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 9 different clustering approaches and 6 clinical features across 566 patients, 10 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.

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

- CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.

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

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

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 9 different clustering approaches and 6 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 10 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 | Fisher's exact test | Fisher's exact 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 | 0.0475 | 2.19e-09 | 0.739 | 0.368 | 0.321 | 0.671
RNAseq CNMF subtypes | 0.23 | 0.00293 | 0.731 | 0.277 | 0.19 | 0.537
RNAseq cHierClus subtypes | 0.788 | 0.153 | 0.614 | 0.277 | 1 | 0.835
MIRseq CNMF subtypes | 0.0208 | 0.53 | 0.385 | 0.113 | 0.476 | 0.704
MIRseq cHierClus subtypes | 0.0354 | 0.353 | 0.809 | 0.382 | 0.284 | 0.461

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
--- | --- | --- | ---
Number of samples | 186 | 177 | 186

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0475 (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 | 184 | 95 | 0.3 - 180.2 (30.1)
subtype2 | 174 | 95 | 0.4 - 125.7 (25.9)
subtype3 | 185 | 100 | 0.4 - 152.0 (29.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 2.19e-09 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 539 | 59.7 (11.7)
subtype1 | 184 | 55.4 (11.3)
subtype2 | 172 | 61.9 (12.1)
subtype3 | 183 | 62.1 (10.4)

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 = 0.739 (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 | 1 | 185 | 0
subtype2 | 0 | 176 | 1
subtype3 | 1 | 184 | 1

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 = 0.368 (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 | 26 | 78.5 (11.2)
subtype2 | 27 | 74.8 (15.3)
subtype3 | 25 | 73.6 (11.1)

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 = 0.321 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 546 | 3
subtype1 | 186 | 0
subtype2 | 175 | 2
subtype3 | 185 | 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 = 0.671 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 441 | 108
subtype1 | 149 | 37
subtype2 | 139 | 38
subtype3 | 153 | 33

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

Clustering Approach #6: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 79 | 103 | 115

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.23 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 295 | 169 | 0.3 - 180.2 (28.3)
subtype1 | 78 | 42 | 1.0 - 116.1 (32.5)
subtype2 | 102 | 63 | 0.3 - 152.0 (27.4)
subtype3 | 115 | 64 | 0.4 - 180.2 (27.5)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00293 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 289 | 59.2 (11.0)
subtype1 | 77 | 55.7 (10.6)
subtype2 | 100 | 61.3 (10.9)
subtype3 | 112 | 59.7 (11.0)

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

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

P value = 0.731 (Fisher's exact test)

Table S39. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY
--- | --- | ---
ALL | 2 | 295
subtype1 | 1 | 78
subtype2 | 0 | 103
subtype3 | 1 | 114

Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.277 (ANOVA)

Table S40. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 16 | 76.2 (13.1)
subtype1 | 2 | 80.0 (0.0)
subtype2 | 3 | 80.0 (0.0)
subtype3 | 11 | 74.5 (15.7)

Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.19 (Fisher's exact test)

Table S41. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 295 | 2
subtype1 | 79 | 0
subtype2 | 101 | 2
subtype3 | 115 | 0

Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.537 (Fisher's exact test)

Table S42. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 241 | 56
subtype1 | 62 | 17
subtype2 | 87 | 16
subtype3 | 92 | 23

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq cHierClus subtypes'

Table S43. Get Full Table Description of clustering approach #7: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 36 | 137 | 124

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.788 (logrank test)

Table S44. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 295 | 169 | 0.3 - 180.2 (28.3)
subtype1 | 36 | 24 | 0.3 - 115.9 (27.8)
subtype2 | 136 | 72 | 0.4 - 180.2 (28.6)
subtype3 | 123 | 73 | 0.5 - 152.0 (28.3)

Figure S37. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.153 (ANOVA)

Table S45. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 289 | 59.2 (11.0)
subtype1 | 35 | 56.2 (10.4)
subtype2 | 134 | 60.2 (11.4)
subtype3 | 120 | 59.0 (10.8)

