Thyroid Adenocarcinoma: 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 8 different clustering approaches and 6 clinical features across 196 patients, 21 significant findings detected with P value < 0.05.

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

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

- CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE', 'GENDER', and 'HISTOLOGICAL.TYPE'.

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

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

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 6 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 21 significant findings detected.

Clinical Features | Time to Death | AGE | GENDER | HISTOLOGICAL TYPE | RADIATIONS RADIATION REGIMENINDICATION | NEOADJUVANT THERAPY
Statistical Tests | logrank test | ANOVA | Fisher's exact test | Chi-square test | Fisher's exact test | Fisher's exact test
CN CNMF | 0.0143 | 0.243 | 0.967 | 0.058 | 1 | 1
METHLYATION CNMF | 0.0253 | 0.00151 | 0.939 | 1.32e-10 | 0.0878 | 1
RPPA CNMF subtypes | 0.414 | 0.00027 | 0.882 | 3.38e-06 | 0.0122 | 0.78
RPPA cHierClus subtypes | 0.0455 | 0.000772 | 0.374 | 1.37e-11 | 0.383 | 1
RNAseq CNMF subtypes | 0.157 | 0.0241 | 0.136 | 8.14e-09 | 0.0757 | 1
RNAseq cHierClus subtypes | 0.157 | 0.00142 | 0.001 | 7.88e-11 | 0.194 | 0.752
MIRseq CNMF subtypes | 0.0143 | 0.0935 | 0.738 | 1.25e-17 | 0.101 | 0.614
MIRseq cHierClus subtypes | 0.0143 | 0.0365 | 0.614 | 1.22e-17 | 0.0352 | 0.781

Clustering Approach #1: 'CN CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 27 | 127 | 28

'CN CNMF' versus 'Time to Death'

P value = 0.0143 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 182 | 1 | 0.1 - 66.1 (8.3)
subtype1 | 27 | 1 | 0.4 - 65.9 (9.0)
subtype2 | 127 | 0 | 0.1 - 66.1 (8.4)
subtype3 | 28 | 0 | 0.3 - 65.9 (7.3)

Figure S1. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.243 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 182 | 46.5 (15.7)
subtype1 | 27 | 51.0 (15.9)
subtype2 | 127 | 45.4 (15.8)
subtype3 | 28 | 47.3 (14.4)

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

'CN CNMF' versus 'GENDER'

P value = 0.967 (Fisher's exact test)

Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 131 | 51
subtype1 | 19 | 8
subtype2 | 92 | 35
subtype3 | 20 | 8

Figure S3. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.058 (Chi-square test)

Table S5. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients | OTHER | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 8 | 99 | 56 | 19
subtype1 | 4 | 12 | 10 | 1
subtype2 | 3 | 73 | 35 | 16
subtype3 | 1 | 14 | 11 | 2

Figure S4. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 14 | 168
subtype1 | 2 | 25
subtype2 | 10 | 117
subtype3 | 2 | 26

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

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 179
subtype1 | 0 | 27
subtype2 | 3 | 124
subtype3 | 0 | 28

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S8. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 44 | 24 | 92

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0253 (logrank test)

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 160 | 1 | 0.1 - 66.1 (8.9)
subtype1 | 44 | 1 | 0.2 - 65.9 (7.8)
subtype2 | 24 | 0 | 0.1 - 66.1 (6.4)
subtype3 | 92 | 0 | 0.2 - 66.1 (10.4)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00151 (ANOVA)

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 160 | 46.8 (15.8)
subtype1 | 44 | 53.8 (16.7)
subtype2 | 24 | 41.7 (13.9)
subtype3 | 92 | 44.8 (14.9)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.939 (Fisher's exact test)

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 116 | 44
subtype1 | 31 | 13
subtype2 | 18 | 6
subtype3 | 67 | 25

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.32e-10 (Chi-square test)

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients | OTHER | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 5 | 92 | 44 | 19
subtype1 | 4 | 9 | 29 | 2
subtype2 | 0 | 16 | 5 | 3
subtype3 | 1 | 67 | 10 | 14

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0878 (Fisher's exact test)

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 14 | 146
subtype1 | 1 | 43
subtype2 | 1 | 23
subtype3 | 12 | 80

