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 828 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', 'AGE', and 'GENDER'.

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

- 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', 'GENDER', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

- 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', 'AGE', and 'GENDER'.

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to '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, 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 | 7.48e-05 | 5.88e-06 | 0.0354 | 0.848 | 0.454
mRNA cHierClus subtypes | 0.445 | 0.00183 | 0.323 | 0.224 | 0.323
METHLYATION CNMF | 0.00518 | 3.46e-05 | 0.0339 | 0.0059 | 0.00153
RNAseq CNMF subtypes | 0.215 | 0.0115 | 0.0875 | 0.285 | 0.214
RNAseq cHierClus subtypes | 0.0273 | 0.00847 | 0.0266 | 0.127 | 0.0735
MIRseq CNMF subtypes | 0.102 | 0.000189 | 0.296 | 0.63 | 0.367
MIRseq cHierClus subtypes | 0.0143 | 3.4e-05 | 0.0666 | 0.574 | 0.446

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 = 0.454 (Chi-square test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 308 | 221
subtype1 | 15 | 5
subtype2 | 18 | 16
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 = 0.323 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 308 | 221
subtype1 | 70 | 59
subtype2 | 76 | 60
subtype3 | 162 | 102

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
--- | --- | --- | ---
Number of samples | 107 | 104 | 102

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00518 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 283 | 36 | 0.1 - 220.9 (19.0)
subtype1 | 99 | 12 | 0.6 - 220.9 (19.1)
subtype2 | 93 | 9 | 0.1 - 189.0 (22.0)
subtype3 | 91 | 15 | 0.1 - 146.5 (16.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 3.46e-05 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 313 | 59.5 (13.4)
subtype1 | 107 | 58.7 (13.3)
subtype2 | 104 | 55.7 (12.4)
subtype3 | 102 | 64.0 (13.2)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0339 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 310 | 3
subtype1 | 107 | 0
subtype2 | 104 | 0
subtype3 | 99 | 3

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

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

P value = 0.0059 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 246 | 67
subtype1 | 74 | 33
subtype2 | 83 | 21
subtype3 | 89 | 13

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 = 0.00153 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 201 | 112
subtype1 | 58 | 49
subtype2 | 64 | 40
subtype3 | 79 | 23

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 | 215 | 131 | 431

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.215 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 723 | 89 | 0.0 - 223.4 (18.2)
subtype1 | 198 | 28 | 0.0 - 211.5 (17.6)
subtype2 | 126 | 13 | 0.3 - 157.4 (31.1)
subtype3 | 399 | 48 | 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 = 0.0115 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 775 | 57.8 (13.2)
subtype1 | 215 | 56.7 (12.7)
subtype2 | 129 | 55.7 (12.7)
subtype3 | 431 | 59.1 (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 = 0.0875 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 770 | 7
subtype1 | 215 | 0
subtype2 | 131 | 0
subtype3 | 424 | 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 = 0.285 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 598 | 179
subtype1 | 163 | 52
subtype2 | 95 | 36
subtype3 | 340 | 91

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 = 0.214 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 507 | 270
subtype1 | 141 | 74
subtype2 | 77 | 54
subtype3 | 289 | 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 | 222 | 187 | 368

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0273 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 723 | 89 | 0.0 - 223.4 (18.2)
subtype1 | 214 | 18 | 0.1 - 173.0 (21.9)
subtype2 | 173 | 26 | 0.0 - 211.5 (18.8)
subtype3 | 336 | 45 | 0.0 - 223.4 (17.7)

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 = 0.00847 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 775 | 57.8 (13.2)
subtype1 | 220 | 56.5 (12.1)
subtype2 | 187 | 56.4 (12.8)
subtype3 | 368 | 59.4 (13.9)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0266 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 770 | 7
subtype1 | 222 | 0
subtype2 | 187 | 0
subtype3 | 361 | 7

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 = 0.127 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 598 | 179
subtype1 | 164 | 58
subtype2 | 139 | 48
subtype3 | 295 | 73

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 = 0.0735 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 507 | 270
subtype1 | 132 | 90
subtype2 | 122 | 65
subtype3 | 253 | 115

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 | 235 | 328 | 218

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.102 (logrank test)

Table S32. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 722 | 90 | 0.0 - 223.4 (18.2)
subtype1 | 224 | 24 | 0.2 - 189.0 (21.7)
subtype2 | 301 | 35 | 0.0 - 223.4 (17.0)
subtype3 | 197 | 31 | 0.0 - 211.5 (17.2)

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 = 0.000189 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 779 | 58.2 (13.4)
subtype1 | 233 | 56.1 (12.4)
subtype2 | 328 | 60.5 (14.0)
subtype3 | 218 | 56.9 (13.0)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.296 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 772 | 9
subtype1 | 234 | 1
subtype2 | 322 | 6
subtype3 | 216 | 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 = 0.63 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 603 | 178
subtype1 | 181 | 54
subtype2 | 258 | 70
subtype3 | 164 | 54

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 = 0.367 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 506 | 275
subtype1 | 144 | 91
subtype2 | 220 | 108
subtype3 | 142 | 76

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 | 249 | 356 | 176

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0143 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 722 | 90 | 0.0 - 223.4 (18.2)
subtype1 | 223 | 36 | 0.1 - 223.4 (17.9)
subtype2 | 336 | 30 | 0.0 - 189.0 (18.0)
subtype3 | 163 | 24 | 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 = 3.4e-05 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 779 | 58.2 (13.4)
subtype1 | 249 | 61.3 (14.3)
subtype2 | 354 | 57.1 (12.5)
subtype3 | 176 | 56.1 (13.0)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0666 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 772 | 9
subtype1 | 243 | 6
subtype2 | 353 | 3
subtype3 | 176 | 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 = 0.574 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 603 | 178
subtype1 | 196 | 53
subtype2 | 276 | 80
subtype3 | 131 | 45

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 = 0.446 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 506 | 275
subtype1 | 169 | 80
subtype2 | 227 | 129
subtype3 | 110 | 66

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 = 828

- 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. Location of data archives could not be determined.

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)
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