Correlation between molecular cancer subtypes and selected clinical features
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

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)
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