Correlation between aggregated molecular cancer subtypes and selected clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12Z148Z

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 176 patients, 9 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' and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'GENDER'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE', 'GENDER', and 'RACE'.
3 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'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'AGE'.
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, 9 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	0.0797 (1.00)	0.499 (1.00)	0.0081 (0.624)	0.75 (1.00)	0.498 (1.00)	0.0675 (1.00)	0.135 (1.00)	0.0828 (1.00)
AGE	Kruskal-Wallis (anova)	0.636 (1.00)	0.000479 (0.0388)	0.0103 (0.782)	0.000113 (0.00976)	0.0125 (0.928)	4.19e-05 (0.00369)	0.000652 (0.0522)	6.14e-05 (0.00534)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.156 (1.00)	0.374 (1.00)	0.0568 (1.00)	0.111 (1.00)	0.231 (1.00)	0.851 (1.00)	0.228 (1.00)	0.923 (1.00)
PATHOLOGY T STAGE	Fisher's exact test	0.202 (1.00)	0.561 (1.00)	0.111 (1.00)	0.00572 (0.446)	0.315 (1.00)	0.826 (1.00)	0.445 (1.00)	0.867 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	1 (1.00)	0.637 (1.00)	1 (1.00)	0.581 (1.00)	1 (1.00)	0.532 (1.00)	0.519 (1.00)	0.91 (1.00)
PATHOLOGY M STAGE	Fisher's exact test	0.929 (1.00)	0.352 (1.00)	0.417 (1.00)	0.336 (1.00)	0.278 (1.00)	0.292 (1.00)	0.421 (1.00)	0.0312 (1.00)
GENDER	Fisher's exact test	0.0138 (1.00)	0.136 (1.00)	0.00024 (0.0204)	0.00035 (0.0287)	0.756 (1.00)	0.0854 (1.00)	0.0387 (1.00)	0.0805 (1.00)
HISTOLOGICAL TYPE	Fisher's exact test	0.365 (1.00)	0.58 (1.00)	0.161 (1.00)	0.786 (1.00)	0.425 (1.00)	0.975 (1.00)	0.0232 (1.00)	0.696 (1.00)
COMPLETENESS OF RESECTION	Fisher's exact test	0.694 (1.00)	0.938 (1.00)	0.407 (1.00)	0.487 (1.00)	0.863 (1.00)	0.269 (1.00)	0.141 (1.00)	0.452 (1.00)
RACE	Fisher's exact test	0.133 (1.00)	0.00026 (0.0218)	0.0425 (1.00)	0.00031 (0.0257)	0.787 (1.00)	0.00409 (0.323)	0.0116 (0.868)	0.0197 (1.00)
ETHNICITY	Fisher's exact test	1 (1.00)	0.101 (1.00)	0.734 (1.00)	0.415 (1.00)	0.269 (1.00)	0.629 (1.00)	0.248 (1.00)	0.388 (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	45	61	54

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0797 (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	157	66	0.0 - 113.0 (13.9)
subtype1	44	21	0.1 - 101.0 (13.1)
subtype2	61	21	0.1 - 107.1 (19.2)
subtype3	52	24	0.0 - 113.0 (12.6)

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 = 0.636 (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	158	61.7 (13.6)
subtype1	45	63.2 (13.0)
subtype2	59	60.9 (14.3)
subtype3	54	61.4 (13.5)

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 = 0.156 (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	65	37	2	31	5	5	1	1	2
subtype1	22	9	0	8	1	0	1	1	0
subtype2	27	10	0	13	3	4	0	0	1
subtype3	16	18	2	10	1	1	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 = 0.202 (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	T1	T2	T3	T4
ALL	68	41	42	8
subtype1	23	11	10	0
subtype2	28	12	17	4
subtype3	17	18	15	4

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	102	3
subtype1	26	1
subtype2	47	1
subtype3	29	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 = 0.929 (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	120	3	37
subtype1	32	1	12
subtype2	48	1	12
subtype3	40	1	13

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 = 0.0138 (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	60	100
subtype1	9	36
subtype2	28	33
subtype3	23	31

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 = 0.365 (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	156	2
subtype1	0	44	1
subtype2	2	59	0
subtype3	0	53	1

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 = 0.694 (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	131	11	1	12
subtype1	37	4	1	2
subtype2	50	3	0	4
subtype3	44	4	0	6

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 = 0.133 (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	36	8	109
subtype1	1	7	5	27
subtype2	0	13	2	45
subtype3	0	16	1	37

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 = 1 (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	3	143
subtype1	1	38
subtype2	1	56
subtype3	1	49

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	76	50	41

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.499 (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	163	68	0.0 - 113.0 (13.8)
subtype1	74	32	0.0 - 102.7 (14.2)
subtype2	49	20	0.1 - 113.0 (12.9)
subtype3	40	16	0.1 - 107.1 (14.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.000479 (Kruskal-Wallis (anova)), Q value = 0.039

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

	nPatients	Mean (Std.Dev)
ALL	165	61.6 (13.8)
subtype1	74	66.5 (9.5)
subtype2	50	57.1 (15.4)
subtype3	41	58.2 (15.6)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.374 (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	64	39	2	36	5	6	1	1	2
subtype1	31	20	1	14	1	1	0	1	1
subtype2	17	13	1	11	1	1	1	0	1
subtype3	16	6	0	11	3	4	0	0	0

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 = 0.561 (Fisher's exact test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	67	43	47	9
subtype1	33	22	17	3
subtype2	17	14	16	3
subtype3	17	7	14	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 = 0.637 (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	106	3
subtype1	40	2
subtype2	34	0
subtype3	32	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 = 0.352 (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	127	3	37
subtype1	54	1	21
subtype2	40	2	8
subtype3	33	0	8

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.136 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	62	105
subtype1	23	53
subtype2	24	26
subtype3	15	26

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.58 (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	1	163	3
subtype1	0	75	1
subtype2	0	49	1
subtype3	1	39	1

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 = 0.938 (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	138	11	1	12
subtype1	64	5	0	5
subtype2	41	4	1	4
subtype3	33	2	0	3

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.00026 (Fisher's exact test), Q value = 0.022

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	39	8	113
subtype1	1	9	4	59
subtype2	0	22	0	26
subtype3	0	8	4	28

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.101 (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	5	149
subtype1	0	65
subtype2	3	46
subtype3	2	38

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	34	25	45	28	30

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0081 (logrank test), Q value = 0.62

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	159	66	0.0 - 113.0 (13.8)
subtype1	34	13	0.0 - 107.1 (11.4)
subtype2	24	14	0.1 - 45.1 (8.4)
subtype3	44	18	0.1 - 102.7 (18.8)
subtype4	28	12	0.1 - 113.0 (14.7)
subtype5	29	9	0.2 - 93.7 (13.4)

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 = 0.0103 (Kruskal-Wallis (anova)), Q value = 0.78

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

	nPatients	Mean (Std.Dev)
ALL	160	61.6 (13.5)
subtype1	34	57.2 (14.9)
subtype2	24	63.1 (12.0)
subtype3	44	66.3 (10.6)
subtype4	28	56.4 (15.1)
subtype5	30	63.2 (13.1)

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 = 0.0568 (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	64	37	2	34	4	6	1	1	2
subtype1	12	7	0	6	0	4	1	0	0
subtype2	5	7	0	8	1	1	0	0	0
subtype3	25	5	1	9	0	0	0	1	1
subtype4	8	10	1	5	2	1	0	0	1
subtype5	14	8	0	6	1	0	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 = 0.111 (Fisher's exact test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	67	41	44	9
subtype1	13	9	9	3
subtype2	5	9	9	2
subtype3	26	5	11	3
subtype4	9	10	8	1
subtype5	14	8	7	0

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 = 1 (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	103	3
subtype1	20	1
subtype2	15	0
subtype3	30	1
subtype4	19	1
subtype5	19	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 = 0.417 (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	122	3	37
subtype1	24	1	9
subtype2	18	0	7
subtype3	33	1	11
subtype4	25	1	2
subtype5	22	0	8

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 = 0.00024 (Fisher's exact test), Q value = 0.02

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

nPatients	FEMALE	MALE
ALL	62	100
subtype1	17	17
subtype2	9	16
subtype3	15	30
subtype4	18	10
subtype5	3	27

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 = 0.161 (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	158	2
subtype1	2	31	1
subtype2	0	25	0
subtype3	0	45	0
subtype4	0	27	1
subtype5	0	30	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 = 0.407 (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	133	11	1	12
subtype1	26	3	1	2
subtype2	17	4	0	3
subtype3	38	3	0	4
subtype4	25	1	0	2
subtype5	27	0	0	1

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 = 0.0425 (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	38	8	109
subtype1	0	10	2	20
subtype2	0	8	1	14
subtype3	0	5	2	37
subtype4	0	11	0	16
subtype5	1	4	3	22

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.734 (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	4	144
subtype1	1	31
subtype2	1	20
subtype3	2	40
subtype4	0	26
subtype5	0	27

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	65	66	31

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.75 (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	159	66	0.0 - 113.0 (13.8)
subtype1	64	28	0.1 - 113.0 (13.6)
subtype2	65	29	0.0 - 102.7 (13.9)
subtype3	30	9	0.2 - 93.7 (13.5)

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 = 0.000113 (Kruskal-Wallis (anova)), Q value = 0.0098

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

	nPatients	Mean (Std.Dev)
ALL	160	61.6 (13.5)
subtype1	65	56.0 (15.2)
subtype2	64	66.3 (9.9)
subtype3	31	63.7 (12.4)

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 = 0.111 (Fisher's exact test), Q value = 1

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	64	37	2	34	4	6	1	1	2
subtype1	16	17	1	19	3	3	1	0	1
subtype2	34	11	1	10	0	2	0	1	1
subtype3	14	9	0	5	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 = 0.00572 (Fisher's exact test), Q value = 0.45

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

nPatients	T1	T2	T3	T4
ALL	67	41	44	9
subtype1	16	19	26	4
subtype2	36	13	12	5
subtype3	15	9	6	0

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 = 0.581 (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	103	3
subtype1	45	1
subtype2	40	1
subtype3	18	1

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 = 0.336 (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	122	3	37
subtype1	53	2	10
subtype2	46	1	19
subtype3	23	0	8

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 = 0.00035 (Fisher's exact test), Q value = 0.029

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

nPatients	FEMALE	MALE
ALL	62	100
subtype1	35	30
subtype2	23	43
subtype3	4	27

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 = 0.786 (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	158	2
subtype1	2	62	1
subtype2	0	65	1
subtype3	0	31	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 = 0.487 (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	133	11	1	12
subtype1	52	5	1	6
subtype2	53	6	0	5
subtype3	28	0	0	1

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 = 0.00031 (Fisher's exact test), Q value = 0.026

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	38	8	109
subtype1	0	26	1	36
subtype2	0	7	4	51
subtype3	1	5	3	22

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.415 (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	4	144
subtype1	1	60
subtype2	3	56
subtype3	0	28

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
Number of samples	37	70	64

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.498 (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	167	66	0.0 - 113.0 (13.6)
subtype1	35	18	0.0 - 102.7 (13.6)
subtype2	69	23	0.2 - 113.0 (12.9)
subtype3	63	25	0.1 - 101.0 (14.2)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0125 (Kruskal-Wallis (anova)), Q value = 0.93

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

	nPatients	Mean (Std.Dev)
ALL	169	61.5 (13.7)
subtype1	35	63.9 (13.5)
subtype2	70	63.2 (14.0)
subtype3	64	58.2 (13.1)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.231 (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	64	39	2	38	5	7	1	1	2
subtype1	15	5	1	7	3	1	0	1	1
subtype2	25	22	0	15	1	2	0	0	0
subtype3	24	12	1	16	1	4	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 = 0.315 (Fisher's exact test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	68	43	49	10
subtype1	16	6	12	3
subtype2	26	24	17	2
subtype3	26	13	20	5

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 = 1 (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	108	3
subtype1	25	1
subtype2	41	1
subtype3	42	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 = 0.278 (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	128	3	40
subtype1	24	1	12
subtype2	55	0	15
subtype3	49	2	13

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.756 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	64	107
subtype1	12	25
subtype2	28	42
subtype3	24	40

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.425 (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	166	3
subtype1	0	37	0
subtype2	0	69	1
subtype3	2	60	2

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 = 0.863 (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	141	11	1	13
subtype1	27	3	0	3
subtype2	61	4	0	4
subtype3	53	4	1	6

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.787 (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	41	8	115
subtype1	0	8	2	25
subtype2	1	15	2	50
subtype3	0	18	4	40

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.269 (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	6	151
subtype1	0	34
subtype2	2	61
subtype3	4	56

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	19	45	67	18	22

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0675 (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	167	66	0.0 - 113.0 (13.6)
subtype1	18	11	0.0 - 83.6 (6.5)
subtype2	44	16	0.1 - 102.7 (14.2)
subtype3	67	29	0.1 - 113.0 (14.2)
subtype4	16	6	0.1 - 60.4 (13.5)
subtype5	22	4	0.3 - 93.7 (12.9)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 4.19e-05 (Kruskal-Wallis (anova)), Q value = 0.0037

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

	nPatients	Mean (Std.Dev)
ALL	169	61.5 (13.7)
subtype1	19	61.0 (14.1)
subtype2	43	68.3 (9.0)
subtype3	67	55.6 (14.8)
subtype4	18	62.8 (14.0)
subtype5	22	65.1 (10.0)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.851 (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	64	39	2	38	5	7	1	1	2
subtype1	6	3	0	6	1	1	0	0	1
subtype2	16	11	1	8	2	1	0	1	0
subtype3	26	15	1	15	1	3	1	0	1
subtype4	4	6	0	6	0	2	0	0	0
subtype5	12	4	0	3	1	0	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 = 0.826 (Fisher's exact test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	68	43	49	10
subtype1	6	4	7	2
subtype2	17	12	13	3
subtype3	27	17	19	4
subtype4	5	6	6	1
subtype5	13	4	4	0

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 = 0.532 (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	108	3
subtype1	13	0
subtype2	25	1
subtype3	45	1
subtype4	11	1
subtype5	14	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 = 0.292 (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	128	3	40
subtype1	12	1	6
subtype2	32	0	13
subtype3	51	2	14
subtype4	17	0	1
subtype5	16	0	6

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0854 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	64	107
subtype1	6	13
subtype2	17	28
subtype3	31	36
subtype4	7	11
subtype5	3	19

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.975 (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	166	3
subtype1	0	19	0
subtype2	0	44	1
subtype3	2	63	2
subtype4	0	18	0
subtype5	0	22	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 = 0.269 (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	141	11	1	13
subtype1	11	3	0	2
subtype2	37	4	0	3
subtype3	58	3	1	5
subtype4	14	1	0	3
subtype5	21	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 = 0.00409 (Fisher's exact test), Q value = 0.32

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	41	8	115
subtype1	0	5	2	12
subtype2	0	3	1	38
subtype3	0	24	4	36
subtype4	0	6	0	12
subtype5	1	3	1	17

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.629 (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	6	151
subtype1	0	18
subtype2	1	39
subtype3	4	60
subtype4	1	15
subtype5	0	19

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	5
Number of samples	21	29	32	54	35

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.135 (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	167	66	0.0 - 113.0 (13.6)
subtype1	19	5	0.1 - 75.7 (13.4)
subtype2	29	10	0.1 - 80.8 (12.7)
subtype3	31	17	0.0 - 102.7 (13.8)
subtype4	54	18	0.2 - 113.0 (14.2)
subtype5	34	16	0.2 - 101.0 (12.6)

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 = 0.000652 (Kruskal-Wallis (anova)), Q value = 0.052

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

	nPatients	Mean (Std.Dev)
ALL	169	61.5 (13.7)
subtype1	21	55.9 (16.2)
subtype2	29	56.4 (13.2)
subtype3	30	66.3 (12.0)
subtype4	54	65.6 (11.0)
subtype5	35	58.5 (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 = 0.228 (Fisher's exact test), Q value = 1

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	64	39	2	38	5	7	1	1	2
subtype1	4	7	1	6	1	2	0	0	0
subtype2	13	5	0	6	0	1	1	0	0
subtype3	10	6	1	6	2	2	0	1	1
subtype4	25	15	0	9	1	0	0	0	0
subtype5	12	6	0	11	1	2	0	0	1

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 = 0.445 (Fisher's exact test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	68	43	49	10
subtype1	5	7	8	1
subtype2	14	5	8	2
subtype3	11	8	9	4
subtype4	26	15	11	1
subtype5	12	8	13	2

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 = 0.519 (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	108	3
subtype1	13	1
subtype2	17	0
subtype3	22	1
subtype4	29	0
subtype5	27	1

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 = 0.421 (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	128	3	40
subtype1	18	0	3
subtype2	21	1	7
subtype3	21	1	10
subtype4	39	0	15
subtype5	29	1	5

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 = 0.0387 (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	64	107
subtype1	8	13
subtype2	6	23
subtype3	13	19
subtype4	17	37
subtype5	20	15

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 = 0.0232 (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	2	166	3
subtype1	0	20	1
subtype2	2	27	0
subtype3	0	32	0
subtype4	0	54	0
subtype5	0	33	2

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 = 0.141 (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	141	11	1	13
subtype1	17	1	0	3
subtype2	25	2	1	1
subtype3	21	4	0	3
subtype4	50	2	0	1
subtype5	28	2	0	5

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 = 0.0116 (Fisher's exact test), Q value = 0.87

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	41	8	115
subtype1	0	8	0	13
subtype2	0	9	3	15
subtype3	0	4	2	24
subtype4	1	6	3	42
subtype5	0	14	0	21

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 = 0.248 (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	6	151
subtype1	0	20
subtype2	2	25
subtype3	0	28
subtype4	1	47
subtype5	3	31

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	18	38	30	63	22

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0828 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	167	66	0.0 - 113.0 (13.6)
subtype1	17	10	0.0 - 83.6 (8.7)
subtype2	37	14	0.2 - 102.7 (21.5)
subtype3	29	14	0.2 - 113.0 (11.0)
subtype4	62	23	0.1 - 80.8 (13.7)
subtype5	22	5	0.3 - 107.1 (12.9)

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 = 6.14e-05 (Kruskal-Wallis (anova)), Q value = 0.0053

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

	nPatients	Mean (Std.Dev)
ALL	169	61.5 (13.7)
subtype1	18	62.4 (13.0)
subtype2	36	68.8 (9.7)
subtype3	30	60.1 (15.1)
subtype4	63	56.3 (14.3)
subtype5	22	65.3 (10.1)

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 = 0.923 (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	64	39	2	38	5	7	1	1	2
subtype1	6	3	0	5	1	1	0	0	1
subtype2	13	9	1	7	2	1	0	1	0
subtype3	11	10	0	7	0	1	0	0	1
subtype4	23	12	1	16	1	4	1	0	0
subtype5	11	5	0	3	1	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 = 0.867 (Fisher's exact test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	68	43	49	10
subtype1	6	4	6	2
subtype2	14	10	11	3
subtype3	11	10	7	2
subtype4	25	14	21	3
subtype5	12	5	4	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 = 0.91 (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	108	3
subtype1	12	0
subtype2	22	1
subtype3	21	0
subtype4	39	2
subtype5	14	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 = 0.0312 (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	128	3	40
subtype1	11	1	6
subtype2	26	0	12
subtype3	28	1	1
subtype4	47	1	15
subtype5	16	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 = 0.0805 (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	64	107
subtype1	6	12
subtype2	14	24
subtype3	17	13
subtype4	23	40
subtype5	4	18

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 = 0.696 (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	2	166	3
subtype1	0	18	0
subtype2	0	38	0
subtype3	0	30	0
subtype4	2	58	3
subtype5	0	22	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 = 0.452 (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	141	11	1	13
subtype1	11	3	0	1
subtype2	31	3	0	3
subtype3	24	2	0	4
subtype4	54	3	1	5
subtype5	21	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 = 0.0197 (Fisher's exact test), Q value = 1

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	41	8	115
subtype1	0	4	2	12
subtype2	0	3	1	31
subtype3	0	12	0	17
subtype4	0	19	4	38
subtype5	1	3	1	17

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 = 0.388 (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	6	151
subtype1	0	17
subtype2	0	34
subtype3	2	26
subtype4	4	55
subtype5	0	19

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 = 176
Number of clustering approaches = 8
Number of selected clinical features = 11
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

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

References

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

[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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