Correlation between molecular cancer subtypes and selected clinical features
Breast Invasive Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1C8276K
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Breast Invasive Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1C8276K
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 10 different clustering approaches and 9 clinical features across 873 patients, 7 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 8 subtypes that correlate to 'AGE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.

  • 5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'GENDER'.

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NEOPLASM.DISEASESTAGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 7 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER RADIATIONS
RADIATION
REGIMENINDICATION 		DISTANT
METASTASIS 		LYMPH
NODE
METASTASIS 		NUMBER
OF
LYMPH
NODES 		TUMOR
STAGECODE 		NEOPLASM
DISEASESTAGE
Statistical Tests logrank test ANOVA Fisher's exact test Fisher's exact test Chi-square test Chi-square test ANOVA ANOVA Chi-square test
mRNA CNMF subtypes 0.159
(1.00) 		7.84e-07
(6.27e-05) 		0.0612
(1.00) 		0.921
(1.00) 		0.817
(1.00) 		0.0163
(1.00) 		0.415
(1.00) 		0.325
(1.00)
mRNA cHierClus subtypes 0.609
(1.00) 		0.000437
(0.0341) 		0.493
(1.00) 		0.105
(1.00) 		0.312
(1.00) 		0.578
(1.00) 		0.692
(1.00) 		0.516
(1.00)
Copy Number Ratio CNMF subtypes 0.376
(1.00) 		0.011
(0.782) 		0.000381
(0.0301) 		0.454
(1.00) 		0.803
(1.00) 		0.504
(1.00) 		0.123
(1.00) 		0.593
(1.00)
METHLYATION CNMF 0.162
(1.00) 		0.0015
(0.113) 		0.0712
(1.00) 		0.183
(1.00) 		0.736
(1.00) 		0.267
(1.00) 		0.273
(1.00) 		0.0393
(1.00)
RPPA CNMF subtypes 0.0477
(1.00) 		0.0287
(1.00) 		0.0564
(1.00) 		0.981
(1.00) 		0.599
(1.00) 		0.148
(1.00) 		0.0305
(1.00) 		0.169
(1.00)
RPPA cHierClus subtypes 0.318
(1.00) 		0.00121
(0.0928) 		0.154
(1.00) 		0.917
(1.00) 		0.748
(1.00) 		0.208
(1.00) 		0.0553
(1.00) 		0.273
(1.00)
RNAseq CNMF subtypes 0.447
(1.00) 		0.0198
(1.00) 		0.0218
(1.00) 		0.337
(1.00) 		0.0982
(1.00) 		0.891
(1.00) 		0.112
(1.00) 		0.0252
(1.00)
RNAseq cHierClus subtypes 0.0495
(1.00) 		0.0442
(1.00) 		0.00631
(0.454) 		0.252
(1.00) 		0.294
(1.00) 		0.113
(1.00) 		0.949
(1.00) 		0.00165
(0.122)
MIRSEQ CNMF 0.106
(1.00) 		0.072
(1.00) 		0.209
(1.00) 		0.374
(1.00) 		0.766
(1.00) 		0.27
(1.00) 		0.0313
(1.00) 		0.0014
(0.106)
MIRSEQ CHIERARCHICAL 0.0899
(1.00) 		0.0986
(1.00) 		0.913
(1.00) 		0.903
(1.00) 		0.107
(1.00) 		0.0717
(1.00) 		0.228
(1.00) 		0.00611
(0.446)
Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1.  Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6 7 8
Number of samples 126 39 23 100 74 20 104 40
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.159 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 494 65 0.1 - 223.4 (24.2)
subtype1 116 17 0.1 - 177.4 (24.4)
subtype2 38 3 0.1 - 157.4 (43.5)
subtype3 21 3 0.3 - 223.4 (14.0)
subtype4 95 12 0.1 - 211.5 (22.6)
subtype5 65 14 0.1 - 189.0 (24.6)
subtype6 19 2 0.2 - 97.5 (36.3)
subtype7 101 10 0.3 - 220.9 (19.0)
subtype8 39 4 0.3 - 112.4 (23.4)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 7.84e-07 (ANOVA), Q value = 6.3e-05

