Correlation between aggregated molecular cancer subtypes and selected clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
14 July 2016  |  awg_lihc__2016_07_14
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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1N0162Q
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 10 different clustering approaches and 12 clinical features across 377 patients, 45 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 'PATHOLOGY_T_STAGE' and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'GENDER',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • CNMF clustering analysis on RPPA data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'RACE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'GENDER', and 'RACE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE',  'RESIDUAL_TUMOR', and 'RACE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE',  'RESIDUAL_TUMOR', and 'RACE'.

  • 6 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE', and 'GENDER'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'GENDER'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'GENDER', and 'HISTOLOGICAL_TYPE'.

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'GENDER', and 'RACE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 45 significant findings detected.

Clinical
Features
Statistical
Tests
Copy
Number
Ratio
CNMF
subtypes
METHLYATION
CNMF
RPPA
CNMF
subtypes
RPPA
cHierClus
subtypes
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRseq
Mature
CNMF
subtypes
MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.206
(0.393)
0.953
(0.956)
0.563
(0.714)
0.0429
(0.12)
0.00485
(0.0224)
0.00337
(0.0169)
0.384
(0.584)
0.0555
(0.142)
0.101
(0.229)
0.00965
(0.0399)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.618
(0.734)
9.51e-09
(1.14e-06)
0.00207
(0.0127)
0.0495
(0.132)
4.66e-06
(0.000187)
1.46e-05
(0.000292)
4.53e-06
(0.000187)
6.58e-06
(0.000198)
0.00393
(0.0188)
7.4e-05
(0.000986)
PATHOLOGIC STAGE Fisher's exact test 0.166
(0.337)
0.737
(0.819)
5e-05
(0.000857)
0.00159
(0.0119)
0.00718
(0.0319)
0.00211
(0.0127)
0.0855
(0.21)
0.948
(0.956)
0.0304
(0.0961)
0.133
(0.289)
PATHOLOGY T STAGE Fisher's exact test 0.0454
(0.124)
0.956
(0.956)
0.00267
(0.0146)
0.00191
(0.0127)
0.00051
(0.00556)
0.00014
(0.00168)
0.0361
(0.108)
0.88
(0.91)
0.224
(0.402)
0.178
(0.345)
PATHOLOGY N STAGE Fisher's exact test 0.468
(0.659)
0.573
(0.716)
0.266
(0.453)
0.725
(0.813)
0.413
(0.619)
0.172
(0.341)
0.944
(0.956)
0.532
(0.695)
0.431
(0.639)
0.449
(0.649)
PATHOLOGY M STAGE Fisher's exact test 0.83
(0.878)
0.287
(0.473)
0.286
(0.473)
0.643
(0.757)
0.495
(0.659)
0.488
(0.659)
0.308
(0.5)
0.351
(0.555)
0.224
(0.402)
0.384
(0.584)
GENDER Fisher's exact test 0.672
(0.775)
0.0156
(0.0594)
0.254
(0.449)
0.0424
(0.12)
1e-05
(0.00024)
6e-05
(9e-04)
0.00201
(0.0127)
0.0287
(0.0931)
0.0396
(0.116)
0.021
(0.0743)
RADIATION THERAPY Fisher's exact test 0.835
(0.878)
0.565
(0.714)
0.492
(0.659)
0.856
(0.893)
0.0728
(0.182)
0.22
(0.402)
0.594
(0.727)
0.14
(0.299)
0.669
(0.775)
0.0991
(0.229)
HISTOLOGICAL TYPE Fisher's exact test 0.101
(0.229)
0.0184
(0.0671)
0.492
(0.659)
0.0921
(0.221)
0.00319
(0.0166)
0.00096
(0.00872)
0.0557
(0.142)
0.691
(0.79)
0.0318
(0.0978)
0.331
(0.53)
RESIDUAL TUMOR Fisher's exact test 0.372
(0.579)
0.547
(0.706)
0.532
(0.695)
0.608
(0.73)
0.014
(0.0561)
0.0242
(0.0829)
0.485
(0.659)
0.702
(0.795)
0.153
(0.318)
0.214
(0.401)
RACE Fisher's exact test 0.0158
(0.0594)
0.00109
(0.00872)
0.00106
(0.00872)
0.00265
(0.0146)
0.00079
(0.0079)
0.0264
(0.0881)
0.131
(0.289)
0.268
(0.453)
0.462
(0.659)
0.00933
(0.0399)
ETHNICITY Fisher's exact test 0.26
(0.452)
0.584
(0.722)
0.152
(0.318)
0.796
(0.853)
0.767
(0.836)
0.608
(0.73)
0.173
(0.341)
0.446
(0.649)
0.756
(0.833)
0.782
(0.845)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 93 151 126
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.206 (logrank test), Q value = 0.39

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

nPatients nDeath Duration Range (Median), Month
ALL 364 129 0.0 - 120.8 (19.8)
subtype1 90 26 0.3 - 108.8 (19.9)
subtype2 150 59 0.0 - 120.8 (19.3)
subtype3 124 44 0.4 - 114.3 (20.5)

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 'YEARS_TO_BIRTH'

P value = 0.618 (Kruskal-Wallis (anova)), Q value = 0.73

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

nPatients Mean (Std.Dev)
ALL 366 59.4 (13.2)
subtype1 92 57.4 (15.3)
subtype2 149 60.3 (11.8)
subtype3 125 59.9 (13.0)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.166 (Fisher's exact test), Q value = 0.34

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

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 173 87 3 62 9 7 2 1 2
subtype1 47 16 1 18 1 3 0 1 1
subtype2 60 42 1 26 5 4 0 0 1
subtype3 66 29 1 18 3 0 2 0 0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0454 (Fisher's exact test), Q value = 0.12

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

nPatients T1 T2 T3 T4
ALL 183 95 77 12
subtype1 50 16 20 4
subtype2 63 49 32 7
subtype3 70 30 25 1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.468 (Fisher's exact test), Q value = 0.66

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

nPatients 0 1
ALL 254 3
subtype1 63 1
subtype2 99 2
subtype3 92 0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.83 (Fisher's exact test), Q value = 0.88

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

nPatients 0 1
ALL 266 4
subtype1 58 1
subtype2 112 1
subtype3 96 2

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.672 (Fisher's exact test), Q value = 0.77

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

nPatients FEMALE MALE
ALL 118 252
subtype1 29 64
subtype2 52 99
subtype3 37 89

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.835 (Fisher's exact test), Q value = 0.88

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

nPatients NO YES
ALL 338 9
subtype1 83 3
subtype2 139 3
subtype3 116 3

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.101 (Fisher's exact test), Q value = 0.23

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 3 360 7
subtype1 3 89 1
subtype2 0 148 3
subtype3 0 123 3

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.372 (Fisher's exact test), Q value = 0.58

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

nPatients R0 R1 R2 RX
ALL 324 17 1 21
subtype1 79 4 0 7
subtype2 131 7 0 11
subtype3 114 6 1 3

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0158 (Fisher's exact test), Q value = 0.059

Table S12.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 158 17 183
subtype1 1 27 6 58
subtype2 0 73 8 68
subtype3 1 58 3 57

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.26 (Fisher's exact test), Q value = 0.45

Table S13.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 16 335
subtype1 2 85
subtype2 10 135
subtype3 4 115

Figure S12.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S14.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 93 163 121
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.953 (logrank test), Q value = 0.96

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 371 132 0.0 - 120.8 (19.8)
subtype1 90 33 0.0 - 108.8 (20.8)
subtype2 162 57 0.2 - 120.8 (20.2)
subtype3 119 42 0.2 - 114.3 (18.3)

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 9.51e-09 (Kruskal-Wallis (anova)), Q value = 1.1e-06

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 373 59.3 (13.4)
subtype1 91 57.9 (15.2)
subtype2 162 63.8 (9.9)
subtype3 120 54.2 (14.2)

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.737 (Fisher's exact test), Q value = 0.82

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 175 87 3 65 9 9 2 1 2
subtype1 42 19 0 19 4 4 0 1 0
subtype2 79 38 2 26 3 3 0 0 1
subtype3 54 30 1 20 2 2 2 0 1

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.956 (Fisher's exact test), Q value = 0.96

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 185 95 81 13
subtype1 43 22 23 3
subtype2 84 41 31 6
subtype3 58 32 27 4

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.573 (Fisher's exact test), Q value = 0.72

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 257 4
subtype1 65 2
subtype2 107 1
subtype3 85 1

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.287 (Fisher's exact test), Q value = 0.47

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 272 4
subtype1 64 0
subtype2 117 1
subtype3 91 3

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0156 (Fisher's exact test), Q value = 0.059

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 122 255
subtype1 34 59
subtype2 40 123
subtype3 48 73

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.565 (Fisher's exact test), Q value = 0.71

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 345 9
subtype1 85 1
subtype2 152 4
subtype3 108 4

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0184 (Fisher's exact test), Q value = 0.067

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 3 367 7
subtype1 3 87 3
subtype2 0 162 1
subtype3 0 118 3

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.547 (Fisher's exact test), Q value = 0.71

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 330 17 1 22
subtype1 76 5 0 8
subtype2 145 8 0 7
subtype3 109 4 1 7

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00109 (Fisher's exact test), Q value = 0.0087

Table S25.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 161 17 187
subtype1 0 29 9 52
subtype2 1 66 7 85
subtype3 1 66 1 50

Figure S23.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.584 (Fisher's exact test), Q value = 0.72

Table S26.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 340
subtype1 4 84
subtype2 6 146
subtype3 8 110

Figure S24.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S27.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 41 75 19 23 24 2
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.563 (logrank test), Q value = 0.71

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 180 93 0.2 - 114.3 (17.6)
subtype1 41 24 0.2 - 113.0 (13.6)
subtype2 75 35 0.3 - 108.8 (20.0)
subtype3 19 12 0.3 - 114.3 (26.9)
subtype4 22 9 0.5 - 66.3 (13.4)
subtype5 23 13 0.3 - 58.5 (14.6)

Figure S25.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00207 (Kruskal-Wallis (anova)), Q value = 0.013

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 180 60.2 (14.8)
subtype1 39 61.9 (11.8)
subtype2 75 64.5 (12.8)
subtype3 19 56.5 (17.0)
subtype4 23 53.8 (15.7)
subtype5 24 53.3 (18.1)

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00086

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVB
ALL 70 38 3 44 6 6 2 1
subtype1 9 10 0 11 4 4 0 0
subtype2 43 10 2 13 0 0 1 0
subtype3 6 6 0 4 0 0 0 1
subtype4 9 3 0 9 0 0 1 0
subtype5 3 9 1 7 2 2 0 0

Figure S27.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00267 (Fisher's exact test), Q value = 0.015

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 73 43 56 9
subtype1 10 12 15 4
subtype2 44 11 17 2
subtype3 7 6 5 1
subtype4 9 5 9 0
subtype5 3 9 10 2

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.266 (Fisher's exact test), Q value = 0.45

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 110 3
subtype1 25 2
subtype2 37 0
subtype3 17 0
subtype4 15 0
subtype5 16 1

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.286 (Fisher's exact test), Q value = 0.47

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 130 3
subtype1 35 0
subtype2 46 1
subtype3 16 1
subtype4 13 1
subtype5 20 0

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.254 (Fisher's exact test), Q value = 0.45

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 69 113
subtype1 16 25
subtype2 22 53
subtype3 9 10
subtype4 12 11
subtype5 10 14

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.492 (Fisher's exact test), Q value = 0.66

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 163 3
subtype1 38 1
subtype2 64 1
subtype3 16 1
subtype4 23 0
subtype5 22 0

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.492 (Fisher's exact test), Q value = 0.66

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 178 2
subtype1 1 38 2
subtype2 1 74 0
subtype3 0 19 0
subtype4 0 23 0
subtype5 0 24 0

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.532 (Fisher's exact test), Q value = 0.69

Table S37.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 147 11 1 16
subtype1 30 5 0 3
subtype2 61 5 1 4
subtype3 17 0 0 2
subtype4 20 0 0 3
subtype5 19 1 0 4

Figure S34.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.00106 (Fisher's exact test), Q value = 0.0087

Table S38.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 52 12 111
subtype1 14 0 24
subtype2 9 7 56
subtype3 9 1 9
subtype4 10 1 11
subtype5 10 3 11

Figure S35.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.152 (Fisher's exact test), Q value = 0.32

Table S39.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 161
subtype1 2 34
subtype2 2 67
subtype3 0 19
subtype4 3 18
subtype5 0 23

Figure S36.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S40.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 41 47 38 19 39
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0429 (logrank test), Q value = 0.12

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 182 94 0.2 - 114.3 (17.8)
subtype1 40 19 0.3 - 114.3 (19.1)
subtype2 47 21 0.3 - 108.8 (19.8)
subtype3 37 22 0.2 - 81.7 (12.3)
subtype4 19 13 0.4 - 60.9 (13.6)
subtype5 39 19 0.3 - 90.7 (21.7)

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0495 (Kruskal-Wallis (anova)), Q value = 0.13

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 182 60.4 (14.9)
subtype1 41 56.2 (18.3)
subtype2 47 63.2 (12.6)
subtype3 36 60.9 (10.3)
subtype4 19 53.8 (17.7)
subtype5 39 64.3 (13.8)

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00159 (Fisher's exact test), Q value = 0.012

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVB
ALL 71 38 3 45 6 6 2 1
subtype1 11 10 1 10 2 4 1 1
subtype2 28 6 1 6 0 0 1 0
subtype3 7 12 0 11 3 1 0 0
subtype4 4 5 0 8 1 0 0 0
subtype5 21 5 1 10 0 1 0 0

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00191 (Fisher's exact test), Q value = 0.013

Table S44.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 74 43 57 9
subtype1 11 12 15 3
subtype2 29 7 9 1
subtype3 8 14 14 2
subtype4 4 5 8 2
subtype5 22 5 11 1

Figure S40.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.725 (Fisher's exact test), Q value = 0.81

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 112 3
subtype1 32 2
subtype2 20 0
subtype3 22 1
subtype4 12 0
subtype5 26 0

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.643 (Fisher's exact test), Q value = 0.76

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 132 3
subtype1 32 2
subtype2 28 1
subtype3 32 0
subtype4 14 0
subtype5 26 0

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0424 (Fisher's exact test), Q value = 0.12

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 70 114
subtype1 24 17
subtype2 13 34
subtype3 13 25
subtype4 7 12
subtype5 13 26

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.856 (Fisher's exact test), Q value = 0.89

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 165 3
subtype1 39 0
subtype2 40 1
subtype3 34 1
subtype4 18 0
subtype5 34 1

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0921 (Fisher's exact test), Q value = 0.22

Table S49.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 180 2
subtype1 0 39 2
subtype2 0 47 0
subtype3 0 38 0
subtype4 0 19 0
subtype5 2 37 0

Figure S45.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.608 (Fisher's exact test), Q value = 0.73

Table S50.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 148 12 1 16
subtype1 34 2 0 4
subtype2 37 4 1 2
subtype3 27 4 0 6
subtype4 17 0 0 2
subtype5 33 2 0 2

Figure S46.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.00265 (Fisher's exact test), Q value = 0.015

Table S51.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 52 12 113
subtype1 15 1 25
subtype2 4 4 36
subtype3 17 1 18
subtype4 8 2 9
subtype5 8 4 25

Figure S47.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.796 (Fisher's exact test), Q value = 0.85

Table S52.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 162
subtype1 3 37
subtype2 1 42
subtype3 1 33
subtype4 1 17
subtype5 2 33

Figure S48.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S53.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 68 88 74 71 70
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00485 (logrank test), Q value = 0.022

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 365 130 0.0 - 120.8 (19.6)
subtype1 65 32 0.3 - 120.8 (13.6)
subtype2 88 29 0.3 - 102.7 (22.9)
subtype3 72 26 0.2 - 114.3 (19.4)
subtype4 70 22 0.0 - 107.1 (20.9)
subtype5 70 21 0.4 - 108.8 (19.0)

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 4.66e-06 (Kruskal-Wallis (anova)), Q value = 0.00019

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 367 59.3 (13.4)
subtype1 66 60.7 (13.3)
subtype2 87 62.7 (12.4)
subtype3 73 52.5 (13.8)
subtype4 71 58.8 (12.9)
subtype5 70 61.2 (12.8)

Figure S50.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00718 (Fisher's exact test), Q value = 0.032

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 171 86 3 65 8 9 2 1 2
subtype1 17 25 0 14 3 3 0 0 1
subtype2 55 13 1 12 1 0 0 0 0
subtype3 33 17 1 14 1 2 1 0 1
subtype4 35 13 0 9 2 3 1 1 0
subtype5 31 18 1 16 1 1 0 0 0

Figure S51.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00051 (Fisher's exact test), Q value = 0.0056

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 181 94 80 13
subtype1 17 29 17 5
subtype2 57 14 14 2
subtype3 37 18 18 1
subtype4 38 15 13 4
subtype5 32 18 18 1

Figure S52.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.413 (Fisher's exact test), Q value = 0.62

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 252 4
subtype1 40 1
subtype2 61 0
subtype3 59 2
subtype4 41 1
subtype5 51 0

Figure S53.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.495 (Fisher's exact test), Q value = 0.66

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 266 4
subtype1 46 1
subtype2 64 0
subtype3 61 2
subtype4 43 1
subtype5 52 0

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 0.00024

Table S60.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 121 250
subtype1 20 48
subtype2 23 65
subtype3 38 36
subtype4 30 41
subtype5 10 60

Figure S55.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0728 (Fisher's exact test), Q value = 0.18

Table S61.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 339 9
subtype1 64 1
subtype2 81 1
subtype3 69 0
subtype4 63 2
subtype5 62 5

Figure S56.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00319 (Fisher's exact test), Q value = 0.017

Table S62.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 3 361 7
subtype1 0 66 2
subtype2 0 88 0
subtype3 0 73 1
subtype4 3 64 4
subtype5 0 70 0

Figure S57.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.014 (Fisher's exact test), Q value = 0.056

Table S63.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 324 17 1 22
subtype1 54 6 0 7
subtype2 78 3 0 6
subtype3 69 1 0 4
subtype4 56 6 1 5
subtype5 67 1 0 0

Figure S58.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.00079 (Fisher's exact test), Q value = 0.0079

Table S64.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 158 17 184
subtype1 1 25 8 32
subtype2 0 32 3 48
subtype3 0 48 1 24
subtype4 0 24 2 43
subtype5 1 29 3 37

Figure S59.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.767 (Fisher's exact test), Q value = 0.84

Table S65.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 334
subtype1 4 58
subtype2 6 76
subtype3 3 70
subtype4 3 64
subtype5 2 66

Figure S60.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S66.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 55 56 116 87 57
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00337 (logrank test), Q value = 0.017

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 365 130 0.0 - 120.8 (19.6)
subtype1 53 29 0.2 - 107.1 (13.1)
subtype2 56 17 0.0 - 90.3 (19.2)
subtype3 116 42 0.3 - 120.8 (21.2)
subtype4 83 27 0.2 - 114.3 (19.6)
subtype5 57 15 0.4 - 108.8 (18.7)

Figure S61.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.46e-05 (Kruskal-Wallis (anova)), Q value = 0.00029

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 367 59.3 (13.4)
subtype1 53 61.3 (11.3)
subtype2 55 61.9 (9.6)
subtype3 115 61.9 (11.7)
subtype4 87 51.7 (16.0)
subtype5 57 60.9 (13.6)

Figure S62.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00211 (Fisher's exact test), Q value = 0.013

Table S69.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 171 86 3 65 8 9 2 1 2
subtype1 11 17 0 15 4 3 0 0 0
subtype2 33 10 0 3 1 2 0 0 0
subtype3 62 25 1 17 2 1 0 0 1
subtype4 41 19 1 17 0 2 2 1 1
subtype5 24 15 1 13 1 1 0 0 0

Figure S63.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00014 (Fisher's exact test), Q value = 0.0017

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 181 94 80 13
subtype1 11 20 20 4
subtype2 37 11 5 2
subtype3 63 28 19 5
subtype4 45 20 21 1
subtype5 25 15 15 1

Figure S64.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.172 (Fisher's exact test), Q value = 0.34

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 252 4
subtype1 33 1
subtype2 31 0
subtype3 77 0
subtype4 67 3
subtype5 44 0

Figure S65.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.488 (Fisher's exact test), Q value = 0.66

Table S72.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 266 4
subtype1 39 0
subtype2 32 0
subtype3 83 1
subtype4 68 3
subtype5 44 0

Figure S66.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 6e-05 (Fisher's exact test), Q value = 9e-04

Table S73.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 121 250
subtype1 23 32
subtype2 13 43
subtype3 35 81
subtype4 42 45
subtype5 8 49

Figure S67.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.22 (Fisher's exact test), Q value = 0.4

Table S74.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 339 9
subtype1 52 1
subtype2 52 0
subtype3 107 2
subtype4 77 2
subtype5 51 4

Figure S68.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00096 (Fisher's exact test), Q value = 0.0087

Table S75.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 3 361 7
subtype1 0 50 5
subtype2 0 56 0
subtype3 0 114 2
subtype4 3 84 0
subtype5 0 57 0

Figure S69.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0242 (Fisher's exact test), Q value = 0.083

Table S76.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 324 17 1 22
subtype1 43 7 0 4
subtype2 46 4 0 4
subtype3 102 4 0 8
subtype4 79 1 1 6
subtype5 54 1 0 0

Figure S70.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0264 (Fisher's exact test), Q value = 0.088

Table S77.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 158 17 184
subtype1 0 26 1 27
subtype2 0 14 4 35
subtype3 0 45 6 59
subtype4 1 50 2 34
subtype5 1 23 4 29

Figure S71.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.608 (Fisher's exact test), Q value = 0.73

Table S78.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 334
subtype1 3 51
subtype2 4 51
subtype3 7 98
subtype4 3 80
subtype5 1 54

Figure S72.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S79.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4 5 6
Number of samples 40 64 92 85 50 41
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.384 (logrank test), Q value = 0.58

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 366 128 0.0 - 120.8 (19.8)
subtype1 38 17 0.2 - 120.8 (14.6)
subtype2 64 23 0.3 - 113.0 (21.9)
subtype3 92 26 0.0 - 114.3 (21.3)
subtype4 84 27 0.3 - 108.8 (18.1)
subtype5 49 19 0.4 - 83.2 (24.5)
subtype6 39 16 0.2 - 89.7 (18.2)

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 4.53e-06 (Kruskal-Wallis (anova)), Q value = 0.00019

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 368 59.2 (13.4)
subtype1 38 61.5 (11.3)
subtype2 63 64.8 (10.6)
subtype3 92 57.0 (13.1)
subtype4 85 61.9 (11.9)
subtype5 50 54.0 (16.1)
subtype6 40 54.1 (14.5)

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.0855 (Fisher's exact test), Q value = 0.21

Table S82.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 172 86 3 64 9 9 2 1 2
subtype1 19 6 0 10 2 1 0 0 1
subtype2 26 20 1 8 3 1 0 0 0
subtype3 51 17 0 9 0 2 1 1 1
subtype4 44 19 0 14 3 2 0 0 0
subtype5 18 14 2 11 0 2 1 0 0
subtype6 14 10 0 12 1 1 0 0 0

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0361 (Fisher's exact test), Q value = 0.11

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 182 94 80 13
subtype1 19 7 12 2
subtype2 27 23 11 3
subtype3 57 17 12 4
subtype4 45 19 17 3
subtype5 20 15 14 1
subtype6 14 13 14 0

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.944 (Fisher's exact test), Q value = 0.96

Table S84.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 254 4
subtype1 28 0
subtype2 40 0
subtype3 55 1
subtype4 62 1
subtype5 38 1
subtype6 31 1

Figure S77.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.308 (Fisher's exact test), Q value = 0.5

Table S85.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 269 4
subtype1 24 1
subtype2 45 0
subtype3 60 2
subtype4 66 0
subtype5 40 1
subtype6 34 0

Figure S78.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00201 (Fisher's exact test), Q value = 0.013

Table S86.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 119 253
subtype1 14 26
subtype2 18 46
subtype3 29 63
subtype4 16 69
subtype5 19 31
subtype6 23 18

Figure S79.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.594 (Fisher's exact test), Q value = 0.73

Table S87.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 340 9
subtype1 39 0
subtype2 61 1
subtype3 83 2
subtype4 75 4
subtype5 46 2
subtype6 36 0

Figure S80.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0557 (Fisher's exact test), Q value = 0.14

Table S88.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 3 362 7
subtype1 0 40 0
subtype2 0 63 1
subtype3 3 88 1
subtype4 0 85 0
subtype5 0 47 3
subtype6 0 39 2

Figure S81.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.485 (Fisher's exact test), Q value = 0.66

Table S89.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 325 17 1 22
subtype1 32 2 0 3
subtype2 53 3 0 6
subtype3 78 5 1 7
subtype4 79 3 0 2
subtype5 45 1 0 4
subtype6 38 3 0 0

Figure S82.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.131 (Fisher's exact test), Q value = 0.29

Table S90.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 161 17 182
subtype1 0 16 5 19
subtype2 0 20 4 36
subtype3 0 35 5 47
subtype4 1 42 2 40
subtype5 0 27 1 22
subtype6 1 21 0 18

Figure S83.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.173 (Fisher's exact test), Q value = 0.34

Table S91.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 335
subtype1 2 36
subtype2 4 55
subtype3 3 82
subtype4 1 82
subtype5 5 44
subtype6 3 36

Figure S84.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S92.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 44 97 57 126 48
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0555 (logrank test), Q value = 0.14

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 366 128 0.0 - 120.8 (19.8)
subtype1 42 18 0.0 - 120.8 (16.9)
subtype2 97 34 0.3 - 102.7 (21.8)
subtype3 55 26 0.3 - 113.0 (16.0)
subtype4 124 40 0.2 - 114.3 (21.5)
subtype5 48 10 0.3 - 108.8 (17.9)

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 6.58e-06 (Kruskal-Wallis (anova)), Q value = 2e-04

Table S94.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 368 59.2 (13.4)
subtype1 43 61.6 (11.0)
subtype2 95 62.8 (11.7)
subtype3 57 59.0 (13.6)
subtype4 125 54.1 (14.8)
subtype5 48 63.6 (9.9)

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.948 (Fisher's exact test), Q value = 0.96

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 172 86 3 64 9 9 2 1 2
subtype1 21 7 0 11 2 1 0 0 1
subtype2 45 23 1 11 4 1 0 0 0
subtype3 26 14 0 12 1 1 0 0 0
subtype4 57 30 2 21 1 5 2 1 1
subtype5 23 12 0 9 1 1 0 0 0

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.88 (Fisher's exact test), Q value = 0.91

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 182 94 80 13
subtype1 21 8 13 2
subtype2 49 26 17 5
subtype3 28 15 14 0
subtype4 60 33 26 5
subtype5 24 12 10 1

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.532 (Fisher's exact test), Q value = 0.69

Table S97.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 254 4
subtype1 31 0
subtype2 59 0
subtype3 39 1
subtype4 88 3
subtype5 37 0

Figure S89.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.351 (Fisher's exact test), Q value = 0.55

Table S98.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 269 4
subtype1 28 1
subtype2 65 0
subtype3 43 0
subtype4 94 3
subtype5 39 0

Figure S90.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0287 (Fisher's exact test), Q value = 0.093

Table S99.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 119 253
subtype1 14 30
subtype2 29 68
subtype3 19 38
subtype4 50 76
subtype5 7 41

Figure S91.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.14 (Fisher's exact test), Q value = 0.3

Table S100.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 340 9
subtype1 41 1
subtype2 93 0
subtype3 50 1
subtype4 113 4
subtype5 43 3

Figure S92.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.691 (Fisher's exact test), Q value = 0.79

Table S101.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 3 362 7
subtype1 0 44 0
subtype2 0 95 2
subtype3 0 56 1
subtype4 3 119 4
subtype5 0 48 0

Figure S93.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.702 (Fisher's exact test), Q value = 0.79

Table S102.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 325 17 1 22
subtype1 35 3 0 3
subtype2 83 5 0 6
subtype3 50 2 0 5
subtype4 111 6 1 8
subtype5 46 1 0 0

Figure S94.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.268 (Fisher's exact test), Q value = 0.45

Table S103.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 161 17 182
subtype1 0 18 5 21
subtype2 0 34 5 52
subtype3 0 32 2 22
subtype4 1 56 4 62
subtype5 1 21 1 25

Figure S95.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.446 (Fisher's exact test), Q value = 0.65

Table S104.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 335
subtype1 3 40
subtype2 4 86
subtype3 3 52
subtype4 8 112
subtype5 0 45

Figure S96.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S105.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 110 81 104 51
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.101 (logrank test), Q value = 0.23

Table S106.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 340 120 0.0 - 120.8 (19.8)
subtype1 107 46 0.3 - 120.8 (17.6)
subtype2 80 28 0.2 - 107.1 (21.4)
subtype3 104 31 0.3 - 108.8 (21.2)
subtype4 49 15 0.0 - 90.3 (17.1)

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00393 (Kruskal-Wallis (anova)), Q value = 0.019

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 342 59.2 (13.1)
subtype1 108 58.6 (14.0)
subtype2 80 58.1 (12.4)
subtype3 104 63.1 (9.8)
subtype4 50 54.3 (16.2)

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0304 (Fisher's exact test), Q value = 0.096

Table S108.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 157 83 3 59 7 8 2 1 2
subtype1 47 26 0 26 5 5 0 0 0
subtype2 38 17 0 10 0 3 1 1 1
subtype3 51 26 1 14 0 0 0 0 1
subtype4 21 14 2 9 2 0 1 0 0

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.224 (Fisher's exact test), Q value = 0.4

Table S109.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 167 91 73 12
subtype1 48 27 30 5
subtype2 42 20 13 5
subtype3 55 29 16 2
subtype4 22 15 14 0

Figure S100.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.431 (Fisher's exact test), Q value = 0.64

Table S110.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 238 4
subtype1 81 3
subtype2 48 1
subtype3 70 0
subtype4 39 0

Figure S101.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.224 (Fisher's exact test), Q value = 0.4

Table S111.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 255 4
subtype1 88 0
subtype2 49 2
subtype3 75 1
subtype4 43 1

Figure S102.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0396 (Fisher's exact test), Q value = 0.12

Table S112.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 115 231
subtype1 45 65
subtype2 27 54
subtype3 24 80
subtype4 19 32

Figure S103.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.669 (Fisher's exact test), Q value = 0.77

Table S113.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 315 9
subtype1 101 3
subtype2 73 2
subtype3 94 4
subtype4 47 0

