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 7 clinical features across 566 patients, 13 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 'TUMOR.STAGE'.

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'TUMOR.STAGE', and 'NEOADJUVANT.THERAPY'.

- 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 correlate to 'Time to Death'.

- 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.

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

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'TUMOR.STAGE'.

- 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 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 9 different clustering approaches and 7 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 | PRIMARY SITE OF DISEASE | KARNOFSKY PERFORMANCE SCORE | TUMOR STAGE | RADIATIONS RADIATION REGIMENINDICATION | NEOADJUVANT THERAPY
Statistical Tests | logrank test | ANOVA | Chi-square test | ANOVA | Chi-square test | Fisher's exact test | Fisher's exact test
mRNA CNMF subtypes | 0.00933 | 0.06 | 0.555 | 0.147 | 0.031 | 0.793 | 0.371
mRNA cHierClus subtypes | 0.00116 | 0.00679 | 0.588 | 0.241 | 0.0228 | 0.619 | 0.0475
miR CNMF subtypes | 0.00559 | 0.698 | 0.201 | 0.677 | 0.11 | 0.265 | 0.275
miR cHierClus subtypes | 0.0418 | 0.504 | 0.0908 | 0.547 | 0.0541 | 0.533 | 0.641
METHLYATION CNMF | 0.0554 | 6.41e-06 | 0.83 | 0.5 | 0.513 | 0.138 | 0.519
RNAseq CNMF subtypes | 0.186 | 0.0011 | 0.736 | 0.277 | 0.021 | 0.189 | 0.536
RNAseq cHierClus subtypes | 0.139 | 0.108 | 0.615 | 0.273 | 0.0365 | 1 | 0.725
MIRseq CNMF subtypes | 0.0121 | 0.594 | 0.0583 | 0.207 | 0.193 | 0.476 | 0.546
MIRseq cHierClus subtypes | 0.284 | 0.757 | 0.812 | 0.577 | 0.49 | 0.281 | 0.753

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 | 225 | 207 | 132

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.00933 (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 | 222 | 127 | 0.3 - 152.0 (29.1)
subtype2 | 205 | 90 | 0.4 - 180.2 (27.9)
subtype3 | 132 | 75 | 0.3 - 119.1 (26.7)

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 = 0.06 (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 | 219 | 61.0 (11.6)
subtype2 | 203 | 58.4 (11.3)
subtype3 | 131 | 59.5 (11.9)

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 = 0.555 (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 | 225 | 0
subtype2 | 1 | 205 | 1
subtype3 | 1 | 130 | 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 = 0.147 (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 | 27 | 76.3 (12.4)
subtype3 | 20 | 71.0 (12.1)

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.031 (Chi-square test)

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 16 | 26 | 433 | 85
subtype1 | 7 | 9 | 178 | 29
subtype2 | 9 | 15 | 150 | 32
subtype3 | 0 | 2 | 105 | 24

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

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

P value = 0.793 (Fisher's exact test)

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 561 | 3
subtype1 | 223 | 2
subtype2 | 206 | 1
subtype3 | 132 | 0

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.371 (Fisher's exact test)

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 456 | 108
subtype1 | 176 | 49
subtype2 | 173 | 34
subtype3 | 107 | 25

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 209 | 223 | 132

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.00116 (logrank test)

Table S10. 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 | 206 | 124 | 0.3 - 152.0 (29.6)
subtype2 | 221 | 87 | 0.5 - 116.1 (27.5)
subtype3 | 132 | 81 | 0.3 - 180.2 (27.6)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.00679 (ANOVA)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 553 | 59.7 (11.6)
subtype1 | 202 | 61.7 (11.5)
subtype2 | 221 | 58.5 (11.0)
subtype3 | 130 | 58.6 (12.4)

Figure S9. 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 = 0.588 (Chi-square test)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 560 | 2
subtype1 | 0 | 209 | 0
subtype2 | 1 | 221 | 1
subtype3 | 1 | 130 | 1

Figure S10. 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 = 0.241 (ANOVA)

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 78 | 75.6 (12.8)
subtype1 | 25 | 79.2 (13.5)
subtype2 | 33 | 73.9 (11.7)
subtype3 | 20 | 74.0 (13.1)

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0228 (Chi-square test)

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 16 | 26 | 433 | 85
subtype1 | 7 | 10 | 165 | 25
subtype2 | 9 | 15 | 162 | 36
subtype3 | 0 | 1 | 106 | 24

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.619 (Fisher's exact test)

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 561 | 3
subtype1 | 207 | 2
subtype2 | 222 | 1
subtype3 | 132 | 0

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0475 (Fisher's exact test)

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 456 | 108
subtype1 | 160 | 49
subtype2 | 191 | 32
subtype3 | 105 | 27

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S17. Get Full Table Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 159 | 163 | 240

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.00559 (logrank test)

Table S18. 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 | 158 | 86 | 0.3 - 130.0 (25.5)
subtype2 | 160 | 92 | 0.3 - 109.7 (24.7)
subtype3 | 239 | 114 | 0.3 - 180.2 (30.1)

Figure S15. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.698 (ANOVA)

Table S19. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 551 | 59.7 (11.6)
subtype1 | 155 | 59.0 (12.3)
subtype2 | 160 | 59.8 (11.8)
subtype3 | 236 | 60.1 (11.1)

Figure S16. 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 = 0.201 (Chi-square test)

Table S20. 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 | 157 | 2
subtype2 | 1 | 162 | 0
subtype3 | 1 | 239 | 0

Figure S17. 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 S21. 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 S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.11 (Chi-square test)

Table S22. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 16 | 25 | 432 | 85
subtype1 | 3 | 6 | 115 | 33
subtype2 | 7 | 5 | 133 | 17
subtype3 | 6 | 14 | 184 | 35

Figure S19. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.265 (Fisher's exact test)

Table S23. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 559 | 3
subtype1 | 159 | 0
subtype2 | 163 | 0
subtype3 | 237 | 3

Figure S20. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.275 (Fisher's exact test)

Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 454 | 108
subtype1 | 130 | 29
subtype2 | 125 | 38
subtype3 | 199 | 41

Figure S21. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S25. Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 200 | 186 | 176

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.0418 (logrank test)

Table S26. 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 | 197 | 95 | 0.3 - 180.2 (29.0)
subtype2 | 184 | 99 | 0.5 - 152.0 (29.0)
subtype3 | 176 | 98 | 0.3 - 130.0 (24.6)

Figure S22. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'AGE'

P value = 0.504 (ANOVA)

Table S27. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 551 | 59.7 (11.6)
subtype1 | 198 | 60.0 (11.8)
subtype2 | 181 | 60.2 (11.0)
subtype3 | 172 | 58.8 (12.2)

Figure S23. 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 = 0.0908 (Chi-square test)

Table S28. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 558 | 2
subtype1 | 2 | 198 | 0
subtype2 | 0 | 186 | 0
subtype3 | 0 | 174 | 2

Figure S24. 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 = 0.547 (ANOVA)

Table S29. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 78 | 75.6 (12.8)
subtype1 | 31 | 76.1 (13.1)
subtype2 | 20 | 73.0 (13.4)
subtype3 | 27 | 77.0 (12.0)

Figure S25. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0541 (Chi-square test)

Table S30. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 16 | 25 | 432 | 85
subtype1 | 4 | 9 | 152 | 33
subtype2 | 9 | 10 | 149 | 17
subtype3 | 3 | 6 | 131 | 35

Figure S26. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.533 (Fisher's exact test)

Table S31. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 559 | 3
subtype1 | 199 | 1
subtype2 | 184 | 2
subtype3 | 176 | 0

Figure S27. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.641 (Fisher's exact test)

Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 454 | 108
subtype1 | 164 | 36
subtype2 | 146 | 40
subtype3 | 144 | 32

Figure S28. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'METHLYATION CNMF'

Table S33. Get Full Table Description of clustering approach #5: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 226 | 125 | 37 | 161

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0554 (logrank test)

Table S34. 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 | 222 | 118 | 0.4 - 125.7 (26.6)
subtype2 | 123 | 63 | 0.3 - 180.2 (31.4)
subtype3 | 37 | 19 | 0.3 - 130.0 (30.6)
subtype4 | 161 | 90 | 0.4 - 152.0 (29.1)

Figure S29. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 6.41e-06 (ANOVA)

Table S35. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 539 | 59.7 (11.7)
subtype1 | 219 | 61.1 (12.7)
subtype2 | 124 | 55.2 (10.3)
subtype3 | 37 | 58.9 (12.0)
subtype4 | 159 | 61.6 (10.1)

Figure S30. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.83 (Chi-square test)

Table S36. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 545 | 2
subtype1 | 0 | 225 | 1
subtype2 | 1 | 124 | 0
subtype3 | 0 | 37 | 0
subtype4 | 1 | 159 | 1

Figure S31. 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 = 0.5 (ANOVA)

Table S37. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 78 | 75.6 (12.8)
subtype1 | 34 | 75.3 (14.0)
subtype2 | 15 | 80.0 (10.7)
subtype3 | 9 | 73.3 (14.1)
subtype4 | 20 | 74.0 (11.4)

Figure S32. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.513 (Chi-square test)

Table S38. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 16 | 24 | 421 | 83
subtype1 | 7 | 9 | 171 | 36
subtype2 | 6 | 3 | 94 | 21
subtype3 | 0 | 1 | 30 | 6
subtype4 | 3 | 11 | 126 | 20

Figure S33. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.138 (Fisher's exact test)

Table S39. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 546 | 3
subtype1 | 224 | 2
subtype2 | 125 | 0
subtype3 | 36 | 1
subtype4 | 161 | 0

Figure S34. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.519 (Fisher's exact test)

Table S40. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 441 | 108
subtype1 | 179 | 47
subtype2 | 104 | 21
subtype3 | 27 | 10
subtype4 | 131 | 30

Figure S35. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RNAseq CNMF subtypes'

Table S41. Get Full Table Description of clustering approach #6: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 81 | 101 | 115

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.186 (logrank test)

Table S42. 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 | 80 | 42 | 1.0 - 116.1 (31.0)
subtype2 | 100 | 63 | 0.3 - 152.0 (26.4)
subtype3 | 115 | 64 | 0.4 - 180.2 (27.5)

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0011 (ANOVA)

Table S43. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 289 | 59.2 (11.0)
subtype1 | 79 | 55.5 (10.6)
subtype2 | 98 | 61.4 (10.7)
subtype3 | 112 | 59.9 (11.1)

Figure S37. 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 = 0.736 (Fisher's exact test)

Table S44. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY
--- | --- | ---
ALL | 2 | 295
subtype1 | 1 | 80
subtype2 | 0 | 101
subtype3 | 1 | 114

Figure S38. 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 S45. 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 S39. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.021 (Chi-square test)

Table S46. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients | II | III | IV
--- | --- | --- | ---
ALL | 18 | 241 | 36
subtype1 | 9 | 58 | 14
subtype2 | 7 | 85 | 8
subtype3 | 2 | 98 | 14

Figure S40. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.189 (Fisher's exact test)

Table S47. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 295 | 2
subtype1 | 81 | 0
subtype2 | 99 | 2
subtype3 | 115 | 0

Figure S41. Get High-res Image Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.536 (Fisher's exact test)

Table S48. Clustering Approach #6: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 241 | 56
subtype1 | 63 | 18
subtype2 | 85 | 16
subtype3 | 93 | 22

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

Clustering Approach #7: 'RNAseq cHierClus subtypes'

Table S49. Get Full Table Description of clustering approach #7: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 35 | 115 | 147

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.139 (logrank test)

Table S50. 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 | 35 | 24 | 0.3 - 89.3 (28.7)
subtype2 | 114 | 71 | 0.5 - 152.0 (27.4)
subtype3 | 146 | 74 | 0.4 - 180.2 (29.0)

Figure S43. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.108 (ANOVA)

Table S51. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 289 | 59.2 (11.0)
subtype1 | 34 | 56.3 (10.6)
subtype2 | 112 | 58.6 (10.6)
subtype3 | 143 | 60.4 (11.4)

Figure S44. 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 = 0.615 (Fisher's exact test)

Table S52. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY
--- | --- | ---
ALL | 2 | 295
subtype1 | 0 | 35
subtype2 | 0 | 115
subtype3 | 2 | 145

Figure S45. 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 = 0.273 (ANOVA)

Table S53. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 16 | 76.2 (13.1)
subtype2 | 3 | 80.0 (0.0)
subtype3 | 13 | 75.4 (14.5)

Figure S46. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0365 (Chi-square test)

