Breast Invasive Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
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 10 different clustering approaches and 5 clinical features across 866 patients, 16 significant findings detected with P value < 0.05.

  • CNMF clustering analysis on array-based mRNA expression data identified 8 subtypes that correlate to 'Time to Death' and 'AGE'.

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

  • 5 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death' and 'GENDER'.

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.

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

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.

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

  • CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 5 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 16 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER RADIATIONS
RADIATION
REGIMENINDICATION 		NEOADJUVANT
THERAPY
Statistical Tests logrank test ANOVA Fisher's exact test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.00106 2.3e-06 0.0519 0.897 0.391
mRNA cHierClus subtypes 0.557 0.00176 0.322 0.195 0.335
CN CNMF 0.0167 0.101 0.000906 0.355 0.737
METHLYATION CNMF 0.0348 0.000404 0.0531 0.23 0.7
RPPA CNMF subtypes 0.0124 0.0191 0.058 0.944 0.0593
RPPA cHierClus subtypes 0.0901 0.000121 0.187 0.726 0.307
RNAseq CNMF subtypes 0.179 0.00573 0.0991 0.467 0.184
RNAseq cHierClus subtypes 0.00841 0.000672 0.0886 0.255 0.0533
MIRseq CNMF subtypes 0.00145 0.0286 0.683 0.424 0.587
MIRseq cHierClus subtypes 0.0666 0.00927 0.359 0.687 0.919
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 126 41 21 103 107 73 20 38
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.00106 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 497 65 0.1 - 223.4 (24.1)
subtype1 116 17 0.1 - 177.4 (24.4)
subtype2 40 4 0.1 - 157.4 (40.6)
subtype3 19 3 0.3 - 223.4 (14.0)
subtype4 98 12 0.1 - 211.5 (21.9)
subtype5 104 10 0.3 - 220.9 (19.0)
subtype6 64 14 0.1 - 189.0 (24.9)
subtype7 19 2 0.2 - 97.5 (36.3)
subtype8 37 3 0.3 - 82.7 (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 = 2.3e-06 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 529 57.9 (13.2)
subtype1 126 58.5 (14.3)
subtype2 41 50.0 (12.1)
subtype3 21 59.4 (13.8)
subtype4 103 53.8 (12.6)
subtype5 107 62.1 (12.4)
subtype6 73 58.5 (12.5)
subtype7 20 60.4 (9.9)
subtype8 38 60.3 (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 = 0.0519 (Chi-square test)

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

nPatients FEMALE MALE
ALL 523 6
subtype1 122 4
subtype2 41 0
subtype3 20 1
subtype4 103 0
subtype5 107 0
subtype6 73 0
subtype7 19 1
subtype8 38 0

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

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

P value = 0.897 (Chi-square test)

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

nPatients NO YES
ALL 147 382
subtype1 33 93
subtype2 11 30
subtype3 4 17
subtype4 34 69
subtype5 29 78
subtype6 19 54
subtype7 5 15
subtype8 12 26

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.391 (Chi-square test)

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

nPatients NO YES
ALL 221 308
subtype1 53 73
subtype2 20 21
subtype3 5 16
subtype4 48 55
subtype5 45 62
subtype6 26 47
subtype7 6 14
subtype8 18 20

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S7.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 129 129 271
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.557 (logrank test)

Table S8.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 497 65 0.1 - 223.4 (24.1)
subtype1 118 15 0.1 - 211.5 (21.6)
subtype2 125 14 0.3 - 157.4 (27.2)
subtype3 254 36 0.1 - 223.4 (23.1)

Figure S6.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.00176 (ANOVA)

Table S9.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 529 57.9 (13.2)
subtype1 129 55.1 (12.6)
subtype2 129 56.7 (13.1)
subtype3 271 59.8 (13.4)

Figure S7.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.322 (Fisher's exact test)

Table S10.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 523 6
subtype1 129 0
subtype2 128 1
subtype3 266 5

Figure S8.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.195 (Fisher's exact test)

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 147 382
subtype1 43 86
subtype2 37 92
subtype3 67 204

Figure S9.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.335 (Fisher's exact test)

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 221 308
subtype1 59 70
subtype2 57 72
subtype3 105 166

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'CN CNMF'

Table S13.  Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 324 231 61 186 41
'CN CNMF' versus 'Time to Death'

P value = 0.0167 (logrank test)

