 4 0 3 7 8 4 6 1 1

Figure S52.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0553 (ANOVA), Q value = 1

Table S59.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: '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 S53.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0496 (Chi-square test), Q value = 1

Table S60.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 30 31 4 137 94 62 13 15 14 8
subtype1 14 12 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 S54.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S61.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 153 76 221 143 220 48 16
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00307 (logrank test), Q value = 0.26

Table S62.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 819 96 0.0 - 223.4 (18.2)
subtype1 147 19 0.1 - 211.5 (20.0)
subtype2 72 8 0.1 - 157.4 (31.7)
subtype3 212 16 0.2 - 223.4 (13.5)
subtype4 126 26 0.0 - 189.0 (20.0)
subtype5 202 24 0.1 - 177.4 (18.3)
subtype6 45 1 0.3 - 194.3 (21.3)
subtype7 15 2 0.2 - 130.2 (25.7)

Figure S55.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.000358 (ANOVA), Q value = 0.033

Table S63.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 876 58.4 (13.2)
subtype1 153 55.6 (12.2)
subtype2 75 55.0 (12.6)
subtype3 221 60.9 (12.4)
subtype4 143 59.3 (13.3)
subtype5 220 57.7 (14.1)
subtype6 48 60.2 (12.7)
subtype7 16 63.2 (14.9)

Figure S56.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.148 (Chi-square test), Q value = 1

Table S64.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 868 9
subtype1 153 0
subtype2 76 0
subtype3 219 2
subtype4 142 1
subtype5 214 6
subtype6 48 0
subtype7 16 0

Figure S57.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 5.3e-25 (Chi-square test), Q value = 5.6e-23

Table S65.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 682 116 4 25 10 40
subtype1 136 1 3 0 2 11
subtype2 37 33 0 4 1 1
subtype3 151 42 0 11 3 14
subtype4 131 6 0 2 0 4
subtype5 189 13 0 5 4 9
subtype6 25 19 0 3 0 1
subtype7 13 2 1 0 0 0

Figure S58.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.548 (Chi-square test), Q value = 1

Table S66.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 213 664
subtype1 46 107
subtype2 17 59
subtype3 54 167
subtype4 29 114
subtype5 52 168
subtype6 10 38
subtype7 5 11

Figure S59.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.405 (Chi-square test), Q value = 1

Table S67.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 778 15 82
subtype1 0 140 3 10
subtype2 1 63 1 11
subtype3 1 192 3 25
subtype4 0 129 5 9
subtype5 0 200 2 18
subtype6 0 41 1 6
subtype7 0 13 0 3

Figure S60.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00714 (Chi-square test), Q value = 0.56

Table S68.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B N3C NX
ALL 256 22 135 1 104 130 32 2 24 50 54 19 31 1 1 15
subtype1 63 4 31 0 16 13 5 1 1 6 6 1 4 1 1 0
subtype2 13 5 11 0 10 15 5 0 5 2 3 3 4 0 0 0
subtype3 68 8 35 1 17 42 6 1 6 9 12 5 7 0 0 4
subtype4 34 1 15 0 23 19 6 0 1 14 12 5 8 0 0 5
subtype5 48 3 35 0 32 34 9 0 9 18 16 4 6 0 0 6
subtype6 22 1 5 0 6 6 1 0 0 1 4 1 1 0 0 0
subtype7 8 0 3 0 0 1 0 0 2 0 1 0 1 0 0 0

Figure S61.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.00365 (ANOVA), Q value = 0.3

Table S69.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 734 2.2 (4.4)
subtype1 139 1.4 (3.1)
subtype2 73 2.9 (6.0)
subtype3 185 2.0 (4.0)
subtype4 99 3.7 (6.3)
subtype5 178 2.0 (3.0)
subtype6 45 2.0 (4.6)
subtype7 15 2.3 (5.9)

Figure S62.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00065 (Chi-square test), Q value = 0.058

Table S70.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: '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 72 69 7 8 296 198 2 125 25 41 15 1 17
subtype1 11 12 0 1 71 30 0 14 2 6 3 0 2
subtype2 10 4 0 2 17 23 1 11 0 6 1 1 0
subtype3 22 28 0 2 76 42 0 27 6 8 3 0 7
subtype4 8 5 0 1 45 33 0 26 6 10 5 0 4
subtype5 11 15 5 0 68 56 1 39 11 8 2 0 4
subtype6 6 4 2 2 13 11 0 7 0 2 1 0 0
subtype7 4 1 0 0 6 3 0 1 0 1 0 0 0

