Correlation between aggregated molecular cancer subtypes and selected clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1DJ5DQK
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DJ5DQK
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 11 clinical features across 363 patients, 31 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 'GENDER' and 'RACE'.

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

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

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

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH'.

  • 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',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE', and 'GENDER'.

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 31 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 0.23
(0.43) 		0.503
(0.671) 		0.00702
(0.0346) 		0.0348
(0.108) 		0.449
(0.619) 		0.0783
(0.215) 		0.339
(0.499) 		0.0842
(0.225)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.18
(0.354) 		2.48e-07
(2.18e-05) 		2.15e-06
(9.44e-05) 		0.000138
(0.00154) 		0.00388
(0.0263) 		2.37e-05
(0.000522) 		0.00707
(0.0346) 		3.13e-05
(0.000551)
NEOPLASM DISEASESTAGE Fisher's exact test 0.247
(0.443) 		0.89
(0.959) 		0.0421
(0.119) 		0.00195
(0.0143) 		0.181
(0.354) 		0.946
(0.991) 		0.00034
(0.00332) 		0.0888
(0.227)
PATHOLOGY T STAGE Fisher's exact test 0.17
(0.354) 		0.868
(0.955) 		0.00512
(0.0322) 		0.00014
(0.00154) 		0.894
(0.959) 		0.856
(0.954) 		0.011
(0.0461) 		0.108
(0.265)
PATHOLOGY N STAGE Fisher's exact test 1
(1.00) 		1
(1.00) 		0.628
(0.789) 		0.279
(0.481) 		0.677
(0.806) 		1
(1.00) 		0.159
(0.342) 		0.944
(0.991)
PATHOLOGY M STAGE Fisher's exact test 1
(1.00) 		0.271
(0.478) 		0.608
(0.787) 		0.472
(0.639) 		0.239
(0.437) 		0.31
(0.496) 		0.286
(0.484) 		0.151
(0.332)
GENDER Fisher's exact test 0.0283
(0.0957) 		0.0368
(0.108) 		1e-05
(0.000293) 		0.00011
(0.00154) 		0.292
(0.486) 		0.0207
(0.0792) 		0.0225
(0.0824) 		0.0367
(0.108)
HISTOLOGICAL TYPE Fisher's exact test 0.668
(0.806) 		0.148
(0.332) 		0.00662
(0.0346) 		0.00181
(0.0143) 		0.0904
(0.227) 		0.836
(0.943) 		0.116
(0.276) 		0.605
(0.787)
COMPLETENESS OF RESECTION Fisher's exact test 0.331
(0.499) 		0.617
(0.787) 		0.00847
(0.0392) 		0.0266
(0.0936) 		0.658
(0.806) 		0.326
(0.499) 		0.184
(0.354) 		0.185
(0.354)
RACE Fisher's exact test 0.0321
(0.105) 		0.00124
(0.0109) 		0.00652
(0.0346) 		0.0202
(0.0792) 		0.376
(0.534) 		0.119
(0.276) 		0.798
(0.912) 		0.00935
(0.0411)
ETHNICITY Fisher's exact test 0.346
(0.499) 		0.45
(0.619) 		0.34
(0.499) 		0.324
(0.499) 		0.303
(0.493) 		0.674
(0.806) 		0.703
(0.824) 		0.722
(0.837)
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 106 119 132
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.23 (logrank test), Q value = 0.43

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

nPatients nDeath Duration Range (Median), Month
ALL 334 109 0.0 - 120.8 (19.3)
subtype1 104 35 0.1 - 114.3 (18.4)
subtype2 106 38 0.0 - 120.8 (18.1)
subtype3 124 36 0.1 - 108.8 (21.0)

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 'YEARS_TO_BIRTH'

P value = 0.18 (Kruskal-Wallis (anova)), Q value = 0.35

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

nPatients Mean (Std.Dev)
ALL 354 59.8 (12.7)
subtype1 105 61.4 (11.2)
subtype2 117 58.2 (13.0)
subtype3 132 59.8 (13.5)

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 'NEOPLASM_DISEASESTAGE'

P value = 0.247 (Fisher's exact test), Q value = 0.44

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

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 168 84 3 59 8 7 3 1 2
subtype1 58 25 0 14 1 0 1 1 0
subtype2 47 33 1 22 3 2 2 0 1
subtype3 63 26 2 23 4 5 0 0 1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.17 (Fisher's exact test), Q value = 0.35

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

nPatients T0+T1 T2 T3 T4
ALL 179 91 73 12
subtype1 61 26 17 1
subtype2 50 36 27 6
subtype3 68 29 29 5

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

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

nPatients 0 1
ALL 249 3
subtype1 73 1
subtype2 86 1
subtype3 90 1

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

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

nPatients 0 1
ALL 260 4
subtype1 77 1
subtype2 91 2
subtype3 92 1

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 = 0.0283 (Fisher's exact test), Q value = 0.096

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

nPatients FEMALE MALE
ALL 113 244
subtype1 23 83
subtype2 43 76
subtype3 47 85

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 'HISTOLOGICAL_TYPE'

P value = 0.668 (Fisher's exact test), Q value = 0.81

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 348 7
subtype1 0 104 2
subtype2 0 116 3
subtype3 2 128 2

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.331 (Fisher's exact test), Q value = 0.5

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

nPatients R0 R1 R2 RX
ALL 315 15 1 19
subtype1 95 6 1 3
subtype2 103 6 0 9
subtype3 117 3 0 7

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0321 (Fisher's exact test), Q value = 0.1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 156 17 173
subtype1 1 49 5 46
subtype2 0 63 4 51
subtype3 0 44 8 76

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.346 (Fisher's exact test), Q value = 0.5

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 329
subtype1 5 96
subtype2 4 112
subtype3 2 121

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S13.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 82 166 115
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.503 (logrank test), Q value = 0.67

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 338 111 0.0 - 120.8 (19.3)
subtype1 75 24 0.1 - 108.8 (20.8)
subtype2 157 53 0.0 - 120.8 (19.0)
subtype3 106 34 0.1 - 114.3 (19.4)

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 2.48e-07 (Kruskal-Wallis (anova)), Q value = 2.2e-05

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 360 59.6 (13.0)
subtype1 82 58.3 (14.0)
subtype2 164 63.6 (10.0)
subtype3 114 54.8 (14.3)

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.89 (Fisher's exact test), Q value = 0.96

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 169 84 3 62 8 9 3 1 2
subtype1 38 16 0 17 3 4 0 0 0
subtype2 79 40 2 25 4 3 1 1 1
subtype3 52 28 1 20 1 2 2 0 1

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.868 (Fisher's exact test), Q value = 0.96

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 180 91 77 13
subtype1 40 18 20 3
subtype2 85 43 30 7
subtype3 55 30 27 3

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

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

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 252 4
subtype1 56 1
subtype2 112 2
subtype3 84 1

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.271 (Fisher's exact test), Q value = 0.48

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 266 4
subtype1 58 0
subtype2 120 1
subtype3 88 3

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0368 (Fisher's exact test), Q value = 0.11

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 116 247
subtype1 29 53
subtype2 42 124
subtype3 45 70

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.148 (Fisher's exact test), Q value = 0.33

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 354 7
subtype1 2 78 2
subtype2 0 164 2
subtype3 0 112 3

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.617 (Fisher's exact test), Q value = 0.79

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 320 15 1 20
subtype1 68 3 0 7
subtype2 149 6 0 8
subtype3 103 6 1 5

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00124 (Fisher's exact test), Q value = 0.011

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 159 17 176
subtype1 0 26 8 45
subtype2 1 68 8 85
subtype3 0 65 1 46

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.45 (Fisher's exact test), Q value = 0.62

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 334
subtype1 2 76
subtype2 4 151
subtype3 6 107

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S25.  Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 59 97 70 65 66
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00702 (logrank test), Q value = 0.035

Table S26.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 332 109 0.0 - 120.8 (19.1)
subtype1 53 25 0.1 - 120.8 (13.6)
subtype2 95 31 0.1 - 102.7 (21.8)
subtype3 58 18 0.1 - 114.3 (19.7)
subtype4 64 19 0.0 - 107.1 (17.0)
subtype5 62 16 0.4 - 108.8 (18.5)

Figure S23.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 2.15e-06 (Kruskal-Wallis (anova)), Q value = 9.4e-05

Table S27.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 354 59.6 (13.0)
subtype1 58 61.6 (11.5)
subtype2 96 62.8 (12.5)
subtype3 69 52.4 (13.8)
subtype4 65 58.5 (13.0)
subtype5 66 61.8 (11.2)

Figure S24.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0421 (Fisher's exact test), Q value = 0.12

Table S28.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 165 83 3 62 7 9 3 1 2
subtype1 15 21 0 12 3 3 1 0 0
subtype2 56 16 1 12 2 0 0 1 1
subtype3 31 17 1 14 1 2 1 0 1
subtype4 33 12 0 10 0 3 1 0 0
subtype5 30 17 1 14 1 1 0 0 0

Figure S25.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00512 (Fisher's exact test), Q value = 0.032

Table S29.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 176 90 76 13
subtype1 15 25 15 4
subtype2 60 17 15 4
subtype3 34 17 18 1
subtype4 36 14 12 3
subtype5 31 17 16 1

Figure S26.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.628 (Fisher's exact test), Q value = 0.79

Table S30.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 247 4
subtype1 35 1
subtype2 64 1
subtype3 58 2
subtype4 39 0
subtype5 51 0

Figure S27.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.608 (Fisher's exact test), Q value = 0.79

Table S31.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 260 4
subtype1 42 0
subtype2 67 1
subtype3 59 2
subtype4 39 1
subtype5 53 0

Figure S28.  Get High-res Image Clustering Approach #3: '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 = 0.00029

Table S32.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 115 242
subtype1 17 42
subtype2 26 71
subtype3 37 33
subtype4 27 38
subtype5 8 58

Figure S29.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00662 (Fisher's exact test), Q value = 0.035

Table S33.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 348 7
subtype1 0 57 2
subtype2 0 97 0
subtype3 0 69 1
subtype4 2 59 4
subtype5 0 66 0

Figure S30.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.00847 (Fisher's exact test), Q value = 0.039

Table S34.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 314 15 1 20
subtype1 48 5 0 5
subtype2 84 3 0 8
subtype3 65 1 0 4
subtype4 53 6 1 3
subtype5 64 0 0 0

Figure S31.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.00652 (Fisher's exact test), Q value = 0.035

Table S35.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 156 17 173
subtype1 0 24 6 27
subtype2 0 35 5 52
subtype3 0 45 1 23
subtype4 0 22 2 39
subtype5 1 30 3 32

Figure S32.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.34 (Fisher's exact test), Q value = 0.5

Table S36.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 328
subtype1 3 51
subtype2 5 86
subtype3 2 67
subtype4 2 60
subtype5 0 64

Figure S33.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S37.  Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 52 55 113 82 55
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0348 (logrank test), Q value = 0.11

Table S38.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 332 109 0.0 - 120.8 (19.1)
subtype1 45 22 0.1 - 107.1 (13.5)
subtype2 55 16 0.0 - 90.3 (17.7)
subtype3 107 37 0.3 - 120.8 (20.7)
subtype4 73 21 0.1 - 114.3 (19.6)
subtype5 52 13 0.4 - 108.8 (17.3)

Figure S34.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000138 (Kruskal-Wallis (anova)), Q value = 0.0015

Table S39.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 354 59.6 (13.0)
subtype1 51 61.0 (11.3)
subtype2 54 61.9 (9.7)
subtype3 112 62.0 (11.7)
subtype4 82 52.7 (15.6)
subtype5 55 61.5 (12.3)

Figure S35.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00195 (Fisher's exact test), Q value = 0.014

Table S40.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 165 83 3 62 7 9 3 1 2
subtype1 10 15 0 15 4 3 0 0 0
subtype2 31 10 0 3 1 2 1 1 0
subtype3 60 25 1 17 1 1 0 0 1
subtype4 40 18 1 16 0 2 2 0 1
subtype5 24 15 1 11 1 1 0 0 0

Figure S36.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00014 (Fisher's exact test), Q value = 0.0015

Table S41.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 176 90 76 13
subtype1 10 18 20 4
subtype2 37 11 5 2
subtype3 61 28 18 5
subtype4 43 18 20 1
subtype5 25 15 13 1

Figure S37.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.279 (Fisher's exact test), Q value = 0.48

Table S42.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 247 4
subtype1 33 1
subtype2 30 1
subtype3 76 0
subtype4 65 2
subtype5 43 0

Figure S38.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.472 (Fisher's exact test), Q value = 0.64

Table S43.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 260 4
subtype1 38 0
subtype2 32 0
subtype3 81 1
subtype4 65 3
subtype5 44 0

Figure S39.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00011 (Fisher's exact test), Q value = 0.0015

Table S44.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 115 242
subtype1 22 30
subtype2 13 42
subtype3 34 79
subtype4 39 43
subtype5 7 48

Figure S40.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00181 (Fisher's exact test), Q value = 0.014

Table S45.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 348 7
subtype1 0 47 5
subtype2 0 55 0
subtype3 0 111 2
subtype4 2 80 0
subtype5 0 55 0

Figure S41.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0266 (Fisher's exact test), Q value = 0.094

Table S46.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 314 15 1 20
subtype1 42 6 0 3
subtype2 46 4 0 3
subtype3 99 4 0 8
subtype4 74 1 1 6
subtype5 53 0 0 0

Figure S42.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0202 (Fisher's exact test), Q value = 0.079

Table S47.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 156 17 173
subtype1 0 25 1 25
subtype2 0 14 4 34
subtype3 0 45 6 56
subtype4 0 49 2 31
subtype5 1 23 4 27

Figure S43.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.324 (Fisher's exact test), Q value = 0.5

Table S48.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 328
subtype1 2 49
subtype2 4 50
subtype3 4 98
subtype4 2 78
subtype5 0 53

Figure S44.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S49.  Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4
Number of samples 62 127 49 120
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.449 (logrank test), Q value = 0.62

Table S50.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 333 107 0.0 - 120.8 (19.3)
subtype1 60 23 0.0 - 120.8 (19.0)
subtype2 117 39 0.3 - 108.8 (19.2)
subtype3 42 13 0.1 - 83.2 (24.7)
subtype4 114 32 0.1 - 114.3 (18.4)

Figure S45.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00388 (Kruskal-Wallis (anova)), Q value = 0.026

Table S51.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 355 59.6 (12.9)
subtype1 60 62.8 (10.9)
subtype2 127 61.2 (13.2)
subtype3 49 55.9 (14.5)
subtype4 119 57.7 (12.4)

Figure S46.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.181 (Fisher's exact test), Q value = 0.35

Table S52.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 166 83 3 61 8 9 3 1 2
subtype1 32 8 1 9 3 1 1 1 1
subtype2 59 32 0 26 3 3 0 0 0
subtype3 19 13 2 10 0 2 1 0 0
subtype4 56 30 0 16 2 3 1 0 1

Figure S47.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.894 (Fisher's exact test), Q value = 0.96

Table S53.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 177 90 76 13
subtype1 35 11 13 3
subtype2 60 33 29 4
subtype3 22 13 12 2
subtype4 60 33 22 4

Figure S48.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.677 (Fisher's exact test), Q value = 0.81

Table S54.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 249 4
subtype1 40 1
subtype2 91 1
subtype3 37 1
subtype4 81 1

Figure S49.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.239 (Fisher's exact test), Q value = 0.44

Table S55.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 263 4
subtype1 36 1
subtype2 101 0
subtype3 39 1
subtype4 87 2

Figure S50.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.292 (Fisher's exact test), Q value = 0.49

Table S56.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 113 245
subtype1 19 43
subtype2 33 94
subtype3 19 30
subtype4 42 78

Figure S51.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0904 (Fisher's exact test), Q value = 0.23

Table S57.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 349 7
subtype1 0 61 1
subtype2 0 127 0
subtype3 0 47 2
subtype4 2 114 4

Figure S52.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.658 (Fisher's exact test), Q value = 0.81

Table S58.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 315 15 1 20
subtype1 49 4 0 5
subtype2 115 4 0 7
subtype3 43 2 0 4
subtype4 108 5 1 4

Figure S53.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.376 (Fisher's exact test), Q value = 0.53

Table S59.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 159 17 171
subtype1 0 20 4 36
subtype2 1 58 6 59
subtype3 0 27 3 19
subtype4 0 54 4 57

Figure S54.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.303 (Fisher's exact test), Q value = 0.49

Table S60.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 329
subtype1 2 56
subtype2 2 119
subtype3 1 46
subtype4 7 108

Figure S55.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S61.  Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 41 96 56 118 47
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0783 (logrank test), Q value = 0.22

Table S62.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 333 107 0.0 - 120.8 (19.3)
subtype1 38 16 0.0 - 120.8 (14.3)
subtype2 90 29 0.1 - 102.7 (21.3)
subtype3 49 22 0.3 - 113.0 (17.6)
subtype4 111 31 0.1 - 114.3 (21.0)
subtype5 45 9 0.3 - 108.8 (16.4)

Figure S56.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 2.37e-05 (Kruskal-Wallis (anova)), Q value = 0.00052

Table S63.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 355 59.6 (12.9)
subtype1 41 62.0 (11.1)
subtype2 94 62.9 (11.8)
subtype3 56 58.7 (13.6)
subtype4 117 54.9 (13.9)
subtype5 47 63.4 (9.9)

Figure S57.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.946 (Fisher's exact test), Q value = 0.99

Table S64.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 166 83 3 61 8 9 3 1 2
subtype1 20 6 0 11 1 1 0 0 1
subtype2 44 22 1 11 4 1 1 1 0
subtype3 26 14 0 12 1 1 0 0 0
subtype4 53 29 2 19 1 5 2 0 1
subtype5 23 12 0 8 1 1 0 0 0

Figure S58.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.856 (Fisher's exact test), Q value = 0.95

Table S65.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 177 90 76 13
subtype1 20 7 12 2
subtype2 49 25 17 5
subtype3 28 14 14 0
subtype4 56 32 24 5
subtype5 24 12 9 1

Figure S59.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

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

Table S66.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 249 4
subtype1 30 0
subtype2 57 1
subtype3 39 1
subtype4 86 2
subtype5 37 0

Figure S60.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.31 (Fisher's exact test), Q value = 0.5

Table S67.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 263 4
subtype1 26 1
subtype2 64 0
subtype3 42 0
subtype4 92 3
subtype5 39 0

Figure S61.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0207 (Fisher's exact test), Q value = 0.079

Table S68.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 113 245
subtype1 13 28
subtype2 29 67
subtype3 19 37
subtype4 46 72
subtype5 6 41

Figure S62.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.836 (Fisher's exact test), Q value = 0.94

Table S69.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 349 7
subtype1 0 41 0
subtype2 0 94 2
subtype3 0 55 1
subtype4 2 112 4
subtype5 0 47 0

Figure S63.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.326 (Fisher's exact test), Q value = 0.5

Table S70.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 315 15 1 20
subtype1 32 3 0 3
subtype2 82 5 0 6
subtype3 50 1 0 5
subtype4 105 6 1 6
subtype5 46 0 0 0

Figure S64.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.119 (Fisher's exact test), Q value = 0.28

Table S71.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 159 17 171
subtype1 0 17 5 19
subtype2 0 33 5 52
subtype3 0 32 2 21
subtype4 0 56 4 55
subtype5 1 21 1 24

Figure S65.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.674 (Fisher's exact test), Q value = 0.81

Table S72.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 329
subtype1 1 39
subtype2 4 85
subtype3 3 51
subtype4 4 110
subtype5 0 44

Figure S66.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S73.  Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 91 84 118 41
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.339 (logrank test), Q value = 0.5

Table S74.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 312 101 0.0 - 120.8 (19.3)
subtype1 80 31 0.2 - 120.8 (17.3)
subtype2 82 27 0.1 - 107.1 (18.4)
subtype3 115 34 0.3 - 108.8 (20.7)
subtype4 35 9 0.0 - 90.3 (18.1)

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00707 (Kruskal-Wallis (anova)), Q value = 0.035

Table S75.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 331 59.6 (12.7)
subtype1 91 58.0 (13.4)
subtype2 83 58.9 (12.4)
subtype3 117 62.9 (10.2)
subtype4 40 55.1 (15.7)

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00034 (Fisher's exact test), Q value = 0.0033

Table S76.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 153 80 3 56 6 8 3 1 2
subtype1 33 22 0 25 5 5 0 0 0
subtype2 36 20 0 12 0 3 2 1 1
subtype3 66 27 1 12 0 0 0 0 1
subtype4 18 11 2 7 1 0 1 0 0

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.011 (Fisher's exact test), Q value = 0.046

Table S77.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 164 87 69 12
subtype1 34 23 29 5
subtype2 41 23 15 5
subtype3 70 30 14 2
subtype4 19 11 11 0

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.159 (Fisher's exact test), Q value = 0.34

Table S78.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 233 4
subtype1 65 3
subtype2 53 1
subtype3 83 0
subtype4 32 0

Figure S71.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.286 (Fisher's exact test), Q value = 0.48

Table S79.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 249 4
subtype1 73 0
subtype2 51 2
subtype3 89 1
subtype4 36 1

Figure S72.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0225 (Fisher's exact test), Q value = 0.082

Table S80.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 110 224
subtype1 38 53
subtype2 29 55
subtype3 27 91
subtype4 16 25

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.116 (Fisher's exact test), Q value = 0.28

Table S81.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 1 326 7
subtype1 0 88 3
subtype2 1 80 3
subtype3 0 118 0
subtype4 0 40 1

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.184 (Fisher's exact test), Q value = 0.35

Table S82.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 295 15 1 17
subtype1 78 5 0 5
subtype2 70 7 1 4
subtype3 111 2 0 4
subtype4 36 1 0 4

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.798 (Fisher's exact test), Q value = 0.91

Table S83.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 153 16 154
subtype1 0 45 4 41
subtype2 0 34 4 42
subtype3 1 50 7 55
subtype4 0 24 1 16

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.703 (Fisher's exact test), Q value = 0.82

Table S84.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 306
subtype1 5 81
subtype2 3 78
subtype3 3 107
subtype4 1 40

Figure S77.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S85.  Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 63 87 98 34 52
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0842 (logrank test), Q value = 0.22

Table S86.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 312 101 0.0 - 120.8 (19.3)
subtype1 53 23 0.1 - 107.1 (17.6)
subtype2 82 27 0.3 - 113.0 (22.0)
subtype3 94 27 0.1 - 114.3 (20.0)
subtype4 32 14 0.0 - 120.8 (13.7)
subtype5 51 10 0.3 - 79.4 (18.6)

Figure S78.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 3.13e-05 (Kruskal-Wallis (anova)), Q value = 0.00055

Table S87.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 331 59.6 (12.7)
subtype1 63 55.1 (12.7)
subtype2 85 63.1 (11.5)
subtype3 97 56.6 (14.0)
subtype4 34 63.2 (10.9)
subtype5 52 62.7 (9.6)

Figure S79.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0888 (Fisher's exact test), Q value = 0.23

Table S88.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 153 80 3 56 6 8 3 1 2
subtype1 24 14 0 19 1 1 0 0 0
subtype2 38 20 1 11 4 1 1 1 0
subtype3 45 25 2 12 0 5 2 0 1
subtype4 19 4 0 8 1 1 0 0 1
subtype5 27 17 0 6 0 0 0 0 0

Figure S80.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.108 (Fisher's exact test), Q value = 0.26

Table S89.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 164 87 69 12
subtype1 26 15 21 1
subtype2 43 23 16 5
subtype3 48 28 17 4
subtype4 19 4 9 2
subtype5 28 17 6 0

Figure S81.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.944 (Fisher's exact test), Q value = 0.99

Table S90.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 233 4
subtype1 46 1
subtype2 51 1
subtype3 69 2
subtype4 26 0
subtype5 41 0

Figure S82.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.151 (Fisher's exact test), Q value = 0.33

Table S91.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 249 4
subtype1 50 0
subtype2 59 0
subtype3 73 3
subtype4 22 1
subtype5 45 0

Figure S83.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0367 (Fisher's exact test), Q value = 0.11

Table S92.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 110 224
subtype1 22 41
subtype2 26 61
subtype3 41 57
subtype4 12 22
subtype5 9 43

Figure S84.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.605 (Fisher's exact test), Q value = 0.79

Table S93.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 1 326 7
subtype1 0 61 2
subtype2 0 86 1
subtype3 1 93 4
subtype4 0 34 0
subtype5 0 52 0

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.185 (Fisher's exact test), Q value = 0.35

Table S94.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

nPatients R0 R1 R2 RX
ALL 295 15 1 17
subtype1 57 4 0 2
subtype2 75 3 0 7
subtype3 85 5 1 7
subtype4 27 3 0 1
subtype5 51 0 0 0

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.00935 (Fisher's exact test), Q value = 0.041

Table S95.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 153 16 154
subtype1 0 41 1 21
subtype2 0 27 7 47
subtype3 0 45 4 46
subtype4 0 13 3 18
subtype5 1 27 1 22

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.722 (Fisher's exact test), Q value = 0.84

Table S96.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 306
subtype1 4 58
subtype2 3 77
subtype3 2 92
subtype4 1 32
subtype5 2 47

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LIHC-TP/15111045/LIHC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LIHC-TP/15082975/LIHC-TP.merged_data.txt

  • Number of patients = 363

  • Number of clustering approaches = 8

  • Number of selected clinical features = 11

  • 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)
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