This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 8 different clustering approaches and 6 clinical features across 218 patients, 18 significant findings detected with P value < 0.05.
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3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'AGE'.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.
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CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
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Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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, 18 significant findings detected.
Clinical Features |
Time to Death |
AGE | GENDER |
HISTOLOGICAL TYPE |
RADIATIONS RADIATION REGIMENINDICATION |
RADIATIONEXPOSURE |
Statistical Tests | logrank test | ANOVA | Fisher's exact test | Chi-square test | Fisher's exact test | Fisher's exact test |
CN CNMF | 100 | 0.0151 | 0.773 | 0.15 | 0.51 | 0.435 |
METHLYATION CNMF | 100 | 0.027 | 0.849 | 1.8e-15 | 0.06 | 1 |
RPPA CNMF subtypes | 100 | 0.000166 | 0.906 | 2.64e-06 | 0.00958 | 0.791 |
RPPA cHierClus subtypes | 100 | 0.001 | 0.39 | 1.74e-11 | 0.331 | 0.0769 |
RNAseq CNMF subtypes | 100 | 0.0108 | 0.734 | 8.86e-17 | 0.00383 | 0.664 |
RNAseq cHierClus subtypes | 100 | 0.0163 | 0.453 | 6.92e-16 | 0.0217 | 1 |
MIRseq CNMF subtypes | 100 | 0.0556 | 0.335 | 6.15e-18 | 0.468 | 1 |
MIRseq cHierClus subtypes | 100 | 0.00403 | 0.365 | 6.25e-20 | 0.0388 | 0.834 |
Table S1. Get Full Table Description of clustering approach #1: 'CN CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 19 | 159 | 36 |
P value = 100 (logrank test)
Table S2. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 214 | 1 | 0.0 - 66.1 (8.1) |
subtype1 | 19 | 1 | 0.4 - 65.9 (12.3) |
subtype2 | 159 | 0 | 0.2 - 66.1 (8.1) |
subtype3 | 36 | 0 | 0.0 - 65.9 (6.9) |
Figure S1. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

P value = 0.0151 (ANOVA)
Table S3. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 214 | 46.5 (15.9) |
subtype1 | 19 | 56.5 (13.5) |
subtype2 | 159 | 45.6 (16.4) |
subtype3 | 36 | 45.2 (12.8) |
Figure S2. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

P value = 0.773 (Fisher's exact test)
Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 160 | 54 |
subtype1 | 13 | 6 |
subtype2 | 119 | 40 |
subtype3 | 28 | 8 |
Figure S3. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

P value = 0.15 (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 | 123 | 61 | 22 |
subtype1 | 2 | 8 | 8 | 1 |
subtype2 | 4 | 98 | 39 | 18 |
subtype3 | 2 | 17 | 14 | 3 |
Figure S4. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

P value = 0.51 (Fisher's exact test)
Table S6. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 200 |
subtype1 | 2 | 17 |
subtype2 | 11 | 148 |
subtype3 | 1 | 35 |
Figure S5. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.435 (Fisher's exact test)
Table S7. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 177 | 9 |
subtype1 | 17 | 1 |
subtype2 | 127 | 8 |
subtype3 | 33 | 0 |
Figure S6. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

Table S8. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 72 | 33 | 113 |
P value = 100 (logrank test)
Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 218 | 1 | 0.0 - 66.1 (8.0) |
subtype1 | 72 | 1 | 0.0 - 66.1 (7.0) |
subtype2 | 33 | 0 | 0.1 - 66.1 (6.0) |
subtype3 | 113 | 0 | 0.2 - 66.1 (10.0) |
Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

P value = 0.027 (ANOVA)
Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 218 | 46.5 (15.9) |
subtype1 | 72 | 50.5 (15.9) |
subtype2 | 33 | 43.5 (14.6) |
subtype3 | 113 | 44.8 (15.9) |
Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

P value = 0.849 (Fisher's exact test)
Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 162 | 56 |
subtype1 | 53 | 19 |
subtype2 | 26 | 7 |
subtype3 | 83 | 30 |
Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

P value = 1.8e-15 (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 | 8 | 124 | 64 | 22 |
subtype1 | 6 | 16 | 47 | 3 |
subtype2 | 1 | 22 | 6 | 4 |
subtype3 | 1 | 86 | 11 | 15 |
Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

P value = 0.06 (Fisher's exact test)
Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 204 |
subtype1 | 1 | 71 |
subtype2 | 2 | 31 |
subtype3 | 11 | 102 |
Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (Fisher's exact test)
Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 180 | 9 |
subtype1 | 57 | 3 |
subtype2 | 28 | 1 |
subtype3 | 95 | 5 |
Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