Figure S104.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0318 (Fisher's exact test), Q value = 0.098

Table S114.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 337 7
subtype1 0 108 2
subtype2 2 77 2
subtype3 0 104 0
subtype4 0 48 3

Figure S105.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.153 (Fisher's exact test), Q value = 0.32

Table S115.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 303 17 1 19
subtype1 96 5 0 6
subtype2 65 8 1 5
subtype3 97 3 0 3
subtype4 45 1 0 5

Figure S106.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.462 (Fisher's exact test), Q value = 0.66

Table S116.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 155 16 163
subtype1 1 56 3 48
subtype2 0 31 4 42
subtype3 1 40 7 52
subtype4 0 28 2 21

Figure S107.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.756 (Fisher's exact test), Q value = 0.83

Table S117.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 16 312
subtype1 5 98
subtype2 5 72
subtype3 3 94
subtype4 3 48

Figure S108.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S118.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 73 84 101 34 54
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.00965 (logrank test), Q value = 0.04

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 340 120 0.0 - 120.8 (19.8)
subtype1 69 34 0.3 - 107.1 (16.0)
subtype2 84 30 0.3 - 113.0 (21.8)
subtype3 100 32 0.2 - 114.3 (21.4)
subtype4 33 14 0.0 - 120.8 (15.5)
subtype5 54 10 0.3 - 108.8 (19.4)

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 7.4e-05 (Kruskal-Wallis (anova)), Q value = 0.00099

Table S120.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 342 59.2 (13.1)
subtype1 72 54.8 (13.1)
subtype2 82 63.2 (11.1)
subtype3 100 56.4 (15.0)
subtype4 34 62.6 (11.1)
subtype5 54 62.4 (10.4)

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.133 (Fisher's exact test), Q value = 0.29

Table S121.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 157 83 3 59 7 8 2 1 2
subtype1 27 17 0 20 2 2 0 0 1
subtype2 39 20 1 11 4 1 0 0 0
subtype3 44 25 2 13 0 4 2 1 0
subtype4 19 4 0 8 1 1 0 0 1
subtype5 28 17 0 7 0 0 0 0 0

Figure S111.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.178 (Fisher's exact test), Q value = 0.35

Table S122.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 167 91 73 12
subtype1 28 19 23 3
subtype2 42 23 15 4
subtype3 49 28 19 3
subtype4 19 4 9 2
subtype5 29 17 7 0

Figure S112.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.449 (Fisher's exact test), Q value = 0.65

Table S123.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 238 4
subtype1 50 1
subtype2 51 0
subtype3 68 3
subtype4 27 0
subtype5 42 0

Figure S113.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.384 (Fisher's exact test), Q value = 0.58

Table S124.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 255 4
subtype1 54 1
subtype2 59 0
subtype3 73 2
subtype4 23 1
subtype5 46 0

Figure S114.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.021 (Fisher's exact test), Q value = 0.074

Table S125.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 115 231
subtype1 24 49
subtype2 24 60
subtype3 44 57
subtype4 13 21
subtype5 10 44

Figure S115.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0991 (Fisher's exact test), Q value = 0.23

Table S126.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 315 9
subtype1 63 3
subtype2 79 1
subtype3 94 1
subtype4 32 0
subtype5 47 4

Figure S116.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.331 (Fisher's exact test), Q value = 0.53

Table S127.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 337 7
subtype1 0 72 1
subtype2 0 83 1
subtype3 2 94 5
subtype4 0 34 0
subtype5 0 54 0

Figure S117.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.214 (Fisher's exact test), Q value = 0.4

Table S128.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 303 17 1 19
subtype1 66 4 0 3
subtype2 73 2 0 7
subtype3 85 7 1 8
subtype4 27 3 0 1
subtype5 52 1 0 0

Figure S118.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.00933 (Fisher's exact test), Q value = 0.04

Table S129.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 155 16 163
subtype1 1 45 0 27
subtype2 0 27 7 44
subtype3 0 43 5 50
subtype4 0 13 3 18
subtype5 1 27 1 24

Figure S119.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.782 (Fisher's exact test), Q value = 0.85

Table S130.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 16 312
subtype1 5 66
subtype2 4 73
subtype3 3 93
subtype4 2 31
subtype5 2 49

Figure S120.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LIHC-TP/26874095/LIHC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LIHC-TP/26850637/LIHC-TP.merged_data.txt

  • Number of patients = 377

  • Number of clustering approaches = 10

  • Number of selected clinical features = 12

  • 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

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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] 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)
[5] 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)