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 157
subtype1 | 1 | 43
subtype2 | 0 | 24
subtype3 | 2 | 90

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

Clustering Approach #3: 'RPPA CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 41 | 55 | 56

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.414 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 152 | 1 | 0.1 - 66.1 (8.2)
subtype1 | 41 | 0 | 0.3 - 50.5 (8.1)
subtype2 | 55 | 0 | 0.2 - 65.9 (9.3)
subtype3 | 56 | 1 | 0.1 - 66.1 (7.4)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.00027 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 152 | 46.7 (16.2)
subtype1 | 41 | 50.9 (13.8)
subtype2 | 55 | 50.5 (16.2)
subtype3 | 56 | 39.9 (15.8)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.882 (Fisher's exact test)

Table S18. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 104 | 48
subtype1 | 27 | 14
subtype2 | 39 | 16
subtype3 | 38 | 18

Figure S15. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.38e-06 (Chi-square test)

Table S19. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients | OTHER | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 7 | 81 | 51 | 13
subtype1 | 2 | 10 | 27 | 2
subtype2 | 4 | 31 | 11 | 9
subtype3 | 1 | 40 | 13 | 2

Figure S16. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0122 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 13 | 139
subtype1 | 0 | 41
subtype2 | 9 | 46
subtype3 | 4 | 52

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

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.78 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 149
subtype1 | 0 | 41
subtype2 | 1 | 54
subtype3 | 2 | 54

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

Clustering Approach #4: 'RPPA cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 23 | 66 | 63

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0455 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 152 | 1 | 0.1 - 66.1 (8.2)
subtype1 | 23 | 0 | 1.1 - 65.9 (8.1)
subtype2 | 66 | 0 | 0.1 - 66.1 (9.2)
subtype3 | 63 | 1 | 0.3 - 65.9 (8.0)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.000772 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 152 | 46.7 (16.2)
subtype1 | 23 | 53.6 (16.7)
subtype2 | 66 | 41.3 (15.0)
subtype3 | 63 | 49.9 (15.7)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.374 (Fisher's exact test)

Table S25. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 104 | 48
subtype1 | 17 | 6
subtype2 | 48 | 18
subtype3 | 39 | 24

Figure S21. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.37e-11 (Chi-square test)

Table S26. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients | OTHER | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 7 | 81 | 51 | 13
subtype1 | 5 | 7 | 10 | 1
subtype2 | 1 | 53 | 4 | 8
subtype3 | 1 | 21 | 37 | 4

Figure S22. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.383 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 13 | 139
subtype1 | 2 | 21
subtype2 | 8 | 58
subtype3 | 3 | 60

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

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 149
subtype1 | 0 | 23
subtype2 | 2 | 64
subtype3 | 1 | 62

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

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 35 | 27 | 42 | 13

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.157 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 117 | 1 | 0.1 - 65.9 (8.8)
subtype1 | 35 | 1 | 0.3 - 65.9 (7.6)
subtype2 | 27 | 0 | 0.5 - 65.7 (9.6)
subtype3 | 42 | 0 | 1.0 - 65.9 (10.2)
subtype4 | 13 | 0 | 0.1 - 65.9 (9.2)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0241 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 117 | 47.9 (16.3)
subtype1 | 35 | 54.5 (17.2)
subtype2 | 27 | 42.9 (13.5)
subtype3 | 42 | 46.6 (16.2)
subtype4 | 13 | 44.4 (15.4)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.136 (Fisher's exact test)

Table S32. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 82 | 35
subtype1 | 24 | 11
subtype2 | 23 | 4
subtype3 | 25 | 17
subtype4 | 10 | 3

Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.14e-09 (Chi-square test)

Table S33. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients | OTHER | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 3 | 65 | 33 | 16
subtype1 | 3 | 7 | 24 | 1
subtype2 | 0 | 21 | 4 | 2
subtype3 | 0 | 29 | 3 | 10
subtype4 | 0 | 8 | 2 | 3

Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0757 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 11 | 106
subtype1 | 1 | 34
subtype2 | 1 | 26
subtype3 | 8 | 34
subtype4 | 1 | 12

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 114
subtype1 | 1 | 34
subtype2 | 1 | 26
subtype3 | 1 | 41
subtype4 | 0 | 13

