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 851 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 'Time to Death', 'AGE', and 'GENDER'.

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

- 7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', and 'GENDER'.

- 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 'NEOADJUVANT.THERAPY'.

- 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 '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, 13 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 | 7.48e-05 | 5.88e-06 | 0.0354 | 0.848 | 0.398
mRNA cHierClus subtypes | 0.445 | 0.00183 | 0.323 | 0.224 | 0.38
METHLYATION CNMF | 0.000294 | 0.00115 | 0.0202 | 0.13 | 0.988
RNAseq CNMF subtypes | 0.137 | 0.0386 | 0.148 | 0.388 | 0.0981
RNAseq cHierClus subtypes | 0.187 | 0.118 | 0.158 | 0.142 | 0.00648
MIRseq CNMF subtypes | 0.00773 | 0.00868 | 0.222 | 0.685 | 0.281
MIRseq cHierClus subtypes | 0.0258 | 0.0131 | 0.13 | 0.412 | 0.282

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 | 20 | 34 | 117 | 103 | 120 | 73 | 20 | 42

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 7.48e-05 (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 | 18 | 3 | 0.3 - 92.0 (14.2)
subtype2 | 33 | 3 | 0.1 - 157.4 (43.4)
subtype3 | 109 | 17 | 0.1 - 177.4 (25.1)
subtype4 | 98 | 12 | 0.1 - 211.5 (21.9)
subtype5 | 115 | 10 | 0.3 - 223.4 (19.0)
subtype6 | 64 | 14 | 0.1 - 189.0 (24.6)
subtype7 | 19 | 2 | 0.2 - 97.5 (36.3)
subtype8 | 41 | 4 | 0.3 - 112.4 (20.0)

Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'AGE'

P value = 5.88e-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 | 20 | 59.6 (14.1)
subtype2 | 34 | 49.9 (10.1)
subtype3 | 117 | 58.0 (14.3)
subtype4 | 103 | 53.8 (12.6)
subtype5 | 120 | 62.0 (12.7)
subtype6 | 73 | 58.2 (12.7)
subtype7 | 20 | 60.4 (9.9)
subtype8 | 42 | 59.9 (12.8)

Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0354 (Chi-square test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 523 | 6
subtype1 | 19 | 1
subtype2 | 34 | 0
subtype3 | 113 | 4
subtype4 | 103 | 0
subtype5 | 120 | 0
subtype6 | 73 | 0
subtype7 | 19 | 1
subtype8 | 42 | 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 = 0.848 (Chi-square test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 382 | 147
subtype1 | 17 | 3
subtype2 | 25 | 9
subtype3 | 86 | 31
subtype4 | 69 | 34
subtype5 | 86 | 34
subtype6 | 54 | 19
subtype7 | 15 | 5
subtype8 | 30 | 12

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 = 0.398 (Chi-square test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 307 | 222
subtype1 | 15 | 5
subtype2 | 17 | 17
subtype3 | 66 | 51
subtype4 | 55 | 48
subtype5 | 71 | 49
subtype6 | 47 | 26
subtype7 | 14 | 6
subtype8 | 22 | 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 | 136 | 264

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.445 (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 | 134 | 15 | 0.3 - 157.4 (27.6)
subtype3 | 245 | 35 | 0.1 - 223.4 (21.1)

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 = 0.00183 (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 | 136 | 56.8 (13.0)
subtype3 | 264 | 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 = 0.323 (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 | 135 | 1
subtype3 | 259 | 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 = 0.224 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 382 | 147
subtype1 | 86 | 43
subtype2 | 98 | 38
subtype3 | 198 | 66

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 = 0.38 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 307 | 222
subtype1 | 70 | 59
subtype2 | 76 | 60
subtype3 | 161 | 103

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 | 7
--- | --- | --- | --- | --- | --- | --- | ---
Number of samples | 82 | 137 | 54 | 97 | 29 | 52 | 87

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.000294 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 509 | 61 | 0.0 - 223.4 (17.9)
subtype1 | 77 | 12 | 0.2 - 211.5 (19.2)
subtype2 | 129 | 12 | 0.3 - 223.4 (12.8)
subtype3 | 50 | 10 | 0.0 - 162.0 (14.0)
subtype4 | 93 | 7 | 0.1 - 177.4 (23.1)
subtype5 | 29 | 3 | 0.1 - 157.4 (20.8)
subtype6 | 50 | 9 | 0.0 - 109.9 (17.9)
subtype7 | 81 | 8 | 0.0 - 194.3 (27.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00115 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 536 | 57.5 (13.1)
subtype1 | 82 | 54.8 (11.9)
subtype2 | 137 | 58.7 (12.1)
subtype3 | 54 | 59.0 (12.3)
subtype4 | 97 | 55.3 (15.0)
subtype5 | 29 | 57.1 (15.8)
subtype6 | 52 | 63.9 (11.5)
subtype7 | 85 | 56.0 (12.4)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0202 (Chi-square test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 532 | 6
subtype1 | 82 | 0
subtype2 | 136 | 1
subtype3 | 54 | 0
subtype4 | 96 | 1
subtype5 | 27 | 2
subtype6 | 50 | 2
subtype7 | 87 | 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 = 0.13 (Chi-square test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 409 | 129
subtype1 | 54 | 28
subtype2 | 107 | 30
subtype3 | 37 | 17
subtype4 | 75 | 22
subtype5 | 25 | 4
subtype6 | 43 | 9
subtype7 | 68 | 19

