Ovarian Serous Cystadenocarcinoma: 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 6 clinical features across 566 patients, 7 significant findings detected with P value < 0.05.

- CNMF clustering analysis on array-based mRNA expression data identified 3 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 'Time to Death' and 'AGE'.

- CNMF clustering analysis on array-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.

- Consensus hierarchical clustering analysis on array-based miR expression data identified 3 subtypes that do not correlate to any clinical features.

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

- CNMF clustering analysis on sequencing-based miR expression data identified 2 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 5 subtypes that do not correlate to any clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 6 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 7 significant findings detected.

Clinical Features | Time to Death | AGE | PRIMARY SITE OF DISEASE | KARNOFSKY PERFORMANCE SCORE | RADIATIONS RADIATION REGIMENINDICATION | NEOADJUVANT THERAPY
Statistical Tests | logrank test | ANOVA | Chi-square test | ANOVA | Chi-square test | Chi-square test
mRNA CNMF subtypes | 0.00682 | 0.0382 | 0.577 | 0.0882 | 0.79 | 0.385
mRNA cHierClus subtypes | 0.000224 | 0.012 | 0.608 | 0.205 | 0.609 | 0.486
miR CNMF subtypes | 0.0104 | 0.757 | 0.22 | 0.677 | 0.264 | 0.437
miR cHierClus subtypes | 0.271 | 0.707 | 0.512 | 0.995 | 1 | 0.314
METHLYATION CNMF | 0.0157 | 2.34e-16 | 0.173 | 0.305 | 0.472 | 0.224
MIRseq CNMF subtypes | 0.249 | 0.669 |  | 0.289 |  | 1
MIRseq cHierClus subtypes | 0.329 | 0.428 |  | 0.748 |  | 0.816

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 222 | 205 | 137

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.00682 (logrank test)

Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 559 | 292 | 0.3 - 180.2 (28.3)
subtype1 | 219 | 126 | 0.3 - 152.0 (29.2)
subtype2 | 203 | 90 | 0.4 - 180.2 (28.6)
subtype3 | 137 | 76 | 0.3 - 119.1 (24.3)

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 = 0.0382 (ANOVA)

Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 553 | 59.7 (11.6)
subtype1 | 216 | 61.2 (11.6)
subtype2 | 201 | 58.4 (10.9)
subtype3 | 136 | 59.2 (12.4)

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

'mRNA CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.577 (Chi-square test)

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 560 | 2
subtype1 | 0 | 222 | 0
subtype2 | 1 | 203 | 1
subtype3 | 1 | 135 | 1

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'mRNA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0882 (ANOVA)

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 78 | 75.6 (12.8)
subtype1 | 31 | 78.1 (13.0)
subtype2 | 26 | 76.9 (12.3)
subtype3 | 21 | 70.5 (12.0)

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.79 (Fisher's exact test)

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 561 | 3
subtype1 | 220 | 2
subtype2 | 204 | 1
subtype3 | 137 | 0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.385 (Fisher's exact test)

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 456 | 108
subtype1 | 173 | 49
subtype2 | 170 | 35
subtype3 | 113 | 24

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 240 | 130 | 194

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.000224 (logrank test)

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 559 | 292 | 0.3 - 180.2 (28.3)
subtype1 | 238 | 99 | 0.3 - 180.2 (27.3)
subtype2 | 130 | 79 | 0.3 - 119.1 (24.3)
subtype3 | 191 | 114 | 0.3 - 152.0 (31.2)

Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.012 (ANOVA)

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 553 | 59.7 (11.6)
subtype1 | 237 | 58.7 (11.1)
subtype2 | 129 | 58.5 (12.2)
subtype3 | 187 | 61.7 (11.7)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.608 (Chi-square test)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 560 | 2
subtype1 | 1 | 238 | 1
subtype2 | 1 | 128 | 1
subtype3 | 0 | 194 | 0

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'mRNA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.205 (ANOVA)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 78 | 75.6 (12.8)
subtype1 | 34 | 74.7 (11.3)
subtype2 | 19 | 72.6 (13.7)
subtype3 | 25 | 79.2 (13.5)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.609 (Fisher's exact test)