Table S14.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 785 94 0.0 - 223.4 (18.4)
subtype1 302 31 0.0 - 223.4 (20.3)
subtype2 212 25 0.1 - 162.0 (16.8)
subtype3 59 5 0.0 - 189.0 (18.9)
subtype4 173 25 0.0 - 211.5 (18.0)
subtype5 39 8 0.7 - 220.9 (21.5)

Figure S11.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.101 (ANOVA)

Table S15.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 842 58.5 (13.2)
subtype1 323 58.3 (13.0)
subtype2 231 58.5 (14.2)
subtype3 61 61.6 (11.5)
subtype4 186 57.2 (13.0)
subtype5 41 61.8 (12.4)

Figure S12.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.000906 (Chi-square test)

Table S16.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 834 9
subtype1 324 0
subtype2 223 8
subtype3 60 1
subtype4 186 0
subtype5 41 0

Figure S13.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.355 (Chi-square test)

Table S17.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 197 646
subtype1 67 257
subtype2 52 179
subtype3 14 47
subtype4 52 134
subtype5 12 29

Figure S14.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.737 (Chi-square test)

Table S18.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 301 542
subtype1 119 205
subtype2 75 156
subtype3 21 40
subtype4 69 117
subtype5 17 24

Figure S15.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S19.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6
Number of samples 92 134 88 123 33 83
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0348 (logrank test)

Table S20.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 524 62 0.0 - 223.4 (18.0)
subtype1 87 14 0.2 - 211.5 (20.0)
subtype2 126 10 0.2 - 223.4 (13.6)
subtype3 86 13 0.0 - 173.0 (16.1)
subtype4 116 13 0.1 - 194.3 (20.0)
subtype5 32 4 0.1 - 157.4 (16.1)
subtype6 77 8 0.0 - 130.2 (25.8)

Figure S16.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000404 (ANOVA)

Table S21.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 552 57.8 (13.1)
subtype1 92 56.4 (13.1)
subtype2 134 58.6 (12.5)
subtype3 88 63.2 (11.6)
subtype4 123 55.6 (14.4)
subtype5 33 54.9 (13.6)
subtype6 82 56.6 (11.8)

Figure S17.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0531 (Chi-square test)

Table S22.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 547 6
subtype1 92 0
subtype2 133 1
subtype3 86 2
subtype4 122 1
subtype5 31 2
subtype6 83 0

Figure S18.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.23 (Chi-square test)

Table S23.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 136 417
subtype1 28 64
subtype2 31 103
subtype3 19 69
subtype4 36 87
subtype5 4 29
subtype6 18 65

Figure S19.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.7 (Chi-square test)

Table S24.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 204 349
subtype1 33 59
subtype2 52 82
subtype3 33 55
subtype4 50 73
subtype5 9 24
subtype6 27 56

Figure S20.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S25.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 151 134 123
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0124 (logrank test)

Table S26.  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 (24.3)
subtype2 130 11 0.2 - 146.5 (17.4)
subtype3 115 11 0.3 - 189.0 (28.5)

Figure S21.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0191 (ANOVA)

Table S27.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 408 57.9 (13.1)
subtype1 151 56.3 (13.1)
subtype2 134 60.4 (13.8)
subtype3 123 57.0 (11.9)

Figure S22.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.058 (Fisher's exact test)

Table S28.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 403 5
subtype1 151 0
subtype2 130 4
subtype3 122 1

Figure S23.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.944 (Fisher's exact test)

Table S29.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 124 284
subtype1 46 105
subtype2 42 92
subtype3 36 87

Figure S24.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0593 (Fisher's exact test)

Table S30.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 188 220
subtype1 64 87
subtype2 73 61
subtype3 51 72

Figure S25.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S31.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 135 162 111
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0901 (logrank test)

Table S32.  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 120 18 0.1 - 189.0 (23.5)
subtype2 151 17 0.2 - 129.7 (19.9)
subtype3 109 9 0.2 - 173.0 (25.3)

Figure S26.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.000121 (ANOVA)

Table S33.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 408 57.9 (13.1)
subtype1 135 54.3 (13.0)
subtype2 162 60.7 (13.4)
subtype3 111 58.0 (11.7)

Figure S27.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.187 (Fisher's exact test)

Table S34.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 403 5
subtype1 135 0
subtype2 158 4
subtype3 110 1

Figure S28.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.726 (Fisher's exact test)

