Liver Hepatocellular Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
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 6 different clustering approaches and 8 clinical features across 69 patients, one significant finding 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 do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.

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

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 6 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL
Time to Death logrank test 0.434
(1.00) 		0.729
(1.00) 		0.862
(1.00) 		0.838
(1.00) 		0.676
(1.00) 		0.74
(1.00)
AGE ANOVA 0.896
(1.00) 		0.0437
(1.00) 		0.175
(1.00) 		0.058
(1.00) 		0.0671
(1.00) 		0.00482
(0.202)
GENDER Fisher's exact test 0.404
(1.00) 		0.502
(1.00) 		0.0916
(1.00) 		0.0536
(1.00) 		0.3
(1.00) 		0.141
(1.00)
DISTANT METASTASIS Chi-square test 0.516
(1.00) 		0.483
(1.00) 		0.572
(1.00) 		0.593
(1.00) 		0.781
(1.00) 		0.879
(1.00)
LYMPH NODE METASTASIS Chi-square test 0.444
(1.00) 		0.5
(1.00) 		0.293
(1.00) 		0.463
(1.00) 		0.259
(1.00) 		0.671
(1.00)
COMPLETENESS OF RESECTION Chi-square test 0.49
(1.00) 		0.499
(1.00) 		0.335
(1.00) 		0.337
(1.00) 		0.418
(1.00) 		0.132
(1.00)
TUMOR STAGECODE ANOVA
NEOPLASM DISEASESTAGE Chi-square test 0.464
(1.00) 		0.362
(1.00) 		0.0279
(1.00) 		0.179
(1.00) 		0.412
(1.00) 		0.865
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 17 25 26
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.434 (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 62 26 0.1 - 90.7 (14.0)
subtype1 16 7 0.1 - 90.7 (7.8)
subtype2 22 8 0.4 - 79.4 (22.5)
subtype3 24 11 0.3 - 83.6 (14.3)

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 = 0.896 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 66 61.5 (14.2)
subtype1 16 60.1 (15.8)
subtype2 25 62.2 (13.6)
subtype3 25 61.6 (14.3)

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 'GENDER'

P value = 0.404 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 24 44
subtype1 4 13
subtype2 11 14
subtype3 9 17

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

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.516 (Chi-square test), Q value = 1

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

nPatients M0 M1 MX
ALL 45 1 22
subtype1 10 1 6
subtype2 17 0 8
subtype3 18 0 8

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.444 (Chi-square test), Q value = 1

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

nPatients N0 N1 NX
ALL 46 1 20
subtype1 12 0 5
subtype2 19 0 5
subtype3 15 1 10

Figure S5.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.49 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 46 8 1 8
subtype1 11 3 1 1
subtype2 17 2 0 2
subtype3 18 3 0 5

Figure S6.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.464 (Chi-square test), Q value = 1

Table S8.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIIA STAGE IIIB STAGE IIIC STAGE IVB
ALL 28 12 13 4 1 1
subtype1 8 5 2 0 0 1
subtype2 10 3 5 3 0 0
subtype3 10 4 6 1 1 0

Figure S7.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S9.  Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 20 18 30
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.729 (logrank test), Q value = 1

Table S10.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 61 25 0.1 - 90.7 (13.8)
subtype1 16 8 0.4 - 90.7 (22.5)
subtype2 16 7 0.1 - 66.3 (8.7)
subtype3 29 10 0.3 - 83.6 (8.3)

Figure S8.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0437 (ANOVA), Q value = 1

Table S11.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 66 60.6 (14.7)
subtype1 19 58.5 (17.0)
subtype2 18 55.0 (17.0)
subtype3 29 65.5 (9.8)

Figure S9.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.502 (Fisher's exact test), Q value = 1

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 25 43
subtype1 9 11
subtype2 7 11
subtype3 9 21

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.483 (Chi-square test), Q value = 1

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 46 1 21
subtype1 13 0 7
subtype2 13 1 4
subtype3 20 0 10

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.5 (Chi-square test), Q value = 1

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 47 1 19
subtype1 15 0 4
subtype2 14 0 4
subtype3 18 1 11

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.499 (Chi-square test), Q value = 1

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 45 8 1 9
subtype1 13 3 0 1
subtype2 12 1 1 4
subtype3 20 4 0 4

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.362 (Chi-square test), Q value = 1

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIIA STAGE IIIB STAGE IIIC STAGE IVB
ALL 27 12 14 4 1 1
subtype1 6 4 5 3 0 0
subtype2 6 4 3 1 0 1
subtype3 15 4 6 0 1 0

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S17.  Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5 6 7 9
Number of samples 16 6 6 11 11 3 4 1
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.862 (logrank test), Q value = 1

Table S18.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 53 23 0.1 - 83.6 (14.4)
subtype1 13 7 3.0 - 49.0 (19.8)
subtype2 6 3 6.3 - 55.2 (12.3)
subtype3 5 2 6.0 - 46.8 (14.3)
subtype4 11 7 0.3 - 83.6 (23.3)
subtype5 11 4 0.6 - 79.4 (6.7)
subtype6 3 0 1.2 - 13.8 (8.3)
subtype7 4 0 0.1 - 34.1 (15.6)

Figure S15.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.175 (ANOVA), Q value = 1

Table S19.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 57 60.5 (14.3)
subtype1 16 57.0 (16.7)
subtype2 6 57.5 (12.6)
subtype3 6 61.8 (12.5)
subtype4 11 67.9 (8.5)
subtype5 11 61.5 (14.0)
subtype6 3 69.0 (5.6)
subtype7 4 47.0 (19.6)

Figure S16.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0916 (Chi-square test), Q value = 1

Table S20.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 21 36
subtype1 8 8
subtype2 3 3
subtype3 1 5
subtype4 5 6
subtype5 0 11
subtype6 2 1
subtype7 2 2

Figure S17.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.572 (Chi-square test), Q value = 1

Table S21.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 38 1 18
subtype1 11 0 5
subtype2 3 1 2
subtype3 4 0 2
subtype4 7 0 4
subtype5 7 0 4
subtype6 3 0 0
subtype7 3 0 1

Figure S18.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.293 (Chi-square test), Q value = 1

Table S22.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 38 1 17
subtype1 10 0 5
subtype2 4 0 2
subtype3 5 0 1
subtype4 7 0 4
subtype5 8 0 3
subtype6 2 0 1
subtype7 2 1 1

Figure S19.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.335 (Chi-square test), Q value = 1

Table S23.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 40 7 1 6
subtype1 8 4 0 3
subtype2 5 0 1 0
subtype3 5 0 0 0
subtype4 8 1 0 2
subtype5 9 1 0 0
subtype6 2 1 0 0
subtype7 3 0 0 1

Figure S20.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0279 (Chi-square test), Q value = 1

Table S24.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIIA STAGE IIIB STAGE IIIC STAGE IVB
ALL 22 10 12 3 1 1
subtype1 2 3 7 1 0 0
subtype2 2 1 1 0 0 1
subtype3 3 0 1 1 0 0
subtype4 8 0 1 0 0 0
subtype5 5 3 2 0 0 0
subtype6 1 2 0 0 0 0
subtype7 1 1 0 1 1 0

Figure S21.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S25.  Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2
Number of samples 24 34
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.838 (logrank test), Q value = 1

Table S26.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 53 23 0.1 - 83.6 (14.4)
subtype1 21 8 0.1 - 55.2 (19.8)
subtype2 32 15 0.3 - 83.6 (14.0)

Figure S22.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.058 (t-test), Q value = 1

Table S27.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 57 60.5 (14.3)
subtype1 24 56.0 (16.5)
subtype2 33 63.7 (11.6)

Figure S23.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0536 (Fisher's exact test), Q value = 1

Table S28.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 22 36
subtype1 13 11
subtype2 9 25

Figure S24.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.593 (Chi-square test), Q value = 1

Table S29.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 38 1 19
subtype1 17 0 7
subtype2 21 1 12

Figure S25.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.463 (Chi-square test), Q value = 1

Table S30.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 39 1 17
subtype1 15 1 7
subtype2 24 0 10

Figure S26.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.337 (Chi-square test), Q value = 1

Table S31.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 41 7 1 6
subtype1 15 4 0 4
subtype2 26 3 1 2

Figure S27.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.179 (Chi-square test), Q value = 1

Table S32.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIIA STAGE IIIB STAGE IIIC STAGE IVB
ALL 22 11 12 3 1 1
subtype1 5 5 7 2 1 0
subtype2 17 6 5 1 0 1

Figure S28.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S33.  Get Full Table Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 25 12 31
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.676 (logrank test), Q value = 1

Table S34.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 61 25 0.1 - 83.6 (13.8)
subtype1 22 11 0.1 - 69.6 (14.6)
subtype2 9 4 1.1 - 83.6 (5.9)
subtype3 30 10 0.3 - 79.4 (19.4)

Figure S29.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0671 (ANOVA), Q value = 1

Table S35.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 66 60.8 (14.9)
subtype1 25 56.0 (15.6)
subtype2 11 59.5 (17.8)
subtype3 30 65.2 (12.1)

Figure S30.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.3 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 25 43
subtype1 11 14
subtype2 2 10
subtype3 12 19

Figure S31.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.781 (Chi-square test), Q value = 1

Table S37.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 45 1 22
subtype1 16 1 8
subtype2 8 0 4
subtype3 21 0 10

Figure S32.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.259 (Chi-square test), Q value = 1

Table S38.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 46 1 20
subtype1 17 0 7
subtype2 11 0 1
subtype3 18 1 12

Figure S33.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.418 (Chi-square test), Q value = 1

Table S39.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 45 8 1 9
subtype1 17 3 1 4
subtype2 5 3 0 1
subtype3 23 2 0 4

Figure S34.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.412 (Chi-square test), Q value = 1

Table S40.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIIA STAGE IIIB STAGE IIIC STAGE IVB
ALL 27 12 14 4 1 1
subtype1 6 5 5 3 0 1
subtype2 5 2 4 1 0 0
subtype3 16 5 5 0 1 0

Figure S35.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S41.  Get Full Table Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 10 9 24 25
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.74 (logrank test), Q value = 1

Table S42.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 61 25 0.1 - 83.6 (13.8)
subtype1 10 2 0.3 - 79.4 (3.9)
subtype2 7 4 1.1 - 83.6 (11.6)
subtype3 21 11 0.1 - 69.6 (19.8)
subtype4 23 8 2.6 - 66.3 (19.1)

Figure S36.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00482 (ANOVA), Q value = 0.2

Table S43.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 66 60.8 (14.9)
subtype1 10 65.5 (6.4)
subtype2 9 60.2 (16.6)
subtype3 24 52.8 (17.0)
subtype4 23 67.2 (10.7)

Figure S37.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.141 (Fisher's exact test), Q value = 1

Table S44.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 25 43
subtype1 1 9
subtype2 2 7
subtype3 10 14
subtype4 12 13

Figure S38.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.879 (Chi-square test), Q value = 1

Table S45.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'DISTANT.METASTASIS'

nPatients M0 M1 MX
ALL 45 1 22
subtype1 6 0 4
subtype2 6 0 3
subtype3 15 1 8
subtype4 18 0 7

Figure S39.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.671 (Chi-square test), Q value = 1

Table S46.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 46 1 20
subtype1 6 0 4
subtype2 8 0 1
subtype3 15 1 7
subtype4 17 0 8

Figure S40.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.132 (Chi-square test), Q value = 1

Table S47.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 45 8 1 9
subtype1 8 0 0 0
subtype2 3 3 0 0
subtype3 16 2 1 5
subtype4 18 3 0 4

Figure S41.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.865 (Chi-square test), Q value = 1

Table S48.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIIA STAGE IIIB STAGE IIIC STAGE IVB
ALL 27 12 14 4 1 1
subtype1 5 2 3 0 0 0
subtype2 3 1 4 1 0 0
subtype3 8 5 3 2 1 1
subtype4 11 4 4 1 0 0

Figure S42.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Methods & Data
Input

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

  • Clinical data file = LIHC-TP.clin.merged.picked.txt

  • Number of patients = 69

  • Number of clustering approaches = 6

  • Number of selected clinical features = 8

  • 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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' 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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] 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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[8] 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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