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 561 | 3
subtype1 | 239 | 1
subtype2 | 130 | 0
subtype3 | 192 | 2

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.486 (Fisher's exact test)

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 456 | 108
subtype1 | 199 | 41
subtype2 | 105 | 25
subtype3 | 152 | 42

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S15. Get Full Table Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 167 | 154 | 241

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.0104 (logrank test)

Table S16. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 557 | 292 | 0.3 - 180.2 (28.2)
subtype1 | 166 | 91 | 0.3 - 130.0 (26.4)
subtype2 | 151 | 86 | 0.3 - 109.7 (25.1)
subtype3 | 240 | 115 | 0.3 - 180.2 (29.3)

Figure S13. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.757 (ANOVA)

Table S17. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 551 | 59.7 (11.6)
subtype1 | 163 | 59.7 (12.4)
subtype2 | 152 | 59.1 (11.6)
subtype3 | 236 | 60.0 (11.1)

Figure S14. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

'miR CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.22 (Chi-square test)

Table S18. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 558 | 2
subtype1 | 0 | 165 | 2
subtype2 | 1 | 153 | 0
subtype3 | 1 | 240 | 0

Figure S15. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'miR CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.677 (ANOVA)

Table S19. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 78 | 75.6 (12.8)
subtype1 | 25 | 76.8 (12.5)
subtype2 | 22 | 73.6 (15.6)
subtype3 | 31 | 76.1 (10.9)

Figure S16. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.264 (Fisher's exact test)

Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 559 | 3
subtype1 | 167 | 0
subtype2 | 154 | 0
subtype3 | 238 | 3

Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.437 (Fisher's exact test)

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 454 | 108
subtype1 | 137 | 30
subtype2 | 119 | 35
subtype3 | 198 | 43

Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S22. Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 293 | 137 | 132

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.271 (logrank test)

Table S23. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 557 | 292 | 0.3 - 180.2 (28.2)
subtype1 | 289 | 146 | 0.3 - 152.0 (28.5)
subtype2 | 137 | 73 | 0.3 - 130.0 (29.8)
subtype3 | 131 | 73 | 1.0 - 180.2 (27.5)

Figure S19. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'AGE'

P value = 0.707 (ANOVA)

Table S24. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 551 | 59.7 (11.6)
subtype1 | 288 | 60.1 (11.5)
subtype2 | 132 | 59.4 (12.0)
subtype3 | 131 | 59.1 (11.5)

Figure S20. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'miR cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.512 (Chi-square test)

Table S25. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 558 | 2
subtype1 | 1 | 292 | 0
subtype2 | 0 | 136 | 1
subtype3 | 1 | 130 | 1

Figure S21. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'miR cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.995 (ANOVA)

Table S26. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 78 | 75.6 (12.8)
subtype1 | 38 | 75.8 (13.3)
subtype2 | 18 | 75.6 (12.9)
subtype3 | 22 | 75.5 (12.2)

Figure S22. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 1 (Fisher's exact test)

Table S27. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 559 | 3
subtype1 | 291 | 2
subtype2 | 136 | 1
subtype3 | 132 | 0

Figure S23. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.314 (Fisher's exact test)

Table S28. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 454 | 108
subtype1 | 236 | 57
subtype2 | 116 | 21
subtype3 | 102 | 30

Figure S24. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'METHLYATION CNMF'

Table S29. Get Full Table Description of clustering approach #5: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6
--- | --- | --- | --- | --- | --- | ---
Number of samples | 62 | 109 | 165 | 92 | 58 | 63

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0157 (logrank test)

Table S30. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 543 | 290 | 0.3 - 180.2 (29.0)
subtype1 | 61 | 31 | 0.3 - 125.7 (31.1)
subtype2 | 108 | 69 | 0.4 - 152.0 (30.2)
subtype3 | 163 | 83 | 0.3 - 180.2 (29.1)
subtype4 | 92 | 51 | 0.4 - 119.1 (26.6)
subtype5 | 57 | 30 | 0.8 - 115.9 (31.8)
subtype6 | 62 | 26 | 0.8 - 130.0 (19.2)

Figure S25. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 2.34e-16 (ANOVA)

