Correlation between aggregated molecular cancer subtypes and selected clinical features
Thyroid Adenocarcinoma (Mut_BRAF)
01 July 2013  |  awg_thca__2013_07_01
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1XK8CPV
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 15 clinical features across 229 patients, 11 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE' and 'MULTIFOCALITY'.

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

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE' and 'EXTRATHYROIDAL.EXTENSION'.

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

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'AGE', and 'EXTRATHYROIDAL.EXTENSION'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'EXTRATHYROIDAL.EXTENSION'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 15 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
Statistical
Tests
Copy
Number
Ratio
CNMF
subtypes
METHLYATION
CNMF
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRseq
Mature
CNMF
subtypes
MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.689
(1.00)
0.026
(1.00)
0.146
(1.00)
0.0833
(1.00)
0.00266
(0.268)
0.0269
(1.00)
0.00136
(0.14)
0.0256
(1.00)
AGE ANOVA 0.00329
(0.329)
0.0642
(1.00)
0.132
(1.00)
0.0427
(1.00)
1.26e-05
(0.00141)
0.0207
(1.00)
0.000152
(0.0166)
0.00386
(0.382)
GENDER Fisher's exact test 0.847
(1.00)
0.0544
(1.00)
0.167
(1.00)
0.0357
(1.00)
0.44
(1.00)
0.581
(1.00)
0.481
(1.00)
0.645
(1.00)
HISTOLOGICAL TYPE Chi-square test 0.208
(1.00)
0.00515
(0.499)
0.0227
(1.00)
0.00013
(0.0143)
0.00407
(0.399)
0.478
(1.00)
0.0616
(1.00)
0.0166
(1.00)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.76
(1.00)
0.427
(1.00)
0.289
(1.00)
0.105
(1.00)
0.503
(1.00)
0.574
(1.00)
0.0398
(1.00)
0.186
(1.00)
RADIATIONEXPOSURE Fisher's exact test 0.322
(1.00)
0.644
(1.00)
0.475
(1.00)
0.812
(1.00)
1
(1.00)
0.555
(1.00)
0.279
(1.00)
0.663
(1.00)
DISTANT METASTASIS Chi-square test 0.0565
(1.00)
0.553
(1.00)
0.912
(1.00)
0.658
(1.00)
0.00677
(0.643)
0.348
(1.00)
0.0103
(0.955)
0.129
(1.00)
EXTRATHYROIDAL EXTENSION Chi-square test 0.0409
(1.00)
0.0011
(0.115)
0.251
(1.00)
1.8e-05
(0.00199)
0.00115
(0.12)
0.0526
(1.00)
0.00195
(0.199)
0.000714
(0.0764)
LYMPH NODE METASTASIS Chi-square test 0.625
(1.00)
0.0145
(1.00)
0.113
(1.00)
0.056
(1.00)
0.588
(1.00)
0.657
(1.00)
0.485
(1.00)
0.72
(1.00)
COMPLETENESS OF RESECTION Chi-square test 0.0259
(1.00)
0.0797
(1.00)
0.185
(1.00)
0.0328
(1.00)
0.00636
(0.61)
0.0968
(1.00)
0.0297
(1.00)
0.0293
(1.00)
NUMBER OF LYMPH NODES ANOVA 0.7
(1.00)
0.0391
(1.00)
0.273
(1.00)
0.0132
(1.00)
0.216
(1.00)
0.259
(1.00)
0.668
(1.00)
0.0718
(1.00)
TUMOR STAGECODE ANOVA
NEOPLASM DISEASESTAGE Chi-square test 0.000219
(0.0237)
0.0317
(1.00)
0.692
(1.00)
0.162
(1.00)
0.0439
(1.00)
0.163
(1.00)
0.0426
(1.00)
0.00737
(0.693)
MULTIFOCALITY Fisher's exact test 0.00107
(0.114)
0.612
(1.00)
0.0253
(1.00)
0.0268
(1.00)
0.265
(1.00)
0.629
(1.00)
0.0785
(1.00)
0.417
(1.00)
TUMOR SIZE ANOVA 0.0986
(1.00)
0.0876
(1.00)
0.117
(1.00)
0.0467
(1.00)
0.986
(1.00)
0.478
(1.00)
0.708
(1.00)
0.871
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1.  Get Full Table Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 173 27 29
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.689 (logrank test), Q value = 1

Table S2.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 227 5 0.0 - 158.8 (15.0)
subtype1 171 4 0.2 - 158.8 (15.4)
subtype2 27 1 0.6 - 138.1 (19.0)
subtype3 29 0 0.0 - 85.1 (14.0)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.00329 (ANOVA), Q value = 0.33

Table S3.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 229 46.8 (14.9)
subtype1 173 45.4 (14.7)
subtype2 27 55.7 (15.5)
subtype3 29 46.9 (13.0)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.847 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 169 60
subtype1 129 44
subtype2 19 8
subtype3 21 8

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.208 (Chi-square test), Q value = 1

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER SPECIFY THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 184 16 26
subtype1 3 140 14 16
subtype2 0 19 1 7
subtype3 0 25 1 3

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.76 (Fisher's exact test), Q value = 1

Table S6.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 218
subtype1 8 165
subtype2 2 25
subtype3 1 28

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.322 (Fisher's exact test), Q value = 1

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients NO YES
ALL 197 8
subtype1 147 5
subtype2 23 2
subtype3 27 1

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

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0565 (Chi-square test), Q value = 1

Table S8.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 132 4 93
subtype1 108 4 61
subtype2 11 0 16
subtype3 13 0 16

Figure S7.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.0409 (Chi-square test), Q value = 1

Table S9.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 76 9 139
subtype1 56 6 106
subtype2 13 3 11
subtype3 7 0 22

Figure S8.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.625 (Chi-square test), Q value = 1

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B NX
ALL 92 27 56 33 21
subtype1 73 21 40 21 18
subtype2 8 4 8 6 1
subtype3 11 2 8 6 2

Figure S9.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0259 (Chi-square test), Q value = 1

Table S11.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 182 22 1 13
subtype1 142 16 1 5
subtype2 17 5 0 4
subtype3 23 1 0 4

Figure S10.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.7 (ANOVA), Q value = 1

Table S12.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 188 3.5 (5.6)
subtype1 137 3.5 (5.7)
subtype2 26 4.3 (6.2)
subtype3 25 3.1 (4.4)

Figure S11.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000219 (Chi-square test), Q value = 0.024

Table S13.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 132 16 55 22 3
subtype1 107 14 36 12 3
subtype2 5 1 15 6 0
subtype3 20 1 4 4 0

Figure S12.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.00107 (Fisher's exact test), Q value = 0.11

Table S14.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 105 121
subtype1 71 100
subtype2 12 15
subtype3 22 6

Figure S13.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.0986 (ANOVA), Q value = 1

Table S15.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

nPatients Mean (Std.Dev)
ALL 186 2.8 (1.6)
subtype1 141 2.8 (1.6)
subtype2 22 3.4 (1.3)
subtype3 23 2.4 (1.5)

Figure S14.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S16.  Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 44 88 97
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.026 (logrank test), Q value = 1

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 227 5 0.0 - 158.8 (15.0)
subtype1 43 0 1.1 - 157.2 (17.5)
subtype2 88 5 0.2 - 147.8 (17.5)
subtype3 96 0 0.0 - 158.8 (12.3)

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0642 (ANOVA), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 229 46.8 (14.9)
subtype1 44 46.0 (13.6)
subtype2 88 49.6 (16.6)
subtype3 97 44.6 (13.6)

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0544 (Fisher's exact test), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 169 60
subtype1 32 12
subtype2 58 30
subtype3 79 18

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00515 (Chi-square test), Q value = 0.5

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER SPECIFY THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 184 16 26
subtype1 0 34 2 8
subtype2 1 70 2 15
subtype3 2 80 12 3

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.427 (Fisher's exact test), Q value = 1

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 218
subtype1 1 43
subtype2 3 85
subtype3 7 90

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'RADIATIONEXPOSURE'

P value = 0.644 (Fisher's exact test), Q value = 1

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients NO YES
ALL 197 8
subtype1 39 2
subtype2 77 4
subtype3 81 2

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.553 (Chi-square test), Q value = 1

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 132 4 93
subtype1 25 0 19
subtype2 48 3 37
subtype3 59 1 37

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.0011 (Chi-square test), Q value = 0.12

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 76 9 139
subtype1 16 0 28
subtype2 32 9 44
subtype3 28 0 67

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0145 (Chi-square test), Q value = 1

Table S25.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B NX
ALL 92 27 56 33 21
subtype1 18 3 13 7 3
subtype2 25 15 26 17 5
subtype3 49 9 17 9 13

Figure S23.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0797 (Chi-square test), Q value = 1

Table S26.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 182 22 1 13
subtype1 33 5 1 3
subtype2 66 13 0 6
subtype3 83 4 0 4

Figure S24.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0391 (ANOVA), Q value = 1

Table S27.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 188 3.5 (5.6)
subtype1 40 2.8 (4.1)
subtype2 72 4.9 (5.8)
subtype3 76 2.7 (6.0)

Figure S25.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0317 (Chi-square test), Q value = 1

Table S28.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 132 16 55 22 3
subtype1 28 2 10 4 0
subtype2 42 6 22 16 2
subtype3 62 8 23 2 1

Figure S26.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'MULTIFOCALITY'

P value = 0.612 (Fisher's exact test), Q value = 1

Table S29.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 105 121
subtype1 18 26
subtype2 43 43
subtype3 44 52

Figure S27.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'METHLYATION CNMF' versus 'TUMOR.SIZE'

P value = 0.0876 (ANOVA), Q value = 1

Table S30.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

nPatients Mean (Std.Dev)
ALL 186 2.8 (1.6)
subtype1 35 2.6 (1.6)
subtype2 75 3.1 (1.5)
subtype3 76 2.6 (1.7)

Figure S28.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S31.  Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 57 77 89
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.146 (logrank test), Q value = 1

Table S32.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 221 5 0.0 - 158.8 (14.7)
subtype1 57 3 0.2 - 157.2 (11.2)
subtype2 76 1 0.2 - 147.8 (17.4)
subtype3 88 1 0.0 - 158.8 (12.6)

Figure S29.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.132 (ANOVA), Q value = 1

Table S33.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 223 47.0 (15.0)
subtype1 57 50.4 (14.9)
subtype2 77 45.7 (16.2)
subtype3 89 45.8 (13.8)

Figure S30.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.167 (Fisher's exact test), Q value = 1

Table S34.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 165 58
subtype1 45 12
subtype2 51 26
subtype3 69 20

Figure S31.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0227 (Chi-square test), Q value = 1

Table S35.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER SPECIFY THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 178 16 26
subtype1 0 44 4 9
subtype2 1 60 2 14
subtype3 2 74 10 3

Figure S32.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.289 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 10 213
subtype1 1 56
subtype2 6 71
subtype3 3 86

Figure S33.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.475 (Fisher's exact test), Q value = 1

Table S37.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients NO YES
ALL 191 8
subtype1 49 1
subtype2 66 2
subtype3 76 5

Figure S34.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.912 (Chi-square test), Q value = 1

Table S38.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 130 4 89
subtype1 35 1 21
subtype2 45 2 30
subtype3 50 1 38

Figure S35.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.251 (Chi-square test), Q value = 1

Table S39.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 75 9 134
subtype1 23 2 32
subtype2 29 4 40
subtype3 23 3 62

Figure S36.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.113 (Chi-square test), Q value = 1

Table S40.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B NX
ALL 89 27 54 33 20
subtype1 19 4 16 13 5
subtype2 29 16 17 9 6
subtype3 41 7 21 11 9

Figure S37.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.185 (Chi-square test), Q value = 1

Table S41.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 177 21 1 13
subtype1 40 9 1 5
subtype2 64 7 0 3
subtype3 73 5 0 5

Figure S38.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.273 (ANOVA), Q value = 1

Table S42.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 182 3.6 (5.7)
subtype1 51 4.0 (5.4)
subtype2 61 4.3 (7.3)
subtype3 70 2.8 (4.1)

Figure S39.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.692 (Chi-square test), Q value = 1

Table S43.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 127 16 54 22 3
subtype1 30 2 16 8 1
subtype2 44 5 21 6 1
subtype3 53 9 17 8 1

Figure S40.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.0253 (Fisher's exact test), Q value = 1

Table S44.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 103 117
subtype1 25 32
subtype2 27 47
subtype3 51 38

Figure S41.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.117 (ANOVA), Q value = 1

Table S45.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

nPatients Mean (Std.Dev)
ALL 182 2.8 (1.6)
subtype1 45 2.5 (1.6)
subtype2 67 3.1 (1.6)
subtype3 70 2.7 (1.6)

Figure S42.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S46.  Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 90 67 66
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0833 (logrank test), Q value = 1

Table S47.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 221 5 0.0 - 158.8 (14.7)
subtype1 89 0 0.0 - 158.8 (12.5)
subtype2 66 4 0.4 - 157.2 (14.6)
subtype3 66 1 0.2 - 147.8 (17.3)

Figure S43.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0427 (ANOVA), Q value = 1

Table S48.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 223 47.0 (15.0)
subtype1 90 46.2 (13.0)
subtype2 67 50.6 (16.1)
subtype3 66 44.3 (15.9)

Figure S44.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0357 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 165 58
subtype1 72 18
subtype2 52 15
subtype3 41 25

Figure S45.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00013 (Chi-square test), Q value = 0.014

Table S50.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER SPECIFY THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 178 16 26
subtype1 1 72 13 4
subtype2 0 49 2 16
subtype3 2 57 1 6

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

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

P value = 0.105 (Fisher's exact test), Q value = 1

Table S51.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 10 213
subtype1 1 89
subtype2 5 62
subtype3 4 62

Figure S47.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.812 (Fisher's exact test), Q value = 1

Table S52.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients NO YES
ALL 191 8
subtype1 80 3
subtype2 60 2
subtype3 51 3

Figure S48.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.658 (Chi-square test), Q value = 1

Table S53.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 130 4 89
subtype1 55 1 34
subtype2 35 1 31
subtype3 40 2 24

Figure S49.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 1.8e-05 (Chi-square test), Q value = 0.002

Table S54.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 75 9 134
subtype1 19 0 70
subtype2 35 4 26
subtype3 21 5 38

Figure S50.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.056 (Chi-square test), Q value = 1

Table S55.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B NX
ALL 89 27 54 33 20
subtype1 45 5 23 9 8
subtype2 17 12 17 14 7
subtype3 27 10 14 10 5

Figure S51.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0328 (Chi-square test), Q value = 1

Table S56.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 177 21 1 13
subtype1 78 3 0 6
subtype2 46 12 1 5
subtype3 53 6 0 2

Figure S52.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0132 (ANOVA), Q value = 1

Table S57.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 182 3.6 (5.7)
subtype1 71 2.6 (4.5)
subtype2 60 5.4 (7.4)
subtype3 51 3.0 (4.4)

Figure S53.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.162 (Chi-square test), Q value = 1

Table S58.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 127 16 54 22 3
subtype1 55 8 19 6 1
subtype2 31 2 24 9 1
subtype3 41 6 11 7 1

Figure S54.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.0268 (Fisher's exact test), Q value = 1

Table S59.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 103 117
subtype1 52 38
subtype2 26 41
subtype3 25 38

Figure S55.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'RNAseq cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.0467 (ANOVA), Q value = 1

Table S60.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

nPatients Mean (Std.Dev)
ALL 182 2.8 (1.6)
subtype1 69 2.4 (1.5)
subtype2 59 3.1 (1.7)
subtype3 54 2.9 (1.6)

Figure S56.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S61.  Get Full Table Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 78 69 81
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00266 (logrank test), Q value = 0.27

Table S62.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 226 5 0.0 - 158.8 (15.1)
subtype1 78 0 0.0 - 158.8 (21.1)
subtype2 67 0 0.6 - 147.8 (14.5)
subtype3 81 5 0.2 - 157.2 (11.2)

Figure S57.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 1.26e-05 (ANOVA), Q value = 0.0014

Table S63.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 228 46.9 (14.9)
subtype1 78 40.7 (12.1)
subtype2 69 48.7 (15.0)
subtype3 81 51.3 (15.3)

Figure S58.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.44 (Fisher's exact test), Q value = 1

Table S64.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 168 60
subtype1 60 18
subtype2 47 22
subtype3 61 20

Figure S59.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00407 (Chi-square test), Q value = 0.4

Table S65.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER SPECIFY THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 183 16 26
subtype1 1 66 10 1
subtype2 0 56 3 10
subtype3 2 61 3 15

Figure S60.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.503 (Fisher's exact test), Q value = 1

Table S66.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 217
subtype1 5 73
subtype2 4 65
subtype3 2 79

Figure S61.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'RADIATIONEXPOSURE'

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

Table S67.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients NO YES
ALL 196 8
subtype1 67 3
subtype2 59 2
subtype3 70 3

Figure S62.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.00677 (Chi-square test), Q value = 0.64

Table S68.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 131 4 93
subtype1 35 1 42
subtype2 37 2 30
subtype3 59 1 21

Figure S63.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.00115 (Chi-square test), Q value = 0.12

Table S69.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 75 9 139
subtype1 15 1 61
subtype2 24 2 40
subtype3 36 6 38

Figure S64.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.588 (Chi-square test), Q value = 1

Table S70.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B NX
ALL 92 27 55 33 21
subtype1 33 10 17 8 10
subtype2 28 9 18 8 6
subtype3 31 8 20 17 5

Figure S65.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.00636 (Chi-square test), Q value = 0.61

Table S71.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 181 22 1 13
subtype1 68 1 0 5
subtype2 56 5 0 4
subtype3 57 16 1 4

Figure S66.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.216 (ANOVA), Q value = 1

Table S72.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 187 3.5 (5.6)
subtype1 61 3.1 (4.6)
subtype2 58 2.8 (4.5)
subtype3 68 4.5 (7.1)

Figure S67.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0439 (Chi-square test), Q value = 1

Table S73.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 131 16 55 22 3
subtype1 56 5 12 4 0
subtype2 38 6 16 7 2
subtype3 37 5 27 11 1

Figure S68.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'MULTIFOCALITY'

P value = 0.265 (Fisher's exact test), Q value = 1

Table S74.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 104 121
subtype1 41 36
subtype2 27 40
subtype3 36 45

Figure S69.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CNMF' versus 'TUMOR.SIZE'

P value = 0.986 (ANOVA), Q value = 1

Table S75.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

nPatients Mean (Std.Dev)
ALL 185 2.8 (1.6)
subtype1 60 2.8 (1.6)
subtype2 61 2.8 (1.5)
subtype3 64 2.8 (1.7)

Figure S70.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S76.  Get Full Table Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 30 105 93
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0269 (logrank test), Q value = 1

Table S77.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 226 5 0.0 - 158.8 (15.1)
subtype1 29 0 1.0 - 147.4 (14.5)
subtype2 104 0 0.2 - 147.8 (15.7)
subtype3 93 5 0.0 - 158.8 (14.6)

Figure S71.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0207 (ANOVA), Q value = 1

Table S78.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 228 46.9 (14.9)
subtype1 30 48.3 (14.0)
subtype2 105 44.0 (14.4)
subtype3 93 49.7 (15.2)

Figure S72.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.581 (Fisher's exact test), Q value = 1

Table S79.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 168 60
subtype1 20 10
subtype2 77 28
subtype3 71 22

Figure S73.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.478 (Chi-square test), Q value = 1

Table S80.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER SPECIFY THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 183 16 26
subtype1 0 24 1 5
subtype2 1 86 10 8
subtype3 2 73 5 13

Figure S74.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.574 (Fisher's exact test), Q value = 1

Table S81.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 217
subtype1 1 29
subtype2 7 98
subtype3 3 90

Figure S75.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONEXPOSURE'

P value = 0.555 (Fisher's exact test), Q value = 1

Table S82.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients NO YES
ALL 196 8
subtype1 29 0
subtype2 87 5
subtype3 80 3

Figure S76.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.348 (Chi-square test), Q value = 1

Table S83.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 131 4 93
subtype1 16 0 14
subtype2 55 3 47
subtype3 60 1 32

Figure S77.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.0526 (Chi-square test), Q value = 1

Table S84.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 75 9 139
subtype1 15 0 14
subtype2 27 3 71
subtype3 33 6 54

Figure S78.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.657 (Chi-square test), Q value = 1

Table S85.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B NX
ALL 92 27 55 33 21
subtype1 13 4 6 3 4
subtype2 45 14 26 11 9
subtype3 34 9 23 19 8

Figure S79.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0968 (Chi-square test), Q value = 1

Table S86.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 181 22 1 13
subtype1 24 3 1 1
subtype2 87 6 0 6
subtype3 70 13 0 6

Figure S80.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.259 (ANOVA), Q value = 1

Table S87.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 187 3.5 (5.6)
subtype1 25 4.6 (9.1)
subtype2 86 2.8 (4.4)
subtype3 76 3.9 (5.4)

Figure S81.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.163 (Chi-square test), Q value = 1

Table S88.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 131 16 55 22 3
subtype1 17 2 10 1 0
subtype2 67 10 18 8 2
subtype3 47 4 27 13 1

Figure S82.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'MULTIFOCALITY'

P value = 0.629 (Fisher's exact test), Q value = 1

Table S89.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 104 121
subtype1 12 18
subtype2 46 56
subtype3 46 47

Figure S83.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.SIZE'

P value = 0.478 (ANOVA), Q value = 1

Table S90.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

nPatients Mean (Std.Dev)
ALL 185 2.8 (1.6)
subtype1 25 3.1 (1.6)
subtype2 85 2.9 (1.6)
subtype3 75 2.7 (1.6)

Figure S84.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S91.  Get Full Table Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 65 40 62 61
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00136 (logrank test), Q value = 0.14

Table S92.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 226 5 0.0 - 158.8 (15.1)
subtype1 65 0 0.0 - 158.8 (20.4)
subtype2 39 0 1.0 - 147.8 (18.2)
subtype3 62 5 0.2 - 157.2 (10.7)
subtype4 60 0 0.6 - 131.2 (11.5)

Figure S85.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.000152 (ANOVA), Q value = 0.017

Table S93.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 228 46.9 (14.9)
subtype1 65 41.0 (12.2)
subtype2 40 45.5 (16.2)
subtype3 62 52.4 (16.2)
subtype4 61 48.5 (13.0)

Figure S86.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.481 (Fisher's exact test), Q value = 1

Table S94.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 168 60
subtype1 48 17
subtype2 26 14
subtype3 49 13
subtype4 45 16

Figure S87.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0616 (Chi-square test), Q value = 1

Table S95.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER SPECIFY THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 183 16 26
subtype1 1 57 6 1
subtype2 0 31 1 8
subtype3 1 46 3 12
subtype4 1 49 6 5

Figure S88.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.0398 (Fisher's exact test), Q value = 1

Table S96.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 217
subtype1 5 60
subtype2 4 36
subtype3 2 60
subtype4 0 61

Figure S89.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.279 (Fisher's exact test), Q value = 1

Table S97.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients NO YES
ALL 196 8
subtype1 52 3
subtype2 35 2
subtype3 53 3
subtype4 56 0

Figure S90.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'MIRseq Mature CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0103 (Chi-square test), Q value = 0.95

Table S98.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 131 4 93
subtype1 27 2 36
subtype2 19 0 21
subtype3 44 1 17
subtype4 41 1 19

Figure S91.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.00195 (Chi-square test), Q value = 0.2

Table S99.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 75 9 139
subtype1 14 1 48
subtype2 15 1 24
subtype3 30 6 26
subtype4 16 1 41

Figure S92.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.485 (Chi-square test), Q value = 1

Table S100.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B NX
ALL 92 27 55 33 21
subtype1 28 8 12 7 10
subtype2 16 5 11 6 2
subtype3 23 8 13 14 4
subtype4 25 6 19 6 5

Figure S93.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0297 (Chi-square test), Q value = 1

Table S101.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 181 22 1 13
subtype1 53 2 0 5
subtype2 32 2 1 3
subtype3 45 13 0 3
subtype4 51 5 0 2

Figure S94.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.668 (ANOVA), Q value = 1

Table S102.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 187 3.5 (5.6)
subtype1 50 3.1 (4.7)
subtype2 35 2.8 (4.6)
subtype3 53 4.1 (5.6)
subtype4 49 3.8 (7.1)

Figure S95.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0426 (Chi-square test), Q value = 1

Table S103.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 131 16 55 22 3
subtype1 45 6 9 3 1
subtype2 23 3 9 5 0
subtype3 29 0 22 10 1
subtype4 34 7 15 4 1

Figure S96.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'MULTIFOCALITY'

P value = 0.0785 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 104 121
subtype1 35 29
subtype2 12 27
subtype3 26 36
subtype4 31 29

Figure S97.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRseq Mature CNMF subtypes' versus 'TUMOR.SIZE'

P value = 0.708 (ANOVA), Q value = 1

Table S105.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

nPatients Mean (Std.Dev)
ALL 185 2.8 (1.6)
subtype1 50 3.0 (1.6)
subtype2 35 2.9 (1.4)
subtype3 49 2.7 (1.6)
subtype4 51 2.7 (1.7)

Figure S98.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S106.  Get Full Table Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 108 21 99
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0256 (logrank test), Q value = 1

Table S107.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 226 5 0.0 - 158.8 (15.1)
subtype1 107 0 0.2 - 158.8 (15.4)
subtype2 21 0 0.0 - 116.9 (20.9)
subtype3 98 5 0.2 - 157.2 (12.9)

Figure S99.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.00386 (ANOVA), Q value = 0.38

Table S108.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 228 46.9 (14.9)
subtype1 108 44.6 (14.2)
subtype2 21 41.6 (13.8)
subtype3 99 50.5 (15.1)

Figure S100.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.645 (Fisher's exact test), Q value = 1

Table S109.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 168 60
subtype1 79 29
subtype2 14 7
subtype3 75 24

Figure S101.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0166 (Chi-square test), Q value = 1

Table S110.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients OTHER SPECIFY THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL 3 183 16 26
subtype1 2 88 11 7
subtype2 0 21 0 0
subtype3 1 74 5 19

Figure S102.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.186 (Fisher's exact test), Q value = 1

Table S111.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 11 217
subtype1 8 100
subtype2 1 20
subtype3 2 97

Figure S103.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONEXPOSURE'

P value = 0.663 (Fisher's exact test), Q value = 1

Table S112.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

nPatients NO YES
ALL 196 8
subtype1 89 5
subtype2 20 0
subtype3 87 3

Figure S104.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONEXPOSURE'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.129 (Chi-square test), Q value = 1

Table S113.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 131 4 93
subtype1 59 3 46
subtype2 8 0 13
subtype3 64 1 34

Figure S105.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'EXTRATHYROIDAL.EXTENSION'

P value = 0.000714 (Chi-square test), Q value = 0.076

Table S114.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

nPatients MINIMAL (T3) MODERATE/ADVANCED (T4A) NONE
ALL 75 9 139
subtype1 26 3 75
subtype2 3 0 18
subtype3 46 6 46

Figure S106.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'EXTRATHYROIDAL.EXTENSION'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.72 (Chi-square test), Q value = 1

Table S115.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 N1A N1B NX
ALL 92 27 55 33 21
subtype1 46 15 27 11 9
subtype2 9 1 5 3 3
subtype3 37 11 23 19 9

Figure S107.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0293 (Chi-square test), Q value = 1

Table S116.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 181 22 1 13
subtype1 89 5 0 7
subtype2 19 0 0 0
subtype3 73 17 1 6

Figure S108.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0718 (ANOVA), Q value = 1

Table S117.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 187 3.5 (5.6)
subtype1 90 2.8 (4.3)
subtype2 14 2.1 (2.9)
subtype3 83 4.6 (6.9)

Figure S109.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00737 (Chi-square test), Q value = 0.69

Table S118.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IVA STAGE IVC
ALL 131 16 55 22 3
subtype1 67 11 19 8 2
subtype2 15 3 1 2 0
subtype3 49 2 35 12 1

Figure S110.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'MULTIFOCALITY'

P value = 0.417 (Fisher's exact test), Q value = 1

Table S119.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

nPatients MULTIFOCAL UNIFOCAL
ALL 104 121
subtype1 52 53
subtype2 11 10
subtype3 41 58

Figure S111.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'MULTIFOCALITY'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR.SIZE'

P value = 0.871 (ANOVA), Q value = 1

Table S120.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

nPatients Mean (Std.Dev)
ALL 185 2.8 (1.6)
subtype1 87 2.9 (1.6)
subtype2 17 2.9 (1.4)
subtype3 81 2.8 (1.7)

Figure S112.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TUMOR.SIZE'

Methods & Data
Input
  • Cluster data file = THCA-Mut_BRAF.mergedcluster.txt

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

  • Number of patients = 229

  • Number of clustering approaches = 8

  • Number of selected clinical features = 15

  • 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)