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

nPatients Mean (Std.Dev)
ALL 526 57.9 (13.2)
subtype1 126 58.5 (14.3)
subtype2 39 49.3 (10.7)
subtype3 23 58.8 (13.8)
subtype4 100 53.9 (12.6)
subtype5 74 58.6 (12.5)
subtype6 20 60.4 (9.9)
subtype7 104 62.4 (12.3)
subtype8 40 59.8 (13.0)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0612 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 520 6
subtype1 122 4
subtype2 39 0
subtype3 22 1
subtype4 100 0
subtype5 74 0
subtype6 19 1
subtype7 104 0
subtype8 40 0

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

'mRNA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.921 (Chi-square test), Q value = 1

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 148 378
subtype1 33 93
subtype2 11 28
subtype3 5 18
subtype4 34 66
subtype5 21 53
subtype6 5 15
subtype7 28 76
subtype8 11 29

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.817 (Chi-square test), Q value = 1

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 369 8 10
subtype1 0 94 1 2
subtype2 1 28 1 0
subtype3 0 18 1 0
subtype4 0 62 2 1
subtype5 0 54 1 1
subtype6 0 17 0 1
subtype7 1 68 2 4
subtype8 0 28 0 1

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.0163 (Chi-square test), Q value = 1

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N1 N1A N1B N1C N1MI N2 N2A N3 N3A NX
ALL 118 7 59 41 62 14 2 11 24 24 5 16 6
subtype1 24 0 18 14 18 4 0 4 3 6 1 3 2
subtype2 3 1 3 3 9 1 0 2 3 1 1 2 1
subtype3 2 0 3 2 2 1 0 0 5 2 0 1 1
subtype4 27 1 13 6 4 3 1 1 5 2 0 2 0
subtype5 13 0 4 10 8 1 0 1 4 8 3 4 0
subtype6 6 0 4 1 3 0 0 1 0 2 0 0 1
subtype7 29 5 11 3 13 2 1 2 4 2 0 3 0
subtype8 14 0 3 2 5 2 0 0 0 1 0 1 1

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'mRNA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.415 (ANOVA), Q value = 1

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 406 1.8 (3.5)
subtype1 89 1.6 (2.3)
subtype2 34 1.9 (2.4)
subtype3 17 1.6 (2.3)
subtype4 91 1.5 (3.3)
subtype5 46 2.8 (3.5)
subtype6 15 1.2 (2.5)
subtype7 79 1.8 (4.0)
subtype8 35 2.6 (6.3)

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.325 (Chi-square test), Q value = 1

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 41 22 2 136 83 60 12 14 8 11
subtype1 6 7 2 32 26 13 6 3 1 1
subtype2 1 1 0 8 9 7 0 2 1 1
subtype3 1 1 0 5 2 6 2 0 1 1
subtype4 7 4 0 28 13 6 1 2 2 2
subtype5 4 0 0 16 13 14 2 5 1 1
subtype6 3 2 0 5 4 3 0 0 0 1
subtype7 13 6 0 29 12 8 1 1 2 3
subtype8 6 1 0 13 4 3 0 1 0 1

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 143 131 252
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.609 (logrank test), Q value = 1

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 494 65 0.1 - 223.4 (24.2)
subtype1 140 14 0.3 - 157.4 (26.4)
subtype2 120 18 0.1 - 211.5 (23.8)
subtype3 234 33 0.1 - 223.4 (20.2)

Figure S9.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.000437 (ANOVA), Q value = 0.034

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 526 57.9 (13.2)
subtype1 143 56.4 (12.8)
subtype2 131 55.1 (13.1)
subtype3 252 60.2 (13.2)

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.493 (Fisher's exact test), Q value = 1

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 520 6
subtype1 141 2
subtype2 131 0
subtype3 248 4

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.105 (Fisher's exact test), Q value = 1

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 148 378
subtype1 42 101
subtype2 45 86
subtype3 61 191

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.312 (Chi-square test), Q value = 1

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 369 8 10
subtype1 2 102 1 3
subtype2 0 84 2 1
subtype3 0 183 5 6

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.578 (Chi-square test), Q value = 1

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N1 N1A N1B N1C N1MI N2 N2A N3 N3A NX
ALL 118 7 59 41 62 14 2 11 24 24 5 16 6
subtype1 32 5 18 11 19 3 0 1 5 6 1 4 3
subtype2 32 1 14 10 8 3 1 1 7 5 1 4 0
subtype3 54 1 27 20 35 8 1 9 12 13 3 8 3

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'mRNA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.692 (ANOVA), Q value = 1

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 406 1.8 (3.5)
subtype1 125 2.0 (4.1)
subtype2 114 1.7 (3.3)
subtype3 167 1.7 (3.2)

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 41 22 2 136 83 60 12 14 8 11
subtype1 16 8 0 40 18 17 1 3 1 4
subtype2 8 5 0 35 17 10 2 5 2 3
subtype3 17 9 2 61 48 33 9 6 5 4

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S19.  Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 312 218 67 213 47
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.376 (logrank test), Q value = 1

Table S20.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 798 94 0.0 - 223.4 (18.1)
subtype1 292 29 0.0 - 223.4 (20.6)
subtype2 201 21 0.1 - 186.4 (17.0)
subtype3 66 8 0.0 - 189.0 (18.1)
subtype4 195 27 0.0 - 211.5 (17.0)
subtype5 44 9 0.7 - 220.9 (20.3)

Figure S17.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.011 (ANOVA), Q value = 0.78

Table S21.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 856 58.5 (13.2)
subtype1 311 57.8 (13.1)
subtype2 218 58.6 (14.2)
subtype3 67 61.9 (11.1)
subtype4 213 57.2 (12.8)
subtype5 47 63.1 (12.2)

Figure S18.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.000381 (Chi-square test), Q value = 0.03

Table S22.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 848 9
subtype1 312 0
subtype2 210 8
subtype3 66 1
subtype4 213 0
subtype5 47 0

Figure S19.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.454 (Chi-square test), Q value = 1

Table S23.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 212 645
subtype1 68 244
subtype2 52 166
subtype3 19 48
subtype4 59 154
subtype5 14 33

Figure S20.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.803 (Chi-square test), Q value = 1

Table S24.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 640 9 75
subtype1 1 233 3 31
subtype2 1 167 4 17
subtype3 0 52 1 2
subtype4 0 150 1 19
subtype5 0 38 0 6

Figure S21.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.504 (Chi-square test), Q value = 1

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B NX
ALL 233 13 88 1 87 111 26 2 20 43 48 14 28 2 10
subtype1 85 7 40 1 32 39 10 2 7 14 13 6 8 1 3
subtype2 51 2 18 0 27 28 5 0 9 14 16 6 9 0 4
subtype3 17 0 8 0 3 15 2 0 0 1 4 1 3 0 1
subtype4 70 3 18 0 19 20 5 0 3 13 11 0 6 1 1
subtype5 10 1 4 0 6 9 4 0 1 1 4 1 2 0 1

Figure S22.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.123 (ANOVA), Q value = 1

Table S26.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 714 2.2 (4.4)
subtype1 277 1.9 (3.9)
subtype2 163 2.7 (4.3)
subtype3 52 3.0 (5.7)
subtype4 181 2.0 (4.7)
subtype5 41 2.9 (5.0)

Figure S23.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

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

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

Table S27.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 71 45 2 8 246 165 2 107 22 36 9 12
subtype1 37 24 0 5 82 58 1 35 7 13 3 3
subtype2 12 7 2 1 66 41 1 32 10 11 4 2
subtype3 4 1 0 1 19 16 0 7 2 2 1 2
subtype4 15 10 0 1 62 41 0 25 3 7 1 4
subtype5 3 3 0 0 17 9 0 8 0 3 0 1

Figure S24.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S28.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6
Number of samples 90 129 87 137 36 80
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.162 (logrank test), Q value = 1

Table S29.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 530 61 0.0 - 223.4 (17.7)
subtype1 85 13 0.2 - 211.5 (19.2)
subtype2 121 10 0.3 - 223.4 (13.7)
subtype3 85 14 0.0 - 173.0 (13.0)
subtype4 130 13 0.1 - 194.3 (19.0)
subtype5 34 3 0.1 - 157.4 (20.4)
subtype6 75 8 0.0 - 130.2 (25.4)

Figure S25.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0015 (ANOVA), Q value = 0.11

Table S30.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 558 57.8 (13.1)
subtype1 90 56.5 (13.2)
subtype2 129 58.3 (12.4)
subtype3 87 63.1 (11.6)
subtype4 137 56.0 (14.3)
subtype5 36 56.2 (13.7)
subtype6 79 56.6 (11.7)

Figure S26.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0712 (Chi-square test), Q value = 1

Table S31.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 553 6
subtype1 90 0
subtype2 128 1
subtype3 85 2
subtype4 136 1
subtype5 34 2
subtype6 80 0

Figure S27.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.183 (Chi-square test), Q value = 1

Table S32.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 147 412
subtype1 30 60
subtype2 30 99
subtype3 20 67
subtype4 43 94
subtype5 6 30
subtype6 18 62

Figure S28.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.736 (Chi-square test), Q value = 1

Table S33.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 1 438 3 70
subtype1 0 71 1 9
subtype2 1 103 0 15
subtype3 0 65 0 13
subtype4 0 111 1 16
subtype5 0 28 1 4
subtype6 0 60 0 13

Figure S29.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.267 (Chi-square test), Q value = 1

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B NX
ALL 150 11 63 1 63 79 22 2 14 34 38 10 17 2 6
subtype1 34 2 10 0 8 10 3 0 0 5 6 0 2 1 0
subtype2 30 3 11 1 12 29 6 1 5 8 5 1 4 0 3
subtype3 22 2 10 0 10 9 4 0 0 7 6 2 4 0 2
subtype4 28 1 17 0 21 20 6 0 5 8 14 2 5 0 1
subtype5 11 0 3 0 3 5 2 0 0 3 1 3 2 0 0
subtype6 25 3 12 0 9 6 1 1 4 3 6 2 0 1 0

Figure S30.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.273 (ANOVA), Q value = 1

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 519 2.5 (4.6)
subtype1 86 1.9 (3.3)
subtype2 121 2.3 (4.2)
subtype3 80 3.1 (6.2)
subtype4 123 2.7 (4.3)
subtype5 32 3.6 (5.4)
subtype6 77 1.9 (4.7)

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0393 (Chi-square test), Q value = 1

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 46 37 2 8 165 119 2 85 13 27 3 4
subtype1 4 4 0 1 33 20 0 12 1 3 1 1
subtype2 10 10 0 2 35 34 0 19 3 5 0 1
subtype3 7 3 1 2 22 18 1 16 0 6 0 2
subtype4 8 8 1 0 40 32 0 25 7 6 1 0
subtype5 2 6 0 1 9 3 0 4 2 5 1 0
subtype6 15 6 0 2 26 12 1 9 0 2 0 0

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S37.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 152 136 120
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0477 (logrank test), Q value = 1

Table S38.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 380 44 0.1 - 189.0 (24.5)
subtype1 135 22 0.1 - 186.4 (22.8)
subtype2 132 11 0.2 - 146.5 (18.0)
subtype3 113 11 0.3 - 189.0 (28.5)