Table S31. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 539 | 59.7 (11.7)
subtype1 | 61 | 60.9 (12.5)
subtype2 | 107 | 64.9 (10.5)
subtype3 | 164 | 53.4 (10.2)
subtype4 | 92 | 62.8 (10.1)
subtype5 | 55 | 61.7 (11.9)
subtype6 | 60 | 60.0 (11.7)

Figure S26. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.173 (Chi-square test)

Table S32. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 545 | 2
subtype1 | 0 | 62 | 0
subtype2 | 0 | 109 | 0
subtype3 | 1 | 164 | 0
subtype4 | 0 | 90 | 2
subtype5 | 0 | 58 | 0
subtype6 | 1 | 62 | 0

Figure S27. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.305 (ANOVA)

Table S33. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 78 | 75.6 (12.8)
subtype1 | 11 | 70.9 (16.4)
subtype2 | 13 | 78.5 (15.2)
subtype3 | 17 | 80.0 (10.0)
subtype4 | 13 | 70.8 (13.2)
subtype5 | 9 | 75.6 (13.3)
subtype6 | 15 | 76.0 (8.3)

Figure S28. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.472 (Chi-square test)

Table S34. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 546 | 3
subtype1 | 61 | 1
subtype2 | 108 | 1
subtype3 | 165 | 0
subtype4 | 92 | 0
subtype5 | 58 | 0
subtype6 | 62 | 1

Figure S29. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.224 (Chi-square test)

Table S35. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 441 | 108
subtype1 | 48 | 14
subtype2 | 82 | 27
subtype3 | 139 | 26
subtype4 | 79 | 13
subtype5 | 43 | 15
subtype6 | 50 | 13

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

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S36. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 16 | 30

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.249 (logrank test)

Table S37. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 45 | 21 | 0.3 - 69.0 (30.0)
subtype1 | 16 | 9 | 2.2 - 68.0 (30.2)
subtype2 | 29 | 12 | 0.3 - 69.0 (30.0)

Figure S31. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.669 (t-test)

Table S38. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 46 | 61.6 (12.5)
subtype1 | 16 | 62.8 (13.0)
subtype2 | 30 | 61.0 (12.4)

Figure S32. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.289 (t-test)

Table S39. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 18 | 76.7 (10.3)
subtype1 | 5 | 72.0 (11.0)
subtype2 | 13 | 78.5 (9.9)

Figure S33. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S40. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 11 | 35
subtype1 | 4 | 12
subtype2 | 7 | 23

Figure S34. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S41. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 9 | 13 | 9 | 6 | 9

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.329 (logrank test)

Table S42. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 45 | 21 | 0.3 - 69.0 (30.0)
subtype1 | 9 | 4 | 2.2 - 54.5 (23.1)
subtype2 | 13 | 6 | 0.3 - 69.0 (37.1)
subtype3 | 8 | 4 | 6.1 - 68.0 (39.7)
subtype4 | 6 | 3 | 8.2 - 63.1 (27.9)
subtype5 | 9 | 4 | 8.3 - 49.1 (25.9)

Figure S35. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.428 (ANOVA)

Table S43. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 46 | 61.6 (12.5)
subtype1 | 9 | 61.6 (15.2)
subtype2 | 13 | 62.9 (11.2)
subtype3 | 9 | 58.2 (12.8)
subtype4 | 6 | 69.5 (10.4)
subtype5 | 9 | 58.0 (12.4)

Figure S36. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.748 (ANOVA)

Table S44. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 18 | 76.7 (10.3)
subtype1 | 2 | 80.0 (0.0)
subtype2 | 6 | 73.3 (10.3)
subtype3 | 2 | 80.0 (0.0)
subtype4 | 5 | 76.0 (16.7)
subtype5 | 3 | 80.0 (0.0)

Figure S37. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.816 (Chi-square test)

Table S45. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 11 | 35
subtype1 | 2 | 7
subtype2 | 3 | 10
subtype3 | 3 | 6
subtype4 | 2 | 4
subtype5 | 1 | 8

Figure S38. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = OV.mergedcluster.txt

- Clinical data file = OV.clin.merged.picked.txt

- Number of patients = 566

- Number of clustering approaches = 7

- Number of selected clinical features = 6

- 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.test' function in R

Download Results

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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)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
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