Table S54. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients | II | III | IV
--- | --- | --- | ---
ALL | 18 | 241 | 36
subtype1 | 2 | 23 | 10
subtype2 | 8 | 95 | 11
subtype3 | 8 | 123 | 15

Figure S47. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 1 (Fisher's exact test)

Table S55. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 295 | 2
subtype1 | 35 | 0
subtype2 | 114 | 1
subtype3 | 146 | 1

Figure S48. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.725 (Fisher's exact test)

Table S56. Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 241 | 56
subtype1 | 27 | 8
subtype2 | 95 | 20
subtype3 | 119 | 28

Figure S49. Get High-res Image Clustering Approach #7: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'MIRseq CNMF subtypes'

Table S57. Get Full Table Description of clustering approach #8: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 113 | 195 | 146

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0121 (logrank test)

Table S58. 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 | 113 | 74 | 0.3 - 152.0 (27.0)
subtype2 | 194 | 101 | 0.3 - 180.2 (32.5)
subtype3 | 144 | 77 | 0.3 - 130.0 (28.7)

Figure S50. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.594 (ANOVA)

Table S59. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 446 | 59.8 (11.5)
subtype1 | 112 | 60.8 (11.7)
subtype2 | 192 | 59.5 (11.3)
subtype3 | 142 | 59.5 (11.6)

Figure S51. 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 = 0.0583 (Chi-square test)

Table S60. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients | OMENTUM | OVARY | PERITONEUM (OVARY)
--- | --- | --- | ---
ALL | 2 | 451 | 1
subtype1 | 2 | 110 | 1
subtype2 | 0 | 195 | 0
subtype3 | 0 | 146 | 0

Figure S52. 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 = 0.207 (ANOVA)

Table S61. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 75.3 (13.2)
subtype1 | 10 | 82.0 (14.8)
subtype2 | 46 | 74.3 (12.4)
subtype3 | 8 | 72.5 (14.9)

Figure S53. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.193 (Chi-square test)

Table S62. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients | II | III | IV
--- | --- | --- | ---
ALL | 24 | 352 | 74
subtype1 | 4 | 91 | 17
subtype2 | 8 | 147 | 38
subtype3 | 12 | 114 | 19

Figure S54. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.476 (Fisher's exact test)

Table S63. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 451 | 3
subtype1 | 113 | 0
subtype2 | 194 | 1
subtype3 | 144 | 2

Figure S55. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.546 (Fisher's exact test)

Table S64. Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 367 | 87
subtype1 | 89 | 24
subtype2 | 162 | 33
subtype3 | 116 | 30

Figure S56. Get High-res Image Clustering Approach #8: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq cHierClus subtypes'

Table S65. Get Full Table Description of clustering approach #9: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 3 | 153 | 298

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.284 (logrank test)

Table S66. 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 | 3 | 4.5 - 60.6 (33.7)
subtype2 | 151 | 82 | 0.3 - 130.0 (28.6)
subtype3 | 297 | 167 | 0.3 - 180.2 (31.0)

Figure S57. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.757 (ANOVA)

Table S67. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 446 | 59.8 (11.5)
subtype1 | 3 | 62.7 (1.5)
subtype2 | 149 | 59.3 (11.9)
subtype3 | 294 | 60.0 (11.4)

Figure S58. 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 = 0.812 (Chi-square test)

Table S68. 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 | 0 | 153 | 0
subtype3 | 2 | 295 | 1

Figure S59. 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 = 0.577 (ANOVA)

Table S69. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 75.3 (13.2)
subtype2 | 8 | 72.5 (14.9)
subtype3 | 56 | 75.7 (13.1)

Figure S60. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.49 (Chi-square test)

Table S70. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients | II | III | IV
--- | --- | --- | ---
ALL | 24 | 352 | 74
subtype1 | 0 | 3 | 0
subtype2 | 11 | 120 | 21
subtype3 | 13 | 229 | 53

Figure S61. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.281 (Fisher's exact test)

Table S71. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 451 | 3
subtype1 | 3 | 0
subtype2 | 151 | 2
subtype3 | 297 | 1

Figure S62. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.753 (Fisher's exact test)

Table S72. Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 367 | 87
subtype1 | 3 | 0
subtype2 | 121 | 32
subtype3 | 243 | 55

Figure S63. Get High-res Image Clustering Approach #9: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: '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 = 7

- 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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