Breast Invasive Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 4 clinical features across 866 patients, 13 significant findings detected with P value < 0.05.

  • CNMF clustering analysis on array-based mRNA expression data identified 8 subtypes that correlate to '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 'GENDER'.

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

  • 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 '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 4 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 13 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER RADIATIONS
RADIATION
REGIMENINDICATION
Statistical Tests logrank test ANOVA Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.151 2.3e-06 0.0519 0.897
mRNA cHierClus subtypes 0.842 0.00176 0.322 0.195
CN CNMF 0.221 0.101 0.000906 0.355
METHLYATION CNMF 0.235 0.00191 0.0441 0.204
RPPA CNMF subtypes 0.0438 0.0191 0.058 0.944
RPPA cHierClus subtypes 0.238 0.000121 0.187 0.726
RNAseq CNMF subtypes 0.258 0.012 0.0682 0.458
RNAseq cHierClus subtypes 0.234 0.00176 0.0578 0.169
MIRseq CNMF subtypes 0.00629 0.0286 0.683 0.424
MIRseq cHierClus subtypes 0.186 0.00927 0.359 0.687
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 = 0.151 (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'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S6.  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 = 0.842 (logrank test)

Table S7.  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 S5.  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 S8.  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 S6.  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 S9.  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 S7.  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 S10.  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 S8.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #3: 'CN CNMF'

Table S11.  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 = 0.221 (logrank test)

Table S12.  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 S9.  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 S13.  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 S10.  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 S14.  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 S11.  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 S15.  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 S12.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S16.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6
Number of samples 96 138 78 125 33 82
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.235 (logrank test)

Table S17.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 523 61 0.0 - 223.4 (17.9)
subtype1 91 14 0.2 - 211.5 (19.2)
subtype2 130 13 0.3 - 223.4 (13.7)
subtype3 76 12 0.0 - 109.9 (17.7)
subtype4 118 12 0.1 - 194.3 (19.0)
subtype5 32 3 0.1 - 157.4 (20.4)
subtype6 76 7 0.0 - 130.2 (25.6)

Figure S13.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00191 (ANOVA)

Table S18.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 551 57.8 (13.1)
subtype1 96 56.3 (13.1)
subtype2 138 59.6 (12.6)
subtype3 78 62.3 (11.7)
subtype4 125 55.4 (14.4)
subtype5 33 55.7 (13.7)
subtype6 81 56.5 (11.7)

Figure S14.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0441 (Chi-square test)

Table S19.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 546 6
subtype1 96 0
subtype2 137 1
subtype3 76 2
subtype4 124 1
subtype5 31 2
subtype6 82 0

Figure S15.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.204 (Chi-square test)

Table S20.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 136 416
subtype1 29 67
subtype2 32 106
subtype3 16 62
subtype4 37 88
subtype5 4 29
subtype6 18 64

Figure S16.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S21.  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 = 0.0438 (logrank test)

Table S22.  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 S17.  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 S23.  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 S18.  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 S24.  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 S19.  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 S25.  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 S20.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S26.  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 = 0.238 (logrank test)

Table S27.  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 S21.  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 S28.  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 S22.  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 S29.  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 S23.  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 S30.  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 S24.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S31.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 239 155 440
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.258 (logrank test)

Table S32.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 777 94 0.0 - 223.4 (18.9)
subtype1 221 33 0.0 - 211.5 (19.3)
subtype2 149 12 0.3 - 194.3 (25.4)
subtype3 407 49 0.0 - 223.4 (17.0)

Figure S25.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.012 (ANOVA)

Table S33.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 833 58.2 (13.2)
subtype1 239 57.2 (12.8)
subtype2 154 56.2 (12.4)
subtype3 440 59.4 (13.5)

Figure S26.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0682 (Fisher's exact test)

Table S34.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 825 9
subtype1 239 0
subtype2 154 1
subtype3 432 8

Figure S27.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.458 (Fisher's exact test)

Table S35.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 197 637
subtype1 57 182
subtype2 42 113
subtype3 98 342

Figure S28.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S36.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 417 218 199
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.234 (logrank test)

Table S37.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 777 94 0.0 - 223.4 (18.9)
subtype1 384 47 0.0 - 223.4 (16.3)
subtype2 210 19 0.1 - 194.3 (25.5)
subtype3 183 28 0.0 - 211.5 (20.0)

Figure S29.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00176 (ANOVA)

Table S38.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 833 58.2 (13.2)
subtype1 417 59.8 (13.3)
subtype2 217 56.4 (13.0)
subtype3 199 56.7 (12.8)

Figure S30.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0578 (Fisher's exact test)

Table S39.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 825 9
subtype1 409 8
subtype2 217 1
subtype3 199 0

Figure S31.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.169 (Fisher's exact test)

Table S40.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 197 637
subtype1 87 330
subtype2 58 160
subtype3 52 147

Figure S32.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S41.  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 = 0.00629 (logrank test)

Table S42.  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 S33.  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 S43.  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 S34.  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 S44.  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 S35.  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 S45.  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 S36.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S46.  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 = 0.186 (logrank test)

Table S47.  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 S37.  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 S48.  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 S38.  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 S49.  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 S39.  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 S50.  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 S40.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data
Input

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

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

  • Number of patients = 866

  • Number of clustering approaches = 10

  • Number of selected clinical features = 4

  • 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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle