Correlation between aggregated molecular cancer subtypes and selected clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

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/C1K936DF

Overview

Introduction

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

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 11 clinical features across 317 patients, 10 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'AGE' and 'GENDER'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.T.STAGE', 'GENDER', and 'RACE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'AGE' and 'NEOPLASM.DISEASESTAGE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 10 significant findings detected.


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

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	93	106	112

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

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

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

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

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

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

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

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

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	146	72	2	52	7	7	2	1	2
subtype1	51	20	0	14	0	0	1	1	0
subtype2	41	30	1	20	3	2	1	0	1
subtype3	54	22	1	18	4	5	0	0	1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.121 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	155	78	64	12
subtype1	54	21	16	1
subtype2	42	33	25	6
subtype3	59	24	23	5

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

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

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

nPatients	0	1
ALL	222	3
subtype1	65	1
subtype2	78	1
subtype3	79	1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.813 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	MX
ALL	238	4	69
subtype1	71	1	21
subtype2	84	2	20
subtype3	83	1	28

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	303	6
subtype1	0	92	1
subtype2	0	103	3
subtype3	2	108	2

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0697 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	274	14	1	16
subtype1	84	6	1	1
subtype2	91	6	0	8
subtype3	99	2	0	7

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0396 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	141	15	145
subtype1	1	45	4	39
subtype2	0	58	4	43
subtype3	0	38	7	63

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	59	108	76

'METHLYATION CNMF' versus 'Time to Death'

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

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

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

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

'METHLYATION CNMF' versus 'AGE'

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

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

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

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

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

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	91	57	2	52	7	9	2	1	2
subtype1	24	11	0	15	3	4	0	0	0
subtype2	42	27	1	20	3	3	0	1	1
subtype3	25	19	1	17	1	2	2	0	1

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

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.586 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	100	63	65	13
subtype1	25	12	18	3
subtype2	48	29	23	7
subtype3	27	22	24	3

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

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

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	153	4
subtype1	43	1
subtype2	60	2
subtype3	50	1

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

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.217 (Fisher's exact test), Q value = 1

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

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

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

'METHLYATION CNMF' versus 'GENDER'

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

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

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

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

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

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	235	6
subtype1	2	56	1
subtype2	0	106	2
subtype3	0	73	3

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

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.74 (Fisher's exact test), Q value = 1

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

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

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

'METHLYATION CNMF' versus 'RACE'

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

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	71	15	147
subtype1	0	16	6	34
subtype2	1	22	8	74
subtype3	0	33	1	39

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

'METHLYATION CNMF' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4	5	6
Number of samples	28	58	43	25	41	39

'RNAseq CNMF subtypes' versus 'Time to Death'

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

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

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

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

'RNAseq CNMF subtypes' versus 'AGE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	85	56	2	51	6	9	2	1	2
subtype1	5	10	0	7	2	1	0	0	0
subtype2	29	9	1	12	1	0	0	0	1
subtype3	9	13	1	11	1	3	1	0	1
subtype4	13	4	0	2	1	1	1	1	0
subtype5	13	10	0	10	0	3	0	0	0
subtype6	16	10	0	9	1	1	0	0	0

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0581 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	94	62	63	13
subtype1	5	12	8	3
subtype2	30	9	14	4
subtype3	12	14	15	2
subtype4	16	4	4	1
subtype5	14	13	12	2
subtype6	17	10	10	1

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.304 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	146	4
subtype1	15	0
subtype2	37	0
subtype3	30	2
subtype4	12	1
subtype5	26	1
subtype6	26	0

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0496 (Fisher's exact test), Q value = 1

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

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

Figure S28. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

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

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

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

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	227	5
subtype1	0	28	0
subtype2	0	58	0
subtype3	0	41	2
subtype4	0	25	0
subtype5	2	36	3
subtype6	0	39	0

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

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.043 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	198	13	1	16
subtype1	18	4	0	5
subtype2	48	4	0	5
subtype3	39	2	0	2
subtype4	20	1	1	1
subtype5	36	2	0	3
subtype6	37	0	0	0

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0278 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	66	15	143
subtype1	0	9	4	14
subtype2	0	11	2	41
subtype3	0	20	1	21
subtype4	0	3	2	17
subtype5	0	15	2	24
subtype6	1	8	4	26

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	85	113	36

'RNAseq cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'AGE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	85	56	2	51	6	9	2	1	2
subtype1	16	25	1	25	4	5	1	0	1
subtype2	54	21	1	20	1	3	1	1	1
subtype3	15	10	0	6	1	1	0	0	0

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

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

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

nPatients	T0+T1	T2	T3	T4
ALL	94	62	63	13
subtype1	18	29	33	5
subtype2	60	22	23	7
subtype3	16	11	7	1

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.385 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	146	4
subtype1	58	3
subtype2	65	1
subtype3	23	0

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.209 (Fisher's exact test), Q value = 1

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

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

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

'RNAseq cHierClus subtypes' versus 'GENDER'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	227	5
subtype1	0	82	3
subtype2	2	109	2
subtype3	0	36	0

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

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.503 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	198	13	1	16
subtype1	71	6	0	7
subtype2	94	6	1	9
subtype3	33	1	0	0

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

'RNAseq cHierClus subtypes' versus 'RACE'

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

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	66	15	143
subtype1	0	39	3	42
subtype2	0	21	8	76
subtype3	1	6	4	25

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3	4
Number of samples	52	112	43	105

'MIRSEQ CNMF' versus 'Time to Death'

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

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

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

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

'MIRSEQ CNMF' versus 'AGE'

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

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

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

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

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

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	144	71	2	54	7	9	2	1	2
subtype1	29	6	1	7	3	1	0	1	1
subtype2	52	28	0	23	2	3	0	0	0
subtype3	17	10	1	10	0	2	1	0	0
subtype4	46	27	0	14	2	3	1	0	1

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

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.677 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	153	77	67	13
subtype1	31	7	11	3
subtype2	53	29	25	4
subtype3	19	11	11	2
subtype4	50	30	20	4

Figure S48. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.758 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	222	4
subtype1	36	1
subtype2	80	1
subtype3	35	1
subtype4	71	1

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

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.019 (Fisher's exact test), Q value = 1

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

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

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

'MIRSEQ CNMF' versus 'GENDER'

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

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

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

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

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

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	304	6
subtype1	0	51	1
subtype2	0	112	0
subtype3	0	42	1
subtype4	2	99	4

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

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.75 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	274	14	1	17
subtype1	40	4	0	4
subtype2	101	4	0	6
subtype3	38	2	0	3
subtype4	95	4	1	4

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.274 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	144	15	143
subtype1	0	18	4	28
subtype2	1	51	4	53
subtype3	0	26	3	14
subtype4	0	49	4	48

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

'MIRSEQ CNMF' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels	1	2	3	4	5
Number of samples	35	82	47	105	43

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

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

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	144	71	2	54	7	9	2	1	2
subtype1	18	4	0	9	1	1	0	0	1
subtype2	36	20	1	10	4	1	0	1	0
subtype3	23	11	0	10	0	1	0	0	0
subtype4	45	26	1	18	1	5	2	0	1
subtype5	22	10	0	7	1	1	0	0	0

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.855 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	153	77	67	13
subtype1	18	5	10	2
subtype2	40	21	16	5
subtype3	24	12	11	0
subtype4	48	29	22	5
subtype5	23	10	8	1

Figure S59. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

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

Table S66. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	222	4
subtype1	26	0
subtype2	51	1
subtype3	35	1
subtype4	77	2
subtype5	33	0

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.0931 (Fisher's exact test), Q value = 1

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

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

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	304	6
subtype1	0	35	0
subtype2	0	80	2
subtype3	0	46	1
subtype4	2	100	3
subtype5	0	43	0

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

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.311 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	274	14	1	17
subtype1	26	3	0	3
subtype2	70	5	0	5
subtype3	42	1	0	4
subtype4	94	5	1	5
subtype5	42	0	0	0

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0393 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	144	15	143
subtype1	0	15	5	15
subtype2	0	28	4	45
subtype3	0	29	1	16
subtype4	0	53	4	45
subtype5	1	19	1	22

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	83	72	98	38

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

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'AGE'

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

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

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

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

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

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

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	132	69	2	49	6	8	2	1	2
subtype1	30	20	0	22	5	5	0	0	0
subtype2	29	18	0	10	0	3	1	1	1
subtype3	56	21	1	10	0	0	0	0	1
subtype4	17	10	1	7	1	0	1	0	0

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0245 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	141	75	61	12
subtype1	31	21	26	5
subtype2	34	20	13	5
subtype3	59	23	12	2
subtype4	17	11	10	0

Figure S70. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

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

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

nPatients	0	1
ALL	206	4
subtype1	60	3
subtype2	46	1
subtype3	70	0
subtype4	30	0

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0173 (Fisher's exact test), Q value = 1

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

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

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	97	194
subtype1	35	48
subtype2	24	48
subtype3	25	73
subtype4	13	25

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.192 (Fisher's exact test), Q value = 1

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	1	284	6
subtype1	0	80	3
subtype2	1	69	2
subtype3	0	98	0
subtype4	0	37	1

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

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.233 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	255	14	1	15
subtype1	70	5	0	5
subtype2	60	6	1	3
subtype3	92	2	0	3
subtype4	33	1	0	4

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.844 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	138	15	128
subtype1	0	42	4	36
subtype2	0	32	4	33
subtype3	1	41	6	45
subtype4	0	23	1	14

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

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5
Number of samples	57	72	86	29	47

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

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'AGE'

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

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

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

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

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

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

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	132	69	2	49	6	8	2	1	2
subtype1	21	13	0	17	1	1	0	0	0
subtype2	31	16	1	10	4	1	0	1	0
subtype3	38	22	1	11	0	5	2	0	1
subtype4	17	3	0	6	1	1	0	0	1
subtype5	25	15	0	5	0	0	0	0	0

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

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.095 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	141	75	61	12
subtype1	23	14	19	1
subtype2	35	17	15	5
subtype3	40	26	15	4
subtype4	17	3	7	2
subtype5	26	15	5	0

Figure S81. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.943 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	206	4
subtype1	42	1
subtype2	44	1
subtype3	61	2
subtype4	22	0
subtype5	37	0

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

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.101 (Fisher's exact test), Q value = 1

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	1	284	6
subtype1	0	55	2
subtype2	0	71	1
subtype3	1	82	3
subtype4	0	29	0
subtype5	0	47	0

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

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.221 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	255	14	1	15
subtype1	51	4	0	2
subtype2	61	3	0	6
subtype3	75	4	1	6
subtype4	22	3	0	1
subtype5	46	0	0	0

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.00564 (Fisher's exact test), Q value = 0.42

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	138	15	128
subtype1	0	38	1	18
subtype2	0	21	6	40
subtype3	0	43	4	36
subtype4	0	11	3	15
subtype5	1	25	1	19

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

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

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

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

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

Methods & Data

Input

Cluster data file = LIHC-TP.mergedcluster.txt
Clinical data file = LIHC-TP.merged_data.txt
Number of patients = 317
Number of clustering approaches = 8
Number of selected clinical features = 11
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

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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