Thyroid Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 218 patients, 11 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

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

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

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

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

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 (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 11 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
(1.00) 		0.0155
(0.543) 		0.66
(1.00) 		0.0953
(1.00) 		0.493
(1.00) 		0.42
(1.00)
METHLYATION CNMF 100
(1.00) 		0.029
(0.929) 		0.976
(1.00) 		2.07e-15
(9.13e-14) 		0.035
(1.00) 		1
(1.00)
RPPA CNMF subtypes 100
(1.00) 		0.000166
(0.00679) 		0.906
(1.00) 		2.64e-06
(0.000111) 		0.00958
(0.354) 		0.791
(1.00)
RPPA cHierClus subtypes 100
(1.00) 		0.001
(0.0402) 		0.39
(1.00) 		1.74e-11
(7.46e-10) 		0.331
(1.00) 		0.0769
(1.00)
RNAseq CNMF subtypes 100
(1.00) 		0.0108
(0.387) 		0.734
(1.00) 		8.86e-17
(4.07e-15) 		0.00383
(0.149) 		0.664
(1.00)
RNAseq cHierClus subtypes 100
(1.00) 		0.0163
(0.554) 		0.453
(1.00) 		6.92e-16
(3.11e-14) 		0.0217
(0.717) 		1
(1.00)
MIRseq CNMF subtypes 100
(1.00) 		0.0556
(1.00) 		0.335
(1.00) 		6.15e-18
(2.89e-16) 		0.468
(1.00) 		1
(1.00)
MIRseq cHierClus subtypes 100
(1.00) 		0.00403
(0.153) 		0.365
(1.00) 		6.25e-20
(3e-18) 		0.0388
(1.00) 		0.834
(1.00)
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 18 161 35
'CN CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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 18 1 0.4 - 65.9 (11.8)
subtype2 161 0 0.1 - 66.1 (8.0)
subtype3 35 0 0.0 - 65.9 (7.6)

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

'CN CNMF' versus 'AGE'

P value = 0.0155 (ANOVA), Q value = 0.54

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

nPatients Mean (Std.Dev)
ALL 214 46.5 (15.9)
subtype1 18 56.7 (13.8)
subtype2 161 45.4 (16.4)
subtype3 35 46.0 (12.6)

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

'CN CNMF' versus 'GENDER'

P value = 0.66 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 160 54
subtype1 12 6
subtype2 121 40
subtype3 27 8

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

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0953 (Chi-square test), Q value = 1

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 0
subtype2 4 99 40 18
subtype3 2 16 13 4

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 = 0.493 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 14 200
subtype1 2 16
subtype2 11 150
subtype3 1 34

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

'CN CNMF' versus 'RADIATIONEXPOSURE'

P value = 0.42 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 177 9
subtype1 16 1
subtype2 128 8
subtype3 33 0

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

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 72 30 116
'METHLYATION CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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 30 0 0.1 - 66.1 (5.8)
subtype3 116 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 = 0.029 (ANOVA), Q value = 0.93

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 30 44.1 (14.8)
subtype3 116 44.6 (15.9)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.976 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 162 56
subtype1 53 19
subtype2 23 7
subtype3 86 30

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 2.07e-15 (Chi-square test), Q value = 9.1e-14

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 20 5 4
subtype3 1 88 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 = 0.035 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 14 204
subtype1 1 71
subtype2 1 29
subtype3 12 104

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

'METHLYATION CNMF' versus 'RADIATIONEXPOSURE'

P value = 1 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 180 9
subtype1 57 3
subtype2 25 1
subtype3 98 5

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

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 = 100 (logrank test), Q value = 1

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), Q value = 0.0068

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), Q value = 1

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), Q value = 0.00011

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), Q value = 0.35

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 'RADIATIONEXPOSURE'

P value = 0.791 (Fisher's exact test), Q value = 1

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'

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 = 100 (logrank test), Q value = 1

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), Q value = 0.04

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), Q value = 1

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), Q value = 7.5e-10

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), Q value = 1

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 'RADIATIONEXPOSURE'

P value = 0.0769 (Fisher's exact test), Q value = 1

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'

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 64 22 47 65
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0108 (ANOVA), Q value = 0.39

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'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.734 (Fisher's exact test), Q value = 1

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'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.86e-17 (Chi-square test), Q value = 4.1e-15

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'

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

P value = 0.00383 (Fisher's exact test), Q value = 0.15

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'

'RNAseq CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.664 (Fisher's exact test), Q value = 1

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'

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 89 34 75
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0163 (ANOVA), Q value = 0.55

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'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.453 (Fisher's exact test), Q value = 1

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'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.92e-16 (Chi-square test), Q value = 3.1e-14

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'

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

P value = 0.0217 (Fisher's exact test), Q value = 0.72

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'

'RNAseq cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 1 (Fisher's exact test), Q value = 1

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'

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 70 84 56
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0556 (ANOVA), Q value = 1

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'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.335 (Fisher's exact test), Q value = 1

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'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.15e-18 (Chi-square test), Q value = 2.9e-16

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'

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

P value = 0.468 (Fisher's exact test), Q value = 1

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'

'MIRseq CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 1 (Fisher's exact test), Q value = 1

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'

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 62 63 85
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.00403 (ANOVA), Q value = 0.15

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'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.365 (Fisher's exact test), Q value = 1

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'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.25e-20 (Chi-square test), Q value = 3e-18

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'

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

P value = 0.0388 (Fisher's exact test), Q value = 1

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'

'MIRseq cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.834 (Fisher's exact test), Q value = 1

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'

Methods & Data
Input

  • Cluster data file = THCA-TP.mergedcluster.txt

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

  • Number of patients = 218

  • 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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

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] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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