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 32 | 64 | 21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.157 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 117 | 1 | 0.1 - 65.9 (8.8)
subtype1 | 32 | 1 | 0.3 - 65.9 (7.7)
subtype2 | 64 | 0 | 0.1 - 65.9 (9.4)
subtype3 | 21 | 0 | 0.5 - 65.7 (9.0)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00142 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 117 | 47.9 (16.3)
subtype1 | 32 | 56.4 (16.0)
subtype2 | 64 | 45.3 (15.7)
subtype3 | 21 | 42.5 (14.3)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.001 (Fisher's exact test)

Table S39. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 82 | 35
subtype1 | 21 | 11
subtype2 | 40 | 24
subtype3 | 21 | 0

Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 7.88e-11 (Chi-square test)

Table S40. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients | OTHER | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 3 | 65 | 33 | 16
subtype1 | 3 | 5 | 23 | 1
subtype2 | 0 | 45 | 5 | 14
subtype3 | 0 | 15 | 5 | 1

Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.194 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 11 | 106
subtype1 | 1 | 31
subtype2 | 9 | 55
subtype3 | 1 | 20

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

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.752 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 114
subtype1 | 1 | 31
subtype2 | 1 | 63
subtype3 | 1 | 20

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

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S43. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 64 | 82 | 49

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0143 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 195 | 1 | 0.1 - 66.1 (8.1)
subtype1 | 64 | 1 | 0.3 - 65.9 (7.2)
subtype2 | 82 | 0 | 0.2 - 66.1 (10.4)
subtype3 | 49 | 0 | 0.1 - 66.1 (8.0)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0935 (ANOVA)

Table S45. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 195 | 46.4 (16.0)
subtype1 | 64 | 49.3 (17.3)
subtype2 | 82 | 43.6 (14.9)
subtype3 | 49 | 47.2 (15.5)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.738 (Fisher's exact test)

Table S46. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 141 | 54
subtype1 | 44 | 20
subtype2 | 61 | 21
subtype3 | 36 | 13

Figure S39. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.25e-17 (Chi-square test)

Table S47. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients | OTHER | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 8 | 106 | 60 | 21
subtype1 | 6 | 12 | 45 | 1
subtype2 | 2 | 62 | 10 | 8
subtype3 | 0 | 32 | 5 | 12

Figure S40. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.101 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 14 | 181
subtype1 | 2 | 62
subtype2 | 10 | 72
subtype3 | 2 | 47

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

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.614 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 192
subtype1 | 2 | 62
subtype2 | 1 | 81
subtype3 | 0 | 49

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

Clustering Approach #8: 'MIRseq cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 57 | 60 | 78

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0143 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 195 | 1 | 0.1 - 66.1 (8.1)
subtype1 | 57 | 0 | 0.1 - 66.1 (8.0)
subtype2 | 60 | 1 | 0.3 - 65.9 (7.2)
subtype3 | 78 | 0 | 0.2 - 66.1 (10.1)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0365 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 195 | 46.4 (16.0)
subtype1 | 57 | 47.5 (16.2)
subtype2 | 60 | 49.8 (16.8)
subtype3 | 78 | 42.9 (14.7)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.614 (Fisher's exact test)

Table S53. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 141 | 54
subtype1 | 44 | 13
subtype2 | 42 | 18
subtype3 | 55 | 23

Figure S45. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.22e-17 (Chi-square test)

Table S54. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients | OTHER | THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL | THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) | THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
--- | --- | --- | --- | ---
ALL | 8 | 106 | 60 | 21
subtype1 | 0 | 36 | 6 | 15
subtype2 | 6 | 11 | 42 | 1
subtype3 | 2 | 59 | 12 | 5

Figure S46. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0352 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 14 | 181
subtype1 | 3 | 54
subtype2 | 1 | 59
subtype3 | 10 | 68

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

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.781 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 192
subtype1 | 0 | 57
subtype2 | 1 | 59
subtype3 | 2 | 76

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

Methods & Data
Input

- Cluster data file = THCA.mergedcluster.txt

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

- Number of patients = 196

- Number of clustering approaches = 8

- 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

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

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

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] 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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
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