Figure S63.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S71.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 218 202 457
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0655 (logrank test), Q value = 1

Table S72.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 819 96 0.0 - 223.4 (18.2)
subtype1 203 31 0.0 - 211.5 (19.3)
subtype2 196 14 0.0 - 194.3 (24.8)
subtype3 420 51 0.0 - 223.4 (16.7)

Figure S64.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000108 (ANOVA), Q value = 0.01

Table S73.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 876 58.4 (13.2)
subtype1 218 56.7 (12.7)
subtype2 201 56.1 (12.8)
subtype3 457 60.2 (13.3)

Figure S65.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0719 (Fisher's exact test), Q value = 1

Table S74.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 868 9
subtype1 218 0
subtype2 201 1
subtype3 449 8

Figure S66.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.59e-26 (Chi-square test), Q value = 2.8e-24

Table S75.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 682 116 4 25 10 40
subtype1 196 5 3 1 0 13
subtype2 114 73 1 8 1 5
subtype3 372 38 0 16 9 22

Figure S67.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S76.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 213 664
subtype1 59 159
subtype2 50 152
subtype3 104 353

Figure S68.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.207 (Chi-square test), Q value = 1

Table S77.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 778 15 82
subtype1 0 202 3 13
subtype2 1 171 3 27
subtype3 1 405 9 42

Figure S69.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0807 (Chi-square test), Q value = 1

Table S78.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B N3C NX
ALL 256 22 135 1 104 130 32 2 24 50 54 19 31 1 1 15
subtype1 73 5 41 0 27 21 9 1 2 10 11 5 8 1 1 3
subtype2 55 9 34 0 22 28 6 0 11 6 13 5 10 0 0 3
subtype3 128 8 60 1 55 81 17 1 11 34 30 9 13 0 0 9

Figure S70.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.564 (ANOVA), Q value = 1

Table S79.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 734 2.2 (4.4)
subtype1 186 2.2 (5.0)
subtype2 185 2.5 (5.2)
subtype3 363 2.1 (3.5)

Figure S71.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00555 (Chi-square test), Q value = 0.45

Table S80.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: '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 72 69 7 8 296 198 2 125 25 41 15 1 17
subtype1 14 17 0 1 93 45 1 22 5 12 3 0 4
subtype2 26 16 3 5 52 47 0 30 1 13 3 1 5
subtype3 32 36 4 2 151 106 1 73 19 16 9 0 8

Figure S72.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S81.  Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 218 399 251
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.115 (logrank test), Q value = 1

Table S82.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 809 95 0.0 - 223.4 (18.2)
subtype1 210 23 0.0 - 194.3 (19.5)
subtype2 371 37 0.0 - 223.4 (17.9)
subtype3 228 35 0.0 - 211.5 (19.1)

Figure S73.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0749 (ANOVA), Q value = 1

Table S83.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 867 58.5 (13.2)
subtype1 217 57.8 (12.9)
subtype2 399 59.6 (13.4)
subtype3 251 57.3 (13.1)

Figure S74.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.189 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 859 9
subtype1 218 0
subtype2 393 6
subtype3 248 3

Figure S75.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 5.6e-24 (Chi-square test), Q value = 5.8e-22

Table S85.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 677 111 4 26 10 40
subtype1 127 73 0 6 3 9
subtype2 322 33 0 16 5 23
subtype3 228 5 4 4 2 8

Figure S76.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.261 (Fisher's exact test), Q value = 1

Table S86.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 210 658
subtype1 44 174
subtype2 100 299
subtype3 66 185

Figure S77.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.46 (Chi-square test), Q value = 1

Table S87.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 771 14 81
subtype1 1 190 1 26
subtype2 1 356 8 34
subtype3 0 225 5 21

Figure S78.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.026 (Chi-square test), Q value = 1

Table S88.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B N3C NX
ALL 255 22 131 1 103 129 32 2 24 50 53 18 30 2 1 15
subtype1 61 11 27 1 27 30 8 1 10 6 15 8 11 1 0 1
subtype2 110 7 63 0 47 73 13 1 11 26 20 7 11 0 0 10
subtype3 84 4 41 0 29 26 11 0 3 18 18 3 8 1 1 4

Figure S79.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.024 (ANOVA), Q value = 1