Table S35.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 124 284
subtype1 40 95
subtype2 47 115
subtype3 37 74

Figure S29.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.307 (Fisher's exact test)

Table S36.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 188 220
subtype1 55 80
subtype2 80 82
subtype3 53 58

Figure S30.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S37.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 234 147 422
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.179 (logrank test)

Table S38.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 748 92 0.0 - 223.4 (19.0)
subtype1 215 32 0.0 - 211.5 (19.2)
subtype2 142 13 0.3 - 194.3 (28.6)
subtype3 391 47 0.0 - 223.4 (17.0)

Figure S31.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00573 (ANOVA)

Table S39.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 802 58.0 (13.1)
subtype1 234 57.0 (12.8)
subtype2 146 55.7 (12.3)
subtype3 422 59.3 (13.5)

Figure S32.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0991 (Fisher's exact test)

Table S40.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 795 8
subtype1 234 0
subtype2 146 1
subtype3 415 7

Figure S33.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.467 (Fisher's exact test)

Table S41.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 191 612
subtype1 57 177
subtype2 40 107
subtype3 94 328

Figure S34.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.184 (Fisher's exact test)

Table S42.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 298 505
subtype1 86 148
subtype2 64 83
subtype3 148 274

Figure S35.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S43.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 235 376 192
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00841 (logrank test)

Table S44.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 748 92 0.0 - 223.4 (19.0)
subtype1 229 19 0.1 - 194.3 (25.4)
subtype2 343 47 0.0 - 223.4 (17.0)
subtype3 176 26 0.0 - 211.5 (19.1)

Figure S36.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000672 (ANOVA)

Table S45.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 802 58.0 (13.1)
subtype1 234 56.2 (12.2)
subtype2 376 59.9 (13.6)
subtype3 192 56.4 (12.8)

Figure S37.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0886 (Fisher's exact test)

Table S46.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 795 8
subtype1 234 1
subtype2 369 7
subtype3 192 0

Figure S38.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.255 (Fisher's exact test)

Table S47.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 191 612
subtype1 59 176
subtype2 80 296
subtype3 52 140

Figure S39.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0533 (Fisher's exact test)

Table S48.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 298 505
subtype1 101 134
subtype2 125 251
subtype3 72 120

Figure S40.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S49.  Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 185 411 242
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00145 (logrank test)

Table S50.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 779 95 0.0 - 223.4 (18.9)
subtype1 178 19 0.4 - 159.1 (21.0)
subtype2 381 37 0.0 - 223.4 (17.9)
subtype3 220 39 0.0 - 211.5 (19.2)

Figure S41.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0286 (ANOVA)

Table S51.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 837 58.4 (13.3)
subtype1 184 57.3 (12.7)
subtype2 411 59.7 (13.7)
subtype3 242 57.1 (12.8)

Figure S42.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.683 (Fisher's exact test)

Table S52.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 829 9
subtype1 184 1
subtype2 405 6
subtype3 240 2

Figure S43.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.424 (Fisher's exact test)

Table S53.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 193 645
subtype1 37 148
subtype2 102 309
subtype3 54 188

Figure S44.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.587 (Fisher's exact test)

Table S54.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 303 535
subtype1 68 117
subtype2 154 257
subtype3 81 161

Figure S45.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S55.  Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 205 268 365
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0666 (logrank test)

Table S56.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 779 95 0.0 - 223.4 (18.9)
subtype1 192 30 0.0 - 211.5 (19.7)
subtype2 237 31 0.1 - 223.4 (17.8)
subtype3 350 34 0.0 - 177.4 (18.6)

Figure S46.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.00927 (ANOVA)

Table S57.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 837 58.4 (13.3)
subtype1 205 56.9 (12.7)
subtype2 268 60.4 (14.3)
subtype3 364 57.8 (12.7)

Figure S47.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.359 (Fisher's exact test)

Table S58.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 829 9
subtype1 204 1
subtype2 263 5
subtype3 362 3

Figure S48.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.687 (Fisher's exact test)

Table S59.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 193 645
subtype1 49 156
subtype2 65 203
subtype3 79 286

Figure S49.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.919 (Fisher's exact test)

Table S60.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 303 535
subtype1 73 132
subtype2 95 173
subtype3 135 230

Figure S50.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = BRCA.mergedcluster.txt

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

  • Number of patients = 866

  • Number of clustering approaches = 10

  • Number of selected clinical features = 5

  • 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

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)
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