Uterine Corpus Endometrioid Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 7 different clustering approaches and 5 clinical features across 430 patients, 28 significant findings detected with P value < 0.05.

- CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE' and 'NEOADJUVANT.THERAPY'.

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

- CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'NEOADJUVANT.THERAPY'.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 5 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 28 significant findings detected.

Clinical Features | Time to Death | AGE | HISTOLOGICAL TYPE | RADIATIONS RADIATION REGIMENINDICATION | NEOADJUVANT THERAPY
Statistical Tests | logrank test | ANOVA | Chi-square test | Fisher's exact test | Fisher's exact test
mRNA CNMF subtypes | 0.53 | 0.0107 | 0.000456 | 0.0317 | 0.0124
mRNA cHierClus subtypes | 0.73 | 0.0598 | 0.00134 | 0.0666 | 0.0114
METHLYATION CNMF | 0.00798 | 0.0123 | 4.28e-17 | 0.0786 | 0.0132
RNAseq CNMF subtypes | 0.00873 | 0.00104 | 8.73e-18 | 0.0148 | 0.000242
RNAseq cHierClus subtypes | 0.00985 | 0.00202 | 8.69e-18 | 0.0325 | 1.45e-05
MIRseq CNMF subtypes | 0.00602 | 0.000193 | 5.4e-20 | 0.0958 | 0.00481
MIRseq cHierClus subtypes | 0.0652 | 3.03e-05 | 1.14e-13 | 0.0303 | 0.0281

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 13 | 19 | 14 | 8

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.53 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 54 | 7 | 6.0 - 133.2 (35.4)
subtype1 | 13 | 2 | 9.0 - 133.2 (39.0)
subtype2 | 19 | 3 | 6.0 - 113.2 (37.7)
subtype3 | 14 | 1 | 8.6 - 89.3 (30.9)
subtype4 | 8 | 1 | 6.4 - 65.5 (22.9)

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 = 0.0107 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 54 | 62.9 (11.8)
subtype1 | 13 | 65.1 (12.0)
subtype2 | 19 | 68.4 (9.1)
subtype3 | 14 | 58.2 (11.0)
subtype4 | 8 | 54.8 (12.9)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000456 (Chi-square test)

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | MIXED SEROUS AND ENDOMETRIOID | SEROUS ENDOMETRIAL ADENOCARCINOMA
--- | --- | --- | ---
ALL | 41 | 1 | 12
subtype1 | 12 | 0 | 1
subtype2 | 8 | 0 | 11
subtype3 | 13 | 1 | 0
subtype4 | 8 | 0 | 0

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0317 (Fisher's exact test)

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 25 | 29
subtype1 | 7 | 6
subtype2 | 13 | 6
subtype3 | 3 | 11
subtype4 | 2 | 6

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0124 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 18 | 36
subtype1 | 6 | 7
subtype2 | 10 | 9
subtype3 | 2 | 12
subtype4 | 0 | 8

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S7. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 20 | 15 | 19

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.73 (logrank test)

Table S8. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 54 | 7 | 6.0 - 133.2 (35.4)
subtype1 | 20 | 2 | 6.4 - 89.3 (29.8)
subtype2 | 15 | 2 | 9.0 - 133.2 (39.0)
subtype3 | 19 | 3 | 6.0 - 113.2 (37.7)

Figure S6. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0598 (ANOVA)

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 54 | 62.9 (11.8)
subtype1 | 20 | 58.2 (13.3)
subtype2 | 15 | 64.0 (10.6)
subtype3 | 19 | 67.1 (9.9)

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00134 (Chi-square test)

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | MIXED SEROUS AND ENDOMETRIOID | SEROUS ENDOMETRIAL ADENOCARCINOMA
--- | --- | --- | ---
ALL | 41 | 1 | 12
subtype1 | 19 | 1 | 0
subtype2 | 13 | 0 | 2
subtype3 | 9 | 0 | 10

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0666 (Fisher's exact test)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 25 | 29
subtype1 | 5 | 15
subtype2 | 9 | 6
subtype3 | 11 | 8

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0114 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 18 | 36
subtype1 | 2 | 18
subtype2 | 6 | 9
subtype3 | 10 | 9

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S13. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 119 | 71 | 123

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00798 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 309 | 30 | 0.0 - 187.1 (12.8)
subtype1 | 117 | 18 | 0.0 - 187.1 (11.5)
subtype2 | 71 | 4 | 0.0 - 92.0 (17.5)
subtype3 | 121 | 8 | 0.1 - 173.6 (13.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0123 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 312 | 63.5 (11.1)
subtype1 | 118 | 65.8 (10.0)
subtype2 | 71 | 63.0 (12.9)
subtype3 | 123 | 61.6 (10.8)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 4.28e-17 (Chi-square test)

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

nPatients | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) | MIXED SEROUS AND ENDOMETRIOID | SEROUS ENDOMETRIAL ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | ---
ALL | 229 | 6 | 1 | 3 | 16 | 58
subtype1 | 54 | 1 | 0 | 1 | 13 | 50
subtype2 | 58 | 2 | 1 | 0 | 2 | 8
subtype3 | 117 | 3 | 0 | 2 | 1 | 0

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

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

P value = 0.0786 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 79 | 234
subtype1 | 32 | 87
subtype2 | 11 | 60
subtype3 | 36 | 87

Figure S14. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.0132 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 70 | 243
subtype1 | 37 | 82
subtype2 | 10 | 61
subtype3 | 23 | 100

Figure S15. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S19. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 96 | 83 | 87