Table S89.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 728 2.2 (4.4)
subtype1 203 2.9 (5.8)
subtype2 313 1.8 (3.2)
subtype3 212 2.2 (4.2)

Figure S80.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 4.98e-05 (Chi-square test), Q value = 0.0048

Table S90.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: '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 72 67 7 8 294 195 2 124 25 41 14 1 17
subtype1 30 17 0 6 60 50 1 28 5 19 1 0 1
subtype2 32 36 6 1 131 93 1 55 14 11 8 0 11
subtype3 10 14 1 1 103 52 0 41 6 11 5 1 5

Figure S81.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S91.  Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 369 269 230
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0759 (logrank test), Q value = 1

Table S92.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 809 95 0.0 - 223.4 (18.2)
subtype1 332 37 0.1 - 223.4 (17.0)
subtype2 260 20 0.0 - 162.0 (18.1)
subtype3 217 38 0.0 - 211.5 (20.8)

Figure S82.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0835 (ANOVA), Q value = 1

Table S93.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 867 58.5 (13.2)
subtype1 369 59.5 (14.0)
subtype2 268 58.2 (12.6)
subtype3 230 57.1 (12.7)

Figure S83.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.913 (Fisher's exact test), Q value = 1

Table S94.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 859 9
subtype1 365 4
subtype2 267 2
subtype3 227 3

Figure S84.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 4.9e-34 (Chi-square test), Q value = 5.3e-32

Table S95.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 677 111 4 26 10 40
subtype1 303 17 0 18 8 23
subtype2 163 91 0 6 2 7
subtype3 211 3 4 2 0 10

Figure S85.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.918 (Fisher's exact test), Q value = 1

Table S96.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 210 658
subtype1 88 281
subtype2 64 205
subtype3 58 172

Figure S86.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.00845 (Chi-square test), Q value = 0.65

Table S97.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 771 14 81
subtype1 1 336 5 27
subtype2 1 227 2 39
subtype3 0 208 7 15

Figure S87.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00593 (Chi-square test), Q value = 0.47

Table S98.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B N3C NX
ALL 255 22 131 1 103 129 32 2 24 50 53 18 30 2 1 15
subtype1 103 5 50 0 48 62 15 1 11 28 20 7 8 0 0 11
subtype2 72 12 37 1 30 46 7 0 12 10 19 8 13 1 0 1
subtype3 80 5 44 0 25 21 10 1 1 12 14 3 9 1 1 3

Figure S88.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.185 (ANOVA), Q value = 1

Table S99.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 728 2.2 (4.4)
subtype1 283 2.0 (3.2)
subtype2 248 2.6 (5.3)
subtype3 197 2.0 (4.4)

Figure S89.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000505 (Chi-square test), Q value = 0.046

Table S100.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: '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 72 67 7 8 294 195 2 124 25 41 14 1 17
subtype1 26 29 6 0 123 83 0 61 13 11 5 1 11
subtype2 31 23 0 6 74 66 2 35 9 19 2 0 2
subtype3 15 15 1 2 97 46 0 28 3 11 7 0 4

Figure S90.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S101.  Get Full Table Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 214 164 168
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0734 (logrank test), Q value = 1

Table S102.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 496 58 0.0 - 194.3 (16.7)
subtype1 192 23 0.1 - 189.0 (14.5)
subtype2 159 15 0.0 - 194.3 (18.1)
subtype3 145 20 0.0 - 130.2 (16.9)

Figure S91.  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 = 0.78 (ANOVA), Q value = 1

Table S103.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 545 58.1 (13.1)
subtype1 214 57.8 (13.3)
subtype2 163 58.7 (13.4)
subtype3 168 57.9 (12.8)

Figure S92.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.295 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 540 6
subtype1 210 4
subtype2 164 0
subtype3 166 2

Figure S93.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.99e-23 (Chi-square test), Q value = 7.2e-21

Table S105.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 405 85 4 17 10 25
subtype1 171 14 0 10 5 14
subtype2 86 67 0 4 3 4
subtype3 148 4 4 3 2 7

Figure S94.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.338 (Fisher's exact test), Q value = 1

Table S106.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 111 435
subtype1 47 167
subtype2 27 137
subtype3 37 131

Figure S95.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.271 (Chi-square test), Q value = 1

Table S107.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 471 3 70
subtype1 1 189 2 22
subtype2 1 133 1 29
subtype3 0 149 0 19