Table S15. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 43 | 55 | 57 |
P value = 100 (logrank test)
Table S16. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 155 | 1 | 0.1 - 66.1 (8.2) |
subtype1 | 43 | 0 | 0.3 - 50.5 (8.1) |
subtype2 | 55 | 0 | 0.2 - 65.9 (9.3) |
subtype3 | 57 | 1 | 0.1 - 66.1 (7.7) |
Figure S13. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.000166 (ANOVA)
Table S17. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 155 | 46.6 (16.2) |
subtype1 | 43 | 50.8 (13.7) |
subtype2 | 55 | 50.5 (16.2) |
subtype3 | 57 | 39.6 (15.7) |
Figure S14. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.906 (Fisher's exact test)
Table S18. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 106 | 49 |
subtype1 | 29 | 14 |
subtype2 | 39 | 16 |
subtype3 | 38 | 19 |
Figure S15. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

P value = 2.64e-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 | 83 | 52 | 13 |
subtype1 | 2 | 11 | 28 | 2 |
subtype2 | 4 | 31 | 11 | 9 |
subtype3 | 1 | 41 | 13 | 2 |
Figure S16. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

P value = 0.00958 (Fisher's exact test)
Table S20. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 13 | 142 |
subtype1 | 0 | 43 |
subtype2 | 9 | 46 |
subtype3 | 4 | 53 |
Figure S17. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.791 (Fisher's exact test)
Table S21. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 130 | 7 |
subtype1 | 38 | 1 |
subtype2 | 46 | 3 |
subtype3 | 46 | 3 |
Figure S18. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

Table S22. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 23 | 66 | 66 |
P value = 100 (logrank test)
Table S23. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 155 | 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 | 66 | 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'

P value = 0.001 (ANOVA)
Table S24. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 155 | 46.6 (16.2) |
subtype1 | 23 | 53.6 (16.7) |
subtype2 | 66 | 41.3 (15.0) |
subtype3 | 66 | 49.4 (15.7) |
Figure S20. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.39 (Fisher's exact test)
Table S25. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 106 | 49 |
subtype1 | 17 | 6 |
subtype2 | 48 | 18 |
subtype3 | 41 | 25 |
Figure S21. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

P value = 1.74e-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 | 83 | 52 | 13 |
subtype1 | 5 | 7 | 10 | 1 |
subtype2 | 1 | 53 | 4 | 8 |
subtype3 | 1 | 23 | 38 | 4 |
Figure S22. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

P value = 0.331 (Fisher's exact test)
Table S27. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 13 | 142 |
subtype1 | 2 | 21 |
subtype2 | 8 | 58 |
subtype3 | 3 | 63 |
Figure S23. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0769 (Fisher's exact test)
Table S28. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 130 | 7 |
subtype1 | 19 | 3 |
subtype2 | 52 | 3 |
subtype3 | 59 | 1 |
Figure S24. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

Table S29. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 64 | 22 | 47 | 65 |
P value = 100 (logrank test)
Table S30. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 198 | 1 | 0.0 - 66.1 (8.1) |
subtype1 | 64 | 1 | 0.0 - 65.9 (6.9) |
subtype2 | 22 | 0 | 0.1 - 66.1 (5.9) |
subtype3 | 47 | 0 | 0.2 - 66.1 (10.3) |
subtype4 | 65 | 0 | 0.2 - 65.9 (10.0) |
Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.0108 (ANOVA)
Table S31. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 198 | 46.7 (16.1) |
subtype1 | 64 | 51.5 (16.3) |
subtype2 | 22 | 44.2 (14.1) |
subtype3 | 47 | 41.5 (13.6) |
subtype4 | 65 | 46.7 (17.0) |
Figure S26. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.734 (Fisher's exact test)
Table S32. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 146 | 52 |
subtype1 | 45 | 19 |
subtype2 | 18 | 4 |
subtype3 | 36 | 11 |
subtype4 | 47 | 18 |
Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

P value = 8.86e-17 (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 | 7 | 113 | 58 | 20 |
subtype1 | 6 | 14 | 43 | 1 |
subtype2 | 0 | 14 | 4 | 4 |
subtype3 | 1 | 37 | 8 | 1 |
subtype4 | 0 | 48 | 3 | 14 |
Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

P value = 0.00383 (Fisher's exact test)
Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 12 | 186 |
subtype1 | 1 | 63 |
subtype2 | 0 | 22 |
subtype3 | 1 | 46 |
subtype4 | 10 | 55 |
Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.664 (Fisher's exact test)
Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 162 | 8 |
subtype1 | 52 | 3 |
subtype2 | 18 | 0 |
subtype3 | 37 | 3 |
subtype4 | 55 | 2 |
Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

