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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[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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