Correlation between aggregated molecular cancer subtypes and selected clinical features
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 11 clinical features across 317 patients, 10 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 do not correlate to any clinical features.

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

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'AGE' and 'GENDER'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.T.STAGE',  'GENDER', and 'RACE'.

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

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

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'AGE' and 'NEOPLASM.DISEASESTAGE'.

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE'.

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, 10 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 0.12
(1.00) 		0.332
(1.00) 		0.312
(1.00) 		0.464
(1.00) 		0.356
(1.00) 		0.024
(1.00) 		0.0622
(1.00) 		0.00488
(0.371)
AGE Kruskal-Wallis (anova) 0.075
(1.00) 		0.00135
(0.11) 		0.00116
(0.0954) 		0.0041
(0.32) 		0.00585
(0.433) 		1.54e-05
(0.00134) 		0.00218
(0.174) 		1.07e-05
(0.000943)
NEOPLASM DISEASESTAGE Fisher's exact test 0.177
(1.00) 		0.789
(1.00) 		0.0739
(1.00) 		0.0125
(0.91) 		0.191
(1.00) 		0.868
(1.00) 		0.00297
(0.235) 		0.0904
(1.00)
PATHOLOGY T STAGE Fisher's exact test 0.121
(1.00) 		0.586
(1.00) 		0.0581
(1.00) 		0.00031
(0.0257) 		0.677
(1.00) 		0.855
(1.00) 		0.0245
(1.00) 		0.095
(1.00)
PATHOLOGY N STAGE Fisher's exact test 1
(1.00) 		1
(1.00) 		0.304
(1.00) 		0.385
(1.00) 		0.758
(1.00) 		1
(1.00) 		0.191
(1.00) 		0.943
(1.00)
PATHOLOGY M STAGE Fisher's exact test 0.813
(1.00) 		0.217
(1.00) 		0.0496
(1.00) 		0.209
(1.00) 		0.019
(1.00) 		0.0931
(1.00) 		0.0173
(1.00) 		0.101
(1.00)
GENDER Fisher's exact test 0.106
(1.00) 		0.0947
(1.00) 		6e-05
(0.00516) 		0.00022
(0.0187) 		0.51
(1.00) 		0.0741
(1.00) 		0.13
(1.00) 		0.166
(1.00)
HISTOLOGICAL TYPE Fisher's exact test 0.536
(1.00) 		0.174
(1.00) 		0.0316
(1.00) 		0.679
(1.00) 		0.147
(1.00) 		0.922
(1.00) 		0.192
(1.00) 		0.815
(1.00)
COMPLETENESS OF RESECTION Fisher's exact test 0.0697
(1.00) 		0.74
(1.00) 		0.043
(1.00) 		0.503
(1.00) 		0.75
(1.00) 		0.311
(1.00) 		0.233
(1.00) 		0.221
(1.00)
RACE Fisher's exact test 0.0396
(1.00) 		0.00465
(0.358) 		0.0278
(1.00) 		0.00026
(0.0218) 		0.274
(1.00) 		0.0393
(1.00) 		0.844
(1.00) 		0.00564
(0.423)
ETHNICITY Fisher's exact test 0.326
(1.00) 		0.464
(1.00) 		0.355
(1.00) 		0.376
(1.00) 		0.201
(1.00) 		0.538
(1.00) 		1
(1.00) 		0.696
(1.00)
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 93 106 112
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.12 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 292 92 0.0 - 113.0 (14.5)
subtype1 92 28 0.1 - 101.0 (14.4)
subtype2 94 34 0.0 - 113.0 (13.8)
subtype3 106 30 0.1 - 108.8 (17.1)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.075 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 308 59.8 (12.7)
subtype1 92 61.3 (11.7)
subtype2 104 57.6 (12.8)
subtype3 112 60.6 (13.1)

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

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

P value = 0.177 (Fisher's exact test), Q value = 1

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 146 72 2 52 7 7 2 1 2
subtype1 51 20 0 14 0 0 1 1 0
subtype2 41 30 1 20 3 2 1 0 1
subtype3 54 22 1 18 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 = 0.121 (Fisher's exact test), Q value = 1

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 155 78 64 12
subtype1 54 21 16 1
subtype2 42 33 25 6
subtype3 59 24 23 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 222 3
subtype1 65 1
subtype2 78 1
subtype3 79 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 = 0.813 (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 M0 M1 MX
ALL 238 4 69
subtype1 71 1 21
subtype2 84 2 20
subtype3 83 1 28

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 = 0.106 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 100 211
subtype1 22 71
subtype2 38 68
subtype3 40 72

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 = 0.536 (Fisher's exact test), Q value = 1

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 303 6
subtype1 0 92 1
subtype2 0 103 3
subtype3 2 108 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 = 0.0697 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 274 14 1 16
subtype1 84 6 1 1
subtype2 91 6 0 8
subtype3 99 2 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 = 0.0396 (Fisher's exact test), Q value = 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 1 141 15 145
subtype1 1 45 4 39
subtype2 0 58 4 43
subtype3 0 38 7 63

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 = 0.326 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 287
subtype1 4 84
subtype2 3 100
subtype3 1 103

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 59 108 76
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.332 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 222 84 0.0 - 113.0 (13.7)
subtype1 53 18 0.1 - 108.8 (13.9)
subtype2 100 38 0.0 - 102.7 (13.6)
subtype3 69 28 0.1 - 113.0 (13.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00135 (Kruskal-Wallis (anova)), Q value = 0.11

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

nPatients Mean (Std.Dev)
ALL 240 60.4 (13.7)
subtype1 59 58.0 (15.2)
subtype2 106 64.3 (10.1)
subtype3 75 56.7 (15.4)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.789 (Fisher's exact test), Q value = 1

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 91 57 2 52 7 9 2 1 2
subtype1 24 11 0 15 3 4 0 0 0
subtype2 42 27 1 20 3 3 0 1 1
subtype3 25 19 1 17 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 = 0.586 (Fisher's exact test), Q value = 1

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 100 63 65 13
subtype1 25 12 18 3
subtype2 48 29 23 7
subtype3 27 22 24 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 153 4
subtype1 43 1
subtype2 60 2
subtype3 50 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 = 0.217 (Fisher's exact test), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 171 4 68
subtype1 45 0 14
subtype2 71 1 36
subtype3 55 3 18

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0947 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 88 155
subtype1 20 39
subtype2 33 75
subtype3 35 41

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.174 (Fisher's exact test), Q value = 1

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 235 6
subtype1 2 56 1
subtype2 0 106 2
subtype3 0 73 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 = 0.74 (Fisher's exact test), Q value = 1

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 206 13 1 17
subtype1 48 2 0 6
subtype2 92 6 0 7
subtype3 66 5 1 4

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.00465 (Fisher's exact test), Q value = 0.36

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 71 15 147
subtype1 0 16 6 34
subtype2 1 22 8 74
subtype3 0 33 1 39

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.464 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 218
subtype1 3 52
subtype2 2 96
subtype3 4 70

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 6
Number of samples 28 58 43 25 41 39
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.312 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 214 82 0.0 - 113.0 (13.7)
subtype1 24 10 1.0 - 44.4 (12.1)
subtype2 56 22 0.1 - 102.7 (21.3)
subtype3 35 12 0.1 - 113.0 (15.0)
subtype4 25 11 0.1 - 73.8 (9.6)
subtype5 38 15 0.0 - 107.1 (14.4)
subtype6 36 12 0.4 - 108.8 (12.9)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00116 (Kruskal-Wallis (anova)), Q value = 0.095

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

nPatients Mean (Std.Dev)
ALL 231 60.4 (13.6)
subtype1 28 61.9 (11.3)
subtype2 57 63.9 (13.5)
subtype3 42 53.9 (14.6)
subtype4 24 64.8 (10.5)
subtype5 41 57.2 (13.9)
subtype6 39 62.1 (12.6)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0739 (Fisher's exact test), Q value = 1

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 85 56 2 51 6 9 2 1 2
subtype1 5 10 0 7 2 1 0 0 0
subtype2 29 9 1 12 1 0 0 0 1
subtype3 9 13 1 11 1 3 1 0 1
subtype4 13 4 0 2 1 1 1 1 0
subtype5 13 10 0 10 0 3 0 0 0
subtype6 16 10 0 9 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 = 0.0581 (Fisher's exact test), Q value = 1

Table S29.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 94 62 63 13
subtype1 5 12 8 3
subtype2 30 9 14 4
subtype3 12 14 15 2
subtype4 16 4 4 1
subtype5 14 13 12 2
subtype6 17 10 10 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 = 0.304 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 146 4
subtype1 15 0
subtype2 37 0
subtype3 30 2
subtype4 12 1
subtype5 26 1
subtype6 26 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 = 0.0496 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 163 4 67
subtype1 19 0 9
subtype2 40 1 17
subtype3 36 2 5
subtype4 12 1 12
subtype5 28 0 13
subtype6 28 0 11

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 = 6e-05 (Fisher's exact test), Q value = 0.0052

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

nPatients FEMALE MALE
ALL 84 150
subtype1 7 21
subtype2 19 39
subtype3 25 18
subtype4 6 19
subtype5 22 19
subtype6 5 34

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 = 0.0316 (Fisher's exact test), Q value = 1

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 227 5
subtype1 0 28 0
subtype2 0 58 0
subtype3 0 41 2
subtype4 0 25 0
subtype5 2 36 3
subtype6 0 39 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 = 0.043 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 198 13 1 16
subtype1 18 4 0 5
subtype2 48 4 0 5
subtype3 39 2 0 2
subtype4 20 1 1 1
subtype5 36 2 0 3
subtype6 37 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 = 0.0278 (Fisher's exact test), Q value = 1

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 66 15 143
subtype1 0 9 4 14
subtype2 0 11 2 41
subtype3 0 20 1 21
subtype4 0 3 2 17
subtype5 0 15 2 24
subtype6 1 8 4 26

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.355 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 209
subtype1 1 23
subtype2 5 48
subtype3 1 40
subtype4 1 22
subtype5 1 39
subtype6 0 37

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
Number of samples 85 113 36
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.464 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 214 82 0.0 - 113.0 (13.7)
subtype1 71 31 0.1 - 113.0 (13.4)
subtype2 109 40 0.0 - 102.7 (17.1)
subtype3 34 11 0.4 - 108.8 (12.9)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0041 (Kruskal-Wallis (anova)), Q value = 0.32

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

nPatients Mean (Std.Dev)
ALL 231 60.4 (13.6)
subtype1 84 56.7 (14.4)
subtype2 111 62.4 (12.8)
subtype3 36 63.1 (12.3)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0125 (Fisher's exact test), Q value = 0.91

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 85 56 2 51 6 9 2 1 2
subtype1 16 25 1 25 4 5 1 0 1
subtype2 54 21 1 20 1 3 1 1 1
subtype3 15 10 0 6 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 = 0.00031 (Fisher's exact test), Q value = 0.026

Table S41.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 94 62 63 13
subtype1 18 29 33 5
subtype2 60 22 23 7
subtype3 16 11 7 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 = 0.385 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 146 4
subtype1 58 3
subtype2 65 1
subtype3 23 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 = 0.209 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 163 4 67
subtype1 66 2 17
subtype2 73 2 38
subtype3 24 0 12

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 = 0.00022 (Fisher's exact test), Q value = 0.019

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

nPatients FEMALE MALE
ALL 84 150
subtype1 43 42
subtype2 36 77
subtype3 5 31

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 = 0.679 (Fisher's exact test), Q value = 1

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 227 5
subtype1 0 82 3
subtype2 2 109 2
subtype3 0 36 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 = 0.503 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 198 13 1 16
subtype1 71 6 0 7
subtype2 94 6 1 9
subtype3 33 1 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 = 0.00026 (Fisher's exact test), Q value = 0.022

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 66 15 143
subtype1 0 39 3 42
subtype2 0 21 8 76
subtype3 1 6 4 25

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.376 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 209
subtype1 3 80
subtype2 6 95
subtype3 0 34

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 52 112 43 105
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.356 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 291 90 0.0 - 113.0 (14.6)
subtype1 50 18 0.0 - 102.7 (14.6)
subtype2 104 35 0.2 - 108.8 (13.1)
subtype3 36 9 0.1 - 75.7 (24.3)
subtype4 101 28 0.1 - 113.0 (14.9)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00585 (Kruskal-Wallis (anova)), Q value = 0.43

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

nPatients Mean (Std.Dev)
ALL 309 59.6 (12.9)
subtype1 50 63.0 (11.4)
subtype2 112 61.1 (13.5)
subtype3 43 55.3 (14.0)
subtype4 104 58.1 (12.1)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.191 (Fisher's exact test), Q value = 1

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 144 71 2 54 7 9 2 1 2
subtype1 29 6 1 7 3 1 0 1 1
subtype2 52 28 0 23 2 3 0 0 0
subtype3 17 10 1 10 0 2 1 0 0
subtype4 46 27 0 14 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 = 0.677 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 153 77 67 13
subtype1 31 7 11 3
subtype2 53 29 25 4
subtype3 19 11 11 2
subtype4 50 30 20 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 = 0.758 (Fisher's exact test), Q value = 1

Table S54.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 222 4
subtype1 36 1
subtype2 80 1
subtype3 35 1
subtype4 71 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 = 0.019 (Fisher's exact test), Q value = 1

Table S55.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 241 4 67
subtype1 32 1 19
subtype2 92 0 20
subtype3 37 1 5
subtype4 80 2 23

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.51 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 100 212
subtype1 18 34
subtype2 30 82
subtype3 15 28
subtype4 37 68

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.147 (Fisher's exact test), Q value = 1

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 304 6
subtype1 0 51 1
subtype2 0 112 0
subtype3 0 42 1
subtype4 2 99 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 = 0.75 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 274 14 1 17
subtype1 40 4 0 4
subtype2 101 4 0 6
subtype3 38 2 0 3
subtype4 95 4 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 = 0.274 (Fisher's exact test), Q value = 1

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 1 144 15 143
subtype1 0 18 4 28
subtype2 1 51 4 53
subtype3 0 26 3 14
subtype4 0 49 4 48

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.201 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 287
subtype1 0 49
subtype2 2 104
subtype3 1 40
subtype4 6 94

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 35 82 47 105 43
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.024 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 291 90 0.0 - 113.0 (14.6)
subtype1 32 14 0.0 - 83.6 (11.0)
subtype2 77 23 0.1 - 102.7 (15.0)
subtype3 43 19 0.2 - 113.0 (13.6)
subtype4 98 25 0.1 - 101.0 (19.2)
subtype5 41 9 0.3 - 108.8 (12.8)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 1.54e-05 (Kruskal-Wallis (anova)), Q value = 0.0013

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

nPatients Mean (Std.Dev)
ALL 309 59.6 (12.9)
subtype1 35 62.0 (11.4)
subtype2 80 63.2 (12.1)
subtype3 47 57.9 (13.7)
subtype4 104 55.1 (13.4)
subtype5 43 64.0 (9.9)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.868 (Fisher's exact test), Q value = 1

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 144 71 2 54 7 9 2 1 2
subtype1 18 4 0 9 1 1 0 0 1
subtype2 36 20 1 10 4 1 0 1 0
subtype3 23 11 0 10 0 1 0 0 0
subtype4 45 26 1 18 1 5 2 0 1
subtype5 22 10 0 7 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 = 0.855 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 153 77 67 13
subtype1 18 5 10 2
subtype2 40 21 16 5
subtype3 24 12 11 0
subtype4 48 29 22 5
subtype5 23 10 8 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 222 4
subtype1 26 0
subtype2 51 1
subtype3 35 1
subtype4 77 2
subtype5 33 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 = 0.0931 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 241 4 67
subtype1 23 1 11
subtype2 58 0 24
subtype3 39 0 8
subtype4 86 3 16
subtype5 35 0 8

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0741 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 100 212
subtype1 12 23
subtype2 27 55
subtype3 16 31
subtype4 39 66
subtype5 6 37

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.922 (Fisher's exact test), Q value = 1

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 2 304 6
subtype1 0 35 0
subtype2 0 80 2
subtype3 0 46 1
subtype4 2 100 3
subtype5 0 43 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 = 0.311 (Fisher's exact test), Q value = 1

Table S70.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 274 14 1 17
subtype1 26 3 0 3
subtype2 70 5 0 5
subtype3 42 1 0 4
subtype4 94 5 1 5
subtype5 42 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 = 0.0393 (Fisher's exact test), Q value = 1

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 1 144 15 143
subtype1 0 15 5 15
subtype2 0 28 4 45
subtype3 0 29 1 16
subtype4 0 53 4 45
subtype5 1 19 1 22

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.538 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 287
subtype1 0 34
subtype2 2 74
subtype3 2 43
subtype4 5 96
subtype5 0 40

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 83 72 98 38
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0622 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 273 84 0.0 - 113.0 (14.4)
subtype1 74 29 0.2 - 113.0 (13.4)
subtype2 71 22 0.1 - 107.1 (15.4)
subtype3 96 27 0.3 - 108.8 (18.4)
subtype4 32 6 0.0 - 73.4 (14.3)

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 'AGE'

P value = 0.00218 (Kruskal-Wallis (anova)), Q value = 0.17

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

nPatients Mean (Std.Dev)
ALL 288 59.7 (12.5)
subtype1 83 57.8 (13.4)
subtype2 71 59.3 (11.9)
subtype3 97 63.5 (10.2)
subtype4 37 54.6 (14.8)

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

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

P value = 0.00297 (Fisher's exact test), Q value = 0.23

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 132 69 2 49 6 8 2 1 2
subtype1 30 20 0 22 5 5 0 0 0
subtype2 29 18 0 10 0 3 1 1 1
subtype3 56 21 1 10 0 0 0 0 1
subtype4 17 10 1 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 = 0.0245 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 141 75 61 12
subtype1 31 21 26 5
subtype2 34 20 13 5
subtype3 59 23 12 2
subtype4 17 11 10 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 = 0.191 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 206 4
subtype1 60 3
subtype2 46 1
subtype3 70 0
subtype4 30 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 = 0.0173 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 227 4 60
subtype1 69 0 14
subtype2 47 2 23
subtype3 77 1 20
subtype4 34 1 3

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 = 0.13 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 97 194
subtype1 35 48
subtype2 24 48
subtype3 25 73
subtype4 13 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 = 0.192 (Fisher's exact test), Q value = 1

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 1 284 6
subtype1 0 80 3
subtype2 1 69 2
subtype3 0 98 0
subtype4 0 37 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 = 0.233 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 255 14 1 15
subtype1 70 5 0 5
subtype2 60 6 1 3
subtype3 92 2 0 3
subtype4 33 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 = 0.844 (Fisher's exact test), Q value = 1

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 1 138 15 128
subtype1 0 42 4 36
subtype2 0 32 4 33
subtype3 1 41 6 45
subtype4 0 23 1 14

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

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 267
subtype1 3 75
subtype2 2 67
subtype3 3 88
subtype4 1 37

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 57 72 86 29 47
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.00488 (logrank test), Q value = 0.37

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

nPatients nDeath Duration Range (Median), Month
ALL 273 84 0.0 - 113.0 (14.4)
subtype1 49 21 0.1 - 107.1 (13.4)
subtype2 69 20 0.2 - 113.0 (17.1)
subtype3 82 21 0.1 - 101.0 (15.4)
subtype4 27 13 0.0 - 83.6 (12.0)
subtype5 46 9 0.3 - 79.4 (13.1)

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 'AGE'

P value = 1.07e-05 (Kruskal-Wallis (anova)), Q value = 0.00094

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

nPatients Mean (Std.Dev)
ALL 288 59.7 (12.5)
subtype1 57 54.8 (12.6)
subtype2 70 63.5 (11.9)
subtype3 85 56.6 (13.2)
subtype4 29 63.4 (11.1)
subtype5 47 63.3 (9.7)

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

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

P value = 0.0904 (Fisher's exact test), Q value = 1

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 132 69 2 49 6 8 2 1 2
subtype1 21 13 0 17 1 1 0 0 0
subtype2 31 16 1 10 4 1 0 1 0
subtype3 38 22 1 11 0 5 2 0 1
subtype4 17 3 0 6 1 1 0 0 1
subtype5 25 15 0 5 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 = 0.095 (Fisher's exact test), Q value = 1

Table S89.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T0+T1 T2 T3 T4
ALL 141 75 61 12
subtype1 23 14 19 1
subtype2 35 17 15 5
subtype3 40 26 15 4
subtype4 17 3 7 2
subtype5 26 15 5 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 = 0.943 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 206 4
subtype1 42 1
subtype2 44 1
subtype3 61 2
subtype4 22 0
subtype5 37 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 = 0.101 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 227 4 60
subtype1 47 0 10
subtype2 52 0 20
subtype3 68 3 15
subtype4 19 1 9
subtype5 41 0 6

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 = 0.166 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 97 194
subtype1 20 37
subtype2 23 49
subtype3 33 53
subtype4 12 17
subtype5 9 38

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 = 0.815 (Fisher's exact test), Q value = 1

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

nPatients FIBROLAMELLAR CARCINOMA HEPATOCELLULAR CARCINOMA HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL 1 284 6
subtype1 0 55 2
subtype2 0 71 1
subtype3 1 82 3
subtype4 0 29 0
subtype5 0 47 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 = 0.221 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 255 14 1 15
subtype1 51 4 0 2
subtype2 61 3 0 6
subtype3 75 4 1 6
subtype4 22 3 0 1
subtype5 46 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 = 0.00564 (Fisher's exact test), Q value = 0.42

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 1 138 15 128
subtype1 0 38 1 18
subtype2 0 21 6 40
subtype3 0 43 4 36
subtype4 0 11 3 15
subtype5 1 25 1 19

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 = 0.696 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 267
subtype1 3 53
subtype2 1 65
subtype3 3 79
subtype4 0 28
subtype5 2 42

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

Methods & Data
Input

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

  • Clinical data file = LIHC-TP.merged_data.txt

  • Number of patients = 317

  • 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.

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)
Made with Nozzle