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, 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' 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', '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 '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, 13 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 | 0.000469 | 0.000613 | 0.0224 | 0.42 | 0.897
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 | 382 | 147
subtype1 | 93 | 33
subtype2 | 30 | 11
subtype3 | 17 | 4
subtype4 | 69 | 34
subtype5 | 78 | 29
subtype6 | 54 | 19
subtype7 | 15 | 5
subtype8 | 26 | 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 = 0.391 (Chi-square test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 308 | 221
subtype1 | 73 | 53
subtype2 | 21 | 20
subtype3 | 16 | 5
subtype4 | 55 | 48
subtype5 | 62 | 45
subtype6 | 47 | 26
subtype7 | 14 | 6
subtype8 | 20 | 18

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 | 382 | 147
subtype1 | 86 | 43
subtype2 | 92 | 37
subtype3 | 204 | 67

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 | 308 | 221
subtype1 | 70 | 59
subtype2 | 72 | 57
subtype3 | 166 | 105

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 | 83 | 144 | 64 | 121 | 30 | 97

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.000469 (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 | 78 | 12 | 0.2 - 211.5 (15.0)
subtype2 | 135 | 11 | 0.3 - 223.4 (12.8)
subtype3 | 62 | 12 | 0.0 - 109.9 (17.7)
subtype4 | 116 | 13 | 0.1 - 177.4 (22.3)
subtype5 | 29 | 4 | 0.1 - 157.4 (18.8)
subtype6 | 90 | 9 | 0.0 - 194.3 (26.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.000613 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 538 | 57.6 (13.1)
subtype1 | 83 | 56.3 (13.2)
subtype2 | 144 | 58.9 (12.3)
subtype3 | 64 | 63.5 (11.4)
subtype4 | 121 | 55.9 (14.7)
subtype5 | 30 | 53.4 (13.3)
subtype6 | 96 | 56.2 (11.9)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0224 (Chi-square test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 533 | 6
subtype1 | 83 | 0
subtype2 | 143 | 1
subtype3 | 62 | 2
subtype4 | 120 | 1
subtype5 | 28 | 2
subtype6 | 97 | 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 = 0.42 (Chi-square test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 405 | 134
subtype1 | 56 | 27
subtype2 | 111 | 33
subtype3 | 49 | 15
subtype4 | 88 | 33
subtype5 | 25 | 5
subtype6 | 76 | 21

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 = 0.897 (Chi-square test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 337 | 202
subtype1 | 50 | 33
subtype2 | 88 | 56
subtype3 | 38 | 26
subtype4 | 76 | 45
subtype5 | 20 | 10
subtype6 | 65 | 32

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 | 612 | 191
subtype1 | 177 | 57
subtype2 | 107 | 40
subtype3 | 328 | 94

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 | 505 | 298
subtype1 | 148 | 86
subtype2 | 83 | 64
subtype3 | 274 | 148

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 | 612 | 191
subtype1 | 176 | 59
subtype2 | 296 | 80
subtype3 | 140 | 52

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 | 505 | 298
subtype1 | 134 | 101
subtype2 | 251 | 125
subtype3 | 120 | 72

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 | 619 | 188
subtype1 | 154 | 45
subtype2 | 301 | 92
subtype3 | 164 | 51

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 | 509 | 298
subtype1 | 117 | 82
subtype2 | 249 | 144
subtype3 | 143 | 72

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 | 619 | 188
subtype1 | 136 | 42
subtype2 | 134 | 47
subtype3 | 153 | 34
subtype4 | 175 | 54
subtype5 | 21 | 11

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 | 509 | 298
subtype1 | 114 | 64
subtype2 | 114 | 67
subtype3 | 130 | 57
subtype4 | 134 | 95
subtype5 | 17 | 15

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.

- 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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