Table S36. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 89 | 34 | 75 |
P value = 100 (logrank test)
Table S37. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 198 | 1 | 0.0 - 66.1 (8.1) |
subtype1 | 89 | 0 | 0.2 - 66.1 (9.3) |
subtype2 | 34 | 0 | 0.2 - 66.1 (10.1) |
subtype3 | 75 | 1 | 0.0 - 65.9 (6.8) |
Figure S31. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.0163 (ANOVA)
Table S38. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 198 | 46.7 (16.1) |
subtype1 | 89 | 46.1 (16.1) |
subtype2 | 34 | 40.8 (15.1) |
subtype3 | 75 | 50.2 (15.7) |
Figure S32. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.453 (Fisher's exact test)
Table S39. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 146 | 52 |
subtype1 | 65 | 24 |
subtype2 | 28 | 6 |
subtype3 | 53 | 22 |
Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

P value = 6.92e-16 (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 | 7 | 113 | 58 | 20 |
subtype1 | 0 | 64 | 8 | 17 |
subtype2 | 1 | 29 | 4 | 0 |
subtype3 | 6 | 20 | 46 | 3 |
Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

P value = 0.0217 (Fisher's exact test)
Table S41. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 12 | 186 |
subtype1 | 10 | 79 |
subtype2 | 1 | 33 |
subtype3 | 1 | 74 |
Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (Fisher's exact test)
Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 162 | 8 |
subtype1 | 73 | 4 |
subtype2 | 29 | 1 |
subtype3 | 60 | 3 |
Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

Table S43. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 70 | 84 | 56 |
P value = 100 (logrank test)
Table S44. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 210 | 1 | 0.0 - 66.1 (8.2) |
subtype1 | 70 | 1 | 0.3 - 65.9 (7.2) |
subtype2 | 84 | 0 | 0.2 - 66.1 (10.1) |
subtype3 | 56 | 0 | 0.0 - 66.1 (8.0) |
Figure S37. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.0556 (ANOVA)
Table S45. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 210 | 46.6 (16.0) |
subtype1 | 70 | 49.6 (16.9) |
subtype2 | 84 | 43.5 (15.1) |
subtype3 | 56 | 47.6 (15.4) |
Figure S38. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.335 (Fisher's exact test)
Table S46. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 155 | 55 |
subtype1 | 48 | 22 |
subtype2 | 62 | 22 |
subtype3 | 45 | 11 |
Figure S39. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

P value = 6.15e-18 (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 | 116 | 64 | 22 |
subtype1 | 6 | 15 | 48 | 1 |
subtype2 | 2 | 64 | 10 | 8 |
subtype3 | 0 | 37 | 6 | 13 |
Figure S40. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

P value = 0.468 (Fisher's exact test)
Table S48. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 196 |
subtype1 | 3 | 67 |
subtype2 | 8 | 76 |
subtype3 | 3 | 53 |
Figure S41. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (Fisher's exact test)
Table S49. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 175 | 9 |
subtype1 | 59 | 3 |
subtype2 | 67 | 4 |
subtype3 | 49 | 2 |
Figure S42. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

Table S50. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 62 | 63 | 85 |
P value = 100 (logrank test)
Table S51. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 210 | 1 | 0.0 - 66.1 (8.2) |
subtype1 | 62 | 0 | 0.0 - 66.1 (8.0) |
subtype2 | 63 | 1 | 0.3 - 65.9 (7.1) |
subtype3 | 85 | 0 | 0.2 - 66.1 (10.0) |
Figure S43. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.00403 (ANOVA)
Table S52. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 210 | 46.6 (16.0) |
subtype1 | 62 | 48.4 (16.2) |
subtype2 | 63 | 50.6 (16.2) |
subtype3 | 85 | 42.3 (14.6) |
Figure S44. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.365 (Fisher's exact test)
Table S53. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 155 | 55 |
subtype1 | 50 | 12 |
subtype2 | 45 | 18 |
subtype3 | 60 | 25 |
Figure S45. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

P value = 6.25e-20 (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 | 116 | 64 | 22 |
subtype1 | 0 | 39 | 7 | 16 |
subtype2 | 6 | 11 | 45 | 1 |
subtype3 | 2 | 66 | 12 | 5 |
Figure S46. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

P value = 0.0388 (Fisher's exact test)
Table S55. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 14 | 196 |
subtype1 | 3 | 59 |
subtype2 | 1 | 62 |
subtype3 | 10 | 75 |
Figure S47. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.834 (Fisher's exact test)
Table S56. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'
nPatients | NO | YES |
---|---|---|
ALL | 175 | 9 |
subtype1 | 56 | 2 |
subtype2 | 52 | 3 |
subtype3 | 67 | 4 |
Figure S48. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

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Cluster data file = THCA-TP.mergedcluster.txt
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Clinical data file = THCA-TP.clin.merged.picked.txt
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Number of patients = 218
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Number of clustering approaches = 8
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Number of selected clinical features = 6
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Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
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
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
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
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