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 = 0.988 (Chi-square test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 336 | 202
subtype1 | 49 | 33
subtype2 | 84 | 53
subtype3 | 33 | 21
subtype4 | 62 | 35
subtype5 | 19 | 10
subtype6 | 32 | 20
subtype7 | 57 | 30

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 | 229 | 130 | 392

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.137 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 697 | 86 | 0.0 - 223.4 (18.1)
subtype1 | 208 | 31 | 0.0 - 211.5 (18.4)
subtype2 | 124 | 10 | 0.3 - 157.4 (26.3)
subtype3 | 365 | 45 | 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 = 0.0386 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 749 | 57.7 (13.3)
subtype1 | 229 | 56.6 (12.9)
subtype2 | 128 | 56.1 (12.5)
subtype3 | 392 | 58.9 (13.6)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.148 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 744 | 7
subtype1 | 229 | 0
subtype2 | 129 | 1
subtype3 | 386 | 6

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 = 0.388 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 568 | 183
subtype1 | 170 | 59
subtype2 | 94 | 36
subtype3 | 304 | 88

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 = 0.0981 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 458 | 293
subtype1 | 139 | 90
subtype2 | 69 | 61
subtype3 | 250 | 142

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 | 195 | 380 | 176

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.187 (logrank test)

Table S26. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 697 | 86 | 0.0 - 223.4 (18.1)
subtype1 | 179 | 26 | 0.0 - 211.5 (16.5)
subtype2 | 348 | 44 | 0.0 - 223.4 (16.8)
subtype3 | 170 | 16 | 0.1 - 157.4 (25.5)

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 = 0.118 (ANOVA)

Table S27. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 749 | 57.7 (13.3)
subtype1 | 195 | 56.3 (13.0)
subtype2 | 380 | 58.6 (13.6)
subtype3 | 174 | 57.3 (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 = 0.158 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 744 | 7
subtype1 | 195 | 0
subtype2 | 374 | 6
subtype3 | 175 | 1

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 = 0.142 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 568 | 183
subtype1 | 142 | 53
subtype2 | 299 | 81
subtype3 | 127 | 49

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 = 0.00648 (Fisher's exact test)

Table S30. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 458 | 293
subtype1 | 117 | 78
subtype2 | 250 | 130
subtype3 | 91 | 85

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 | 194 | 403 | 210

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00773 (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 | 186 | 21 | 0.3 - 159.1 (24.5)
subtype2 | 372 | 39 | 0.0 - 223.4 (18.0)
subtype3 | 190 | 32 | 0.0 - 211.5 (17.6)

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 = 0.00868 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 805 | 58.2 (13.3)
subtype1 | 192 | 56.0 (12.6)
subtype2 | 403 | 59.5 (13.6)
subtype3 | 210 | 57.6 (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 = 0.222 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 798 | 9
subtype1 | 194 | 0
subtype2 | 396 | 7
subtype3 | 208 | 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 = 0.685 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 623 | 184
subtype1 | 154 | 40
subtype2 | 307 | 96
subtype3 | 162 | 48

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 = 0.281 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 508 | 299
subtype1 | 116 | 78
subtype2 | 251 | 152
subtype3 | 141 | 69

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
--- | --- | --- | ---
Number of samples | 223 | 423 | 161

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0258 (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 | 194 | 26 | 0.0 - 194.3 (17.0)
subtype2 | 401 | 40 | 0.1 - 223.4 (19.1)
subtype3 | 153 | 26 | 0.1 - 211.5 (20.0)

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 = 0.0131 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 805 | 58.2 (13.3)
subtype1 | 223 | 59.4 (13.4)
subtype2 | 421 | 58.5 (13.2)
subtype3 | 161 | 55.5 (12.8)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.13 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 798 | 9
subtype1 | 218 | 5
subtype2 | 419 | 4
subtype3 | 161 | 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 = 0.412 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 623 | 184
subtype1 | 179 | 44
subtype2 | 323 | 100
subtype3 | 121 | 40

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 = 0.282 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 508 | 299
subtype1 | 150 | 73
subtype2 | 258 | 165
subtype3 | 100 | 61

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 = 851

- 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.

- 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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