Figure S96.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.324 (Chi-square test), Q value = 1

Table S108.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B NX
ALL 197 8 52 1 75 77 18 2 20 29 33 10 16 2 6
subtype1 75 1 20 1 31 36 8 1 8 13 10 2 4 0 4
subtype2 51 4 17 0 19 27 6 0 8 5 11 6 8 1 1
subtype3 71 3 15 0 25 14 4 1 4 11 12 2 4 1 1

Figure S97.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0927 (ANOVA), Q value = 1

Table S109.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 491 2.3 (4.8)
subtype1 193 1.9 (3.5)
subtype2 154 3.0 (5.8)
subtype3 144 2.3 (4.9)

Figure S98.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

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

P value = 0.00137 (Chi-square test), Q value = 0.12

Table S110.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: '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 64 37 2 8 184 125 2 77 12 26 3 5
subtype1 28 19 2 1 69 49 0 31 6 5 2 2
subtype2 21 13 0 6 40 43 2 21 3 14 1 0
subtype3 15 5 0 1 75 33 0 25 3 7 0 3

Figure S99.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S111.  Get Full Table Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 106 123 132 185
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0971 (logrank test), Q value = 1

Table S112.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 496 58 0.0 - 194.3 (16.7)
subtype1 103 7 0.0 - 194.3 (9.4)
subtype2 102 11 0.0 - 144.6 (10.1)
subtype3 120 19 0.1 - 130.2 (20.6)
subtype4 171 21 0.1 - 189.0 (21.6)

Figure S100.  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 = 0.0401 (ANOVA), Q value = 1

Table S113.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 545 58.1 (13.1)
subtype1 106 60.4 (13.5)
subtype2 123 59.4 (14.0)
subtype3 132 57.5 (13.1)
subtype4 184 56.3 (12.1)

Figure S101.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.676 (Fisher's exact test), Q value = 1

Table S114.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 540 6
subtype1 106 0
subtype2 121 2
subtype3 131 1
subtype4 182 3

Figure S102.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.92e-25 (Chi-square test), Q value = 4.2e-23

Table S115.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients INFILTRATING DUCTAL CARCINOMA INFILTRATING LOBULAR CARCINOMA MEDULLARY CARCINOMA MIXED HISTOLOGY (PLEASE SPECIFY) MUCINOUS CARCINOMA OTHER SPECIFY
ALL 405 85 4 17 10 25
subtype1 50 51 0 1 2 2
subtype2 96 4 0 7 7 9
subtype3 118 2 4 1 0 7
subtype4 141 28 0 8 1 7

Figure S103.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.068 (Fisher's exact test), Q value = 1

Table S116.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 111 435
subtype1 13 93
subtype2 30 93
subtype3 32 100
subtype4 36 149

Figure S104.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.074 (Chi-square test), Q value = 1

Table S117.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 471 3 70
subtype1 0 85 0 21
subtype2 0 110 0 13
subtype3 0 118 0 14
subtype4 2 158 3 22

Figure S105.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.113 (Chi-square test), Q value = 1

Table S118.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B NX
ALL 197 8 52 1 75 77 18 2 20 29 33 10 16 2 6
subtype1 29 3 13 0 13 16 3 0 4 4 8 6 7 0 0
subtype2 38 1 13 0 21 21 3 0 3 9 7 1 2 0 4
subtype3 60 2 11 0 17 10 5 1 3 8 10 1 3 1 0
subtype4 70 2 15 1 24 30 7 1 10 8 8 2 4 1 2

Figure S106.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00541 (ANOVA), Q value = 0.44

Table S119.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 491 2.3 (4.8)
subtype1 96 3.9 (7.4)
subtype2 103 1.9 (2.9)
subtype3 121 1.8 (3.4)
subtype4 171 2.2 (4.5)

Figure S107.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

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

P value = 0.000142 (Chi-square test), Q value = 0.013

Table S120.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: '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 64 37 2 8 184 125 2 77 12 26 3 5
subtype1 13 3 0 5 30 25 2 13 3 12 0 0
subtype2 9 11 0 0 40 34 0 21 4 3 0 1
subtype3 12 3 0 2 59 28 0 18 2 5 0 2
subtype4 30 20 2 1 55 38 0 25 3 6 3 2

Figure S108.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Methods & Data
Input

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

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

  • Number of patients = 901

  • 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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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