Figure S38. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.614 (Fisher's exact test)

Table S46. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY
--- | --- | ---
ALL | 2 | 295
subtype1 | 0 | 36
subtype2 | 2 | 135
subtype3 | 0 | 124

Figure S39. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.277 (ANOVA)

Table S47. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 16 | 76.2 (13.1)
subtype2 | 11 | 74.5 (15.7)
subtype3 | 5 | 80.0 (0.0)

Figure S40. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 1 (Fisher's exact test)

Table S48. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 295 | 2
subtype1 | 36 | 0
subtype2 | 136 | 1
subtype3 | 123 | 1

Figure S41. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.835 (Fisher's exact test)

Table S49. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 241 | 56
subtype1 | 28 | 8
subtype2 | 112 | 25
subtype3 | 101 | 23

Figure S42. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'MIRseq CNMF subtypes'

Table S50. Get Full Table Description of clustering approach #8: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 114 | 195 | 145

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0208 (logrank test)

Table S51. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 451 | 252 | 0.3 - 180.2 (30.1)
subtype1 | 114 | 71 | 0.3 - 152.0 (24.3)
subtype2 | 194 | 102 | 0.3 - 180.2 (33.6)
subtype3 | 143 | 79 | 0.3 - 130.0 (28.6)

Figure S43. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.53 (ANOVA)

Table S52. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 446 | 59.8 (11.5)
subtype1 | 113 | 60.8 (11.8)
subtype2 | 192 | 59.5 (11.2)
subtype3 | 141 | 59.4 (11.8)

Figure S44. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.385 (Chi-square test)

Table S53. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 451 | 1
subtype1 | 1 | 112 | 1
subtype2 | 1 | 194 | 0
subtype3 | 0 | 145 | 0

Figure S45. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.113 (ANOVA)

Table S54. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 75.3 (13.2)
subtype1 | 11 | 81.8 (14.0)
subtype2 | 48 | 74.6 (12.2)
subtype3 | 5 | 68.0 (17.9)

Figure S46. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.476 (Fisher's exact test)

Table S55. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 451 | 3
subtype1 | 114 | 0
subtype2 | 194 | 1
subtype3 | 143 | 2

Figure S47. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.704 (Fisher's exact test)

Table S56. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 367 | 87
subtype1 | 91 | 23
subtype2 | 161 | 34
subtype3 | 115 | 30

Figure S48. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq cHierClus subtypes'

Table S57. Get Full Table Description of clustering approach #9: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 3 | 297 | 154

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0354 (logrank test)

Table S58. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 451 | 252 | 0.3 - 180.2 (30.1)
subtype1 | 3 | 2 | 4.5 - 33.7 (8.6)
subtype2 | 296 | 164 | 0.3 - 180.2 (31.0)
subtype3 | 152 | 86 | 0.3 - 130.0 (27.4)

Figure S49. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.353 (ANOVA)

Table S59. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 446 | 59.8 (11.5)
subtype1 | 3 | 69.0 (12.2)
subtype2 | 293 | 59.9 (11.4)
subtype3 | 150 | 59.4 (11.8)

Figure S50. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.809 (Chi-square test)

Table S60. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 451 | 1
subtype1 | 0 | 3 | 0
subtype2 | 2 | 294 | 1
subtype3 | 0 | 154 | 0

Figure S51. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.382 (ANOVA)

Table S61. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 75.3 (13.2)
subtype2 | 59 | 75.9 (12.7)
subtype3 | 5 | 68.0 (17.9)

Figure S52. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.284 (Fisher's exact test)

Table S62. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 451 | 3
subtype1 | 3 | 0
subtype2 | 296 | 1
subtype3 | 152 | 2

Figure S53. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.461 (Fisher's exact test)

Table S63. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 367 | 87
subtype1 | 2 | 1
subtype2 | 243 | 54
subtype3 | 122 | 32

Figure S54. Get High-res Image Clustering Approach #9: '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 = 9

- 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

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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