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 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.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[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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