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00873 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 266 | 20 | 0.0 - 173.6 (20.2)
subtype1 | 96 | 13 | 0.5 - 133.2 (17.8)
subtype2 | 83 | 2 | 0.6 - 101.1 (21.4)
subtype3 | 87 | 5 | 0.0 - 173.6 (22.6)

Figure S16. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00104 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 266 | 63.1 (10.8)
subtype1 | 96 | 66.2 (10.4)
subtype2 | 83 | 62.4 (10.6)
subtype3 | 87 | 60.4 (10.7)

Figure S17. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.73e-18 (Chi-square test)

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

nPatients | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) | MIXED SEROUS AND ENDOMETRIOID | SEROUS ENDOMETRIAL ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | ---
ALL | 205 | 3 | 4 | 1 | 5 | 7 | 41
subtype1 | 45 | 0 | 0 | 0 | 4 | 6 | 41
subtype2 | 78 | 2 | 2 | 0 | 0 | 1 | 0
subtype3 | 82 | 1 | 2 | 1 | 1 | 0 | 0

Figure S18. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0148 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 99 | 167
subtype1 | 44 | 52
subtype2 | 21 | 62
subtype3 | 34 | 53

Figure S19. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.000242 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 83 | 183
subtype1 | 45 | 51
subtype2 | 19 | 64
subtype3 | 19 | 68

Figure S20. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S25. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 96 | 82 | 88

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00985 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 266 | 20 | 0.0 - 173.6 (20.2)
subtype1 | 96 | 12 | 0.6 - 133.2 (17.7)
subtype2 | 82 | 1 | 0.6 - 101.1 (22.7)
subtype3 | 88 | 7 | 0.0 - 173.6 (22.5)

Figure S21. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00202 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 266 | 63.1 (10.8)
subtype1 | 96 | 66.0 (10.4)
subtype2 | 82 | 62.5 (10.4)
subtype3 | 88 | 60.5 (11.0)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.69e-18 (Chi-square test)

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

nPatients | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) | MIXED SEROUS AND ENDOMETRIOID | SEROUS ENDOMETRIAL ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | ---
ALL | 205 | 3 | 4 | 1 | 5 | 7 | 41
subtype1 | 45 | 0 | 0 | 0 | 4 | 6 | 41
subtype2 | 77 | 2 | 2 | 0 | 0 | 1 | 0
subtype3 | 83 | 1 | 2 | 1 | 1 | 0 | 0

Figure S23. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0325 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 99 | 167
subtype1 | 44 | 52
subtype2 | 22 | 60
subtype3 | 33 | 55

Figure S24. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1.45e-05 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 83 | 183
subtype1 | 47 | 49
subtype2 | 15 | 67
subtype3 | 21 | 67

Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 147 | 124 | 101

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00602 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 370 | 30 | 0.0 - 187.1 (17.4)
subtype1 | 146 | 18 | 0.0 - 187.1 (15.5)
subtype2 | 123 | 3 | 0.1 - 101.1 (17.3)
subtype3 | 101 | 9 | 0.5 - 173.6 (23.3)

Figure S26. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.000193 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 372 | 63.2 (11.2)
subtype1 | 147 | 65.9 (10.6)
subtype2 | 124 | 62.5 (11.6)
subtype3 | 101 | 60.1 (10.8)

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

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 5.4e-20 (Chi-square test)

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

nPatients | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) | MIXED SEROUS AND ENDOMETRIOID | SEROUS ENDOMETRIAL ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | ---
ALL | 284 | 3 | 9 | 2 | 7 | 13 | 54
subtype1 | 77 | 1 | 1 | 0 | 4 | 11 | 53
subtype2 | 117 | 2 | 3 | 0 | 1 | 1 | 0
subtype3 | 90 | 0 | 5 | 2 | 2 | 1 | 1

Figure S28. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0958 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 123 | 249
subtype1 | 58 | 89
subtype2 | 34 | 90
subtype3 | 31 | 70

Figure S29. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00481 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 96 | 276
subtype1 | 51 | 96
subtype2 | 22 | 102
subtype3 | 23 | 78

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

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S37. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 114 | 169 | 89

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0652 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 370 | 30 | 0.0 - 187.1 (17.4)
subtype1 | 113 | 4 | 0.1 - 101.1 (17.3)
subtype2 | 168 | 18 | 0.0 - 187.1 (17.8)
subtype3 | 89 | 8 | 0.6 - 173.6 (16.8)

Figure S31. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 3.03e-05 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 372 | 63.2 (11.2)
subtype1 | 114 | 63.8 (10.5)
subtype2 | 169 | 65.2 (10.8)
subtype3 | 89 | 58.6 (11.7)

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

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.14e-13 (Chi-square test)

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

nPatients | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) | ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) | MIXED SEROUS AND ENDOMETRIOID | SEROUS ENDOMETRIAL ADENOCARCINOMA
--- | --- | --- | --- | --- | --- | --- | ---
ALL | 284 | 3 | 9 | 2 | 7 | 13 | 54
subtype1 | 103 | 2 | 4 | 0 | 1 | 1 | 3
subtype2 | 100 | 0 | 2 | 0 | 4 | 12 | 51
subtype3 | 81 | 1 | 3 | 2 | 2 | 0 | 0

Figure S33. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0303 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 123 | 249
subtype1 | 31 | 83
subtype2 | 68 | 101
subtype3 | 24 | 65

Figure S34. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0281 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 96 | 276
subtype1 | 23 | 91
subtype2 | 55 | 114
subtype3 | 18 | 71

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

Methods & Data
Input

- Cluster data file = UCEC.mergedcluster.txt

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

- Number of patients = 430

- Number of clustering approaches = 7

- Number of selected clinical features = 5

- Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
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