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 211 patients, 23 significant findings detected with P value < 0.05.

  • 4 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death' and 'AGE'.

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

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that correlate to 'Time to Death',  'AGE', 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, 23 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 0.0291 1 0.512 0.467 1
METHLYATION CNMF 0.114 0.0161 0.692 1.39e-16 0.0596 1
RPPA CNMF subtypes 0.414 0.000166 0.906 2.64e-06 0.00958 0.779
RPPA cHierClus subtypes 0.0455 0.001 0.39 1.74e-11 0.331 1
RNAseq CNMF subtypes 0.0253 0.0495 0.623 6.02e-15 0.003 1
RNAseq cHierClus subtypes 0.0253 0.04 0.292 1.11e-16 0.0999 1
MIRseq CNMF subtypes 0.0143 0.0559 0.432 2.47e-17 0.393 0.63
MIRseq cHierClus subtypes 0.0143 0.00353 0.229 1.65e-19 0.0362 0.637
Clustering Approach #1: 'CN CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 150 36 12 8
'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 206 1 0.0 - 66.1 (8.5)
subtype1 150 0 0.1 - 66.1 (8.1)
subtype2 36 0 0.0 - 65.9 (9.0)
subtype3 12 1 0.3 - 65.9 (7.5)
subtype4 8 0 9.3 - 35.2 (13.5)

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

'CN CNMF' versus 'AGE'

P value = 0.0291 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 206 46.4 (16.0)
subtype1 150 45.1 (16.4)
subtype2 36 46.1 (12.4)
subtype3 12 56.8 (16.2)
subtype4 8 56.0 (16.9)

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

'CN CNMF' versus 'GENDER'

P value = 1 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 153 53
subtype1 111 39
subtype2 27 9
subtype3 9 3
subtype4 6 2

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

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.512 (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 8 115 61 22
subtype1 4 88 41 17
subtype2 2 16 14 4
subtype3 1 6 5 0
subtype4 1 5 1 1

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 = 0.467 (Fisher's exact test)

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

nPatients NO YES
ALL 14 192
subtype1 11 139
subtype2 1 35
subtype3 1 11
subtype4 1 7

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 3 203
subtype1 3 147
subtype2 0 36
subtype3 0 12
subtype4 0 8

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 70 30 111
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.114 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 211 1 0.0 - 66.1 (8.2)
subtype1 70 1 0.0 - 66.1 (7.0)
subtype2 30 0 0.1 - 66.1 (6.9)
subtype3 111 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'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0161 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 211 46.5 (16.0)
subtype1 70 50.9 (16.0)
subtype2 30 42.9 (15.2)
subtype3 111 44.7 (15.8)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.692 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 156 55
subtype1 50 20
subtype2 24 6
subtype3 82 29

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.39e-16 (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 8 117 64 22
subtype1 6 13 48 3
subtype2 1 21 4 4
subtype3 1 83 12 15

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 = 0.0596 (Fisher's exact test)

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

nPatients NO YES
ALL 14 197
subtype1 1 69
subtype2 2 28
subtype3 11 100

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 3 208
subtype1 1 69
subtype2 0 30
subtype3 2 109

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 43 55 57
'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 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'

'RPPA CNMF subtypes' versus 'AGE'

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'

'RPPA CNMF subtypes' versus 'GENDER'

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'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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'

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

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'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.779 (Fisher's exact test)

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

nPatients NO YES
ALL 3 152
subtype1 0 43
subtype2 1 54
subtype3 2 55

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 66
'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 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'

'RPPA cHierClus subtypes' versus 'AGE'

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'

'RPPA cHierClus subtypes' versus 'GENDER'

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'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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'

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

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'

'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 3 152
subtype1 0 23
subtype2 2 64
subtype3 1 65

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 58 16 43 56
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0253 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 173 1 0.1 - 66.1 (8.2)
subtype1 58 1 0.3 - 65.9 (6.9)
subtype2 16 0 0.1 - 66.1 (6.4)
subtype3 43 0 0.2 - 66.1 (10.0)
subtype4 56 0 0.2 - 65.9 (11.6)

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 = 0.0495 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 173 47.3 (16.0)
subtype1 58 51.3 (17.1)
subtype2 16 43.2 (14.6)
subtype3 43 43.0 (12.6)
subtype4 56 47.6 (16.9)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.623 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 124 49
subtype1 39 19
subtype2 13 3
subtype3 33 10
subtype4 39 17

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 = 6.02e-15 (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 92 54 20
subtype1 6 11 40 1
subtype2 0 8 4 4
subtype3 1 33 7 2
subtype4 0 40 3 13

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 = 0.003 (Fisher's exact test)

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

nPatients NO YES
ALL 12 161
subtype1 1 57
subtype2 0 16
subtype3 1 42
subtype4 10 46

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 3 170
subtype1 1 57
subtype2 0 16
subtype3 1 42
subtype4 1 55

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 1 110 62
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0253 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 172 1 0.1 - 66.1 (8.2)
subtype2 110 0 0.2 - 66.1 (9.5)
subtype3 62 1 0.1 - 65.9 (7.0)

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 = 0.04 (t-test)

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

nPatients Mean (Std.Dev)
ALL 172 47.4 (16.0)
subtype2 110 45.5 (15.2)
subtype3 62 50.9 (16.7)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.292 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 123 49
subtype2 82 28
subtype3 41 21

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 = 1.11e-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 92 53 20
subtype2 1 80 11 18
subtype3 6 12 42 2

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 = 0.0999 (Fisher's exact test)

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

nPatients NO YES
ALL 11 161
subtype2 10 100
subtype3 1 61

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 = 1 (Fisher's exact test)

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

nPatients NO YES
ALL 2 170
subtype2 1 109
subtype3 1 61

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 71 81 58
'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 210 1 0.0 - 66.1 (8.2)
subtype1 71 1 0.3 - 65.9 (7.2)
subtype2 81 0 0.2 - 66.1 (10.4)
subtype3 58 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'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0559 (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 71 49.7 (16.9)
subtype2 81 43.5 (15.1)
subtype3 58 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 = 0.432 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 155 55
subtype1 49 22
subtype2 60 21
subtype3 46 12

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 = 2.47e-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 8 116 64 22
subtype1 6 15 48 2
subtype2 2 62 10 7
subtype3 0 39 6 13

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 = 0.393 (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 68
subtype2 8 73
subtype3 3 55

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 = 0.63 (Fisher's exact test)

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

nPatients NO YES
ALL 3 207
subtype1 2 69
subtype2 1 80
subtype3 0 58

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 65 82 63
'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 210 1 0.0 - 66.1 (8.2)
subtype1 65 0 0.0 - 66.1 (8.0)
subtype2 82 0 0.2 - 66.1 (9.9)
subtype3 63 1 0.3 - 65.9 (7.1)

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 = 0.00353 (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 65 48.1 (15.9)
subtype2 82 42.2 (14.8)
subtype3 63 50.8 (16.3)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.229 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 155 55
subtype1 53 12
subtype2 58 24
subtype3 44 19

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 = 1.65e-19 (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 42 7 16
subtype2 2 63 12 5
subtype3 6 11 45 1

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 = 0.0362 (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 62
subtype2 10 72
subtype3 1 62

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 = 0.637 (Fisher's exact test)

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

nPatients NO YES
ALL 3 207
subtype1 0 65
subtype2 2 80
subtype3 1 62

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 = 211

  • 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[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)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle