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 7 different clustering approaches and 5 clinical features across 852 patients, 12 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'.

  • 6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and '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 5 subtypes that correlate to 'Time to Death' and 'AGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 5 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 12 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER RADIATIONS
RADIATION
REGIMENINDICATION 		NEOADJUVANT
THERAPY
Statistical Tests logrank test ANOVA Chi-square test Chi-square test Chi-square 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
METHLYATION CNMF 0.00169 3.91e-07 0.0642 0.291 0.932
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 0.0145 0.00623 0.152 0.964 0.263
MIRseq cHierClus subtypes 0.0244 0.0124 0.382 0.225 0.146
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: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3 4 5 6
Number of samples 89 139 94 95 33 89
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00169 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 510 61 0.0 - 223.4 (17.9)
subtype1 83 13 0.2 - 211.5 (17.2)
subtype2 130 10 0.3 - 223.4 (13.2)
subtype3 92 16 0.0 - 109.9 (14.2)
subtype4 90 10 0.1 - 177.4 (23.4)
subtype5 32 4 4.3 - 157.4 (20.9)
subtype6 83 8 0.0 - 194.3 (25.7)

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

'METHLYATION CNMF' versus 'AGE'

P value = 3.91e-07 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 538 57.6 (13.1)
subtype1 89 55.5 (12.9)
subtype2 139 59.0 (12.3)
subtype3 94 63.8 (12.0)
subtype4 95 53.6 (14.7)
subtype5 33 54.7 (13.1)
subtype6 88 56.0 (11.4)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0642 (Chi-square test)

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

nPatients FEMALE MALE
ALL 533 6
subtype1 89 0
subtype2 138 1
subtype3 92 2
subtype4 94 1
subtype5 31 2
subtype6 89 0

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

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

P value = 0.291 (Chi-square test)

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

nPatients NO YES
ALL 134 405
subtype1 29 60
subtype2 33 106
subtype3 20 74
subtype4 27 68
subtype5 5 28
subtype6 20 69

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.932 (Chi-square test)

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

nPatients NO YES
ALL 202 337
subtype1 35 54
subtype2 55 84
subtype3 34 60
subtype4 37 58
subtype5 11 22
subtype6 30 59

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

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S19.  Get Full Table Description of clustering approach #4: '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 S20.  Clustering Approach #4: '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 S16.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00573 (ANOVA)

Table S21.  Clustering Approach #4: '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 S17.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0991 (Fisher's exact test)

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

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

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

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

P value = 0.467 (Fisher's exact test)

Table S23.  Clustering Approach #4: '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 S19.  Get High-res Image Clustering Approach #4: '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 S24.  Clustering Approach #4: '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 S20.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S25.  Get Full Table Description of clustering approach #5: '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 S26.  Clustering Approach #5: '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 S21.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000672 (ANOVA)

Table S27.  Clustering Approach #5: '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 S22.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0886 (Fisher's exact test)

Table S28.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

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

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

P value = 0.255 (Fisher's exact test)

Table S29.  Clustering Approach #5: '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 S24.  Get High-res Image Clustering Approach #5: '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 S30.  Clustering Approach #5: '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 S25.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S31.  Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 199 393 215
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0145 (logrank test)

Table S32.  Clustering Approach #6: 'MIRseq 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 190 20 0.3 - 159.1 (23.0)
subtype2 364 40 0.0 - 223.4 (18.0)
subtype3 194 32 0.0 - 211.5 (18.0)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.00623 (ANOVA)

Table S33.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 806 58.2 (13.2)
subtype1 198 56.2 (12.5)
subtype2 393 59.6 (13.6)
subtype3 215 57.4 (12.9)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.152 (Fisher's exact test)

Table S34.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 798 9
subtype1 199 0
subtype2 386 7
subtype3 213 2

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

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

P value = 0.964 (Fisher's exact test)

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

nPatients NO YES
ALL 188 619
subtype1 45 154
subtype2 92 301
subtype3 51 164

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

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.263 (Fisher's exact test)

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

nPatients NO YES
ALL 298 509
subtype1 82 117
subtype2 144 249
subtype3 72 143

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

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S37.  Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 178 181 187 229 32
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0244 (logrank test)

Table S38.  Clustering Approach #7: 'MIRseq 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 169 27 0.1 - 211.5 (19.5)
subtype2 159 19 0.1 - 223.4 (16.5)
subtype3 170 24 0.0 - 189.0 (17.8)
subtype4 222 19 0.1 - 177.4 (20.3)
subtype5 28 3 0.5 - 113.8 (34.2)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0124 (ANOVA)

Table S39.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 806 58.2 (13.2)
subtype1 178 56.3 (12.9)
subtype2 181 59.7 (15.0)
subtype3 187 59.4 (12.4)
subtype4 228 58.3 (12.2)
subtype5 32 52.9 (14.5)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.382 (Chi-square test)

Table S40.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 798 9
subtype1 177 1
subtype2 177 4
subtype3 184 3
subtype4 228 1
subtype5 32 0

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

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

P value = 0.225 (Chi-square test)

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

nPatients NO YES
ALL 188 619
subtype1 42 136
subtype2 47 134
subtype3 34 153
subtype4 54 175
subtype5 11 21

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

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.146 (Chi-square test)

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

nPatients NO YES
ALL 298 509
subtype1 64 114
subtype2 67 114
subtype3 57 130
subtype4 95 134
subtype5 15 17

Figure S35.  Get High-res Image Clustering Approach #7: '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 = 852

  • Number of clustering approaches = 7

  • 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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