Figure S33.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0287 (ANOVA), Q value = 1

Table S39.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 408 57.9 (13.1)
subtype1 152 56.1 (13.4)
subtype2 136 60.1 (13.7)
subtype3 120 57.5 (11.7)

Figure S34.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0564 (Fisher's exact test), Q value = 1

Table S40.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 403 5
subtype1 152 0
subtype2 132 4
subtype3 119 1

Figure S35.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.981 (Fisher's exact test), Q value = 1

Table S41.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 125 283
subtype1 47 105
subtype2 42 94
subtype3 36 84

Figure S36.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.599 (Chi-square test), Q value = 1

Table S42.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 1 293 8 5
subtype1 0 108 4 1
subtype2 0 97 2 3
subtype3 1 88 2 1

Figure S37.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.148 (Chi-square test), Q value = 1

Table S43.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N1 N1A N1B N1C N1MI N2 N2A N3 N3A NX
ALL 86 7 47 36 53 4 1 10 20 22 4 13 4
subtype1 34 1 15 17 12 2 0 3 9 10 2 6 2
subtype2 26 4 18 7 30 1 1 3 4 2 1 4 1
subtype3 26 2 14 12 11 1 0 4 7 10 1 3 1

Figure S38.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0305 (ANOVA), Q value = 1

Table S44.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 330 1.9 (3.5)
subtype1 114 2.1 (3.6)
subtype2 113 1.2 (2.0)
subtype3 103 2.4 (4.5)

Figure S39.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.169 (Chi-square test), Q value = 1

Table S45.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 30 15 1 105 74 49 10 10 8 5
subtype1 6 5 0 39 27 20 2 7 4 3
subtype2 11 6 0 38 29 9 4 1 2 2
subtype3 13 4 1 28 18 20 4 2 2 0

Figure S40.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S46.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 163 115 130
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.318 (logrank test), Q value = 1

Table S47.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 380 44 0.1 - 189.0 (24.5)
subtype1 152 17 0.2 - 129.7 (24.2)
subtype2 112 10 0.2 - 173.0 (25.5)
subtype3 116 17 0.1 - 189.0 (21.9)

Figure S41.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00121 (ANOVA), Q value = 0.093

Table S48.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 408 57.9 (13.1)
subtype1 163 60.4 (13.6)
subtype2 115 57.6 (11.8)
subtype3 130 54.8 (13.0)

Figure S42.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.154 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 403 5
subtype1 159 4
subtype2 114 1
subtype3 130 0

Figure S43.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.917 (Fisher's exact test), Q value = 1

Table S50.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 125 283
subtype1 48 115
subtype2 36 79
subtype3 41 89

Figure S44.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.748 (Chi-square test), Q value = 1

Table S51.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 1 293 8 5
subtype1 0 121 3 3
subtype2 1 82 2 1
subtype3 0 90 3 1

Figure S45.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.208 (Chi-square test), Q value = 1

Table S52.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N1 N1A N1B N1C N1MI N2 N2A N3 N3A NX
ALL 86 7 47 36 53 4 1 10 20 22 4 13 4
subtype1 35 4 22 10 30 1 1 3 8 4 1 5 3
subtype2 21 2 12 11 16 0 0 4 5 10 1 3 1
subtype3 30 1 13 15 7 3 0 3 7 8 2 5 0

Figure S46.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0553 (ANOVA), Q value = 1

Table S53.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 330 1.9 (3.5)
subtype1 126 1.3 (2.1)
subtype2 99 2.4 (4.5)
subtype3 105 2.1 (3.7)

Figure S47.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.273 (Chi-square test), Q value = 1

Table S54.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 30 15 1 105 74 49 10 10 8 5
subtype1 14 6 0 43 34 17 5 2 3 3
subtype2 11 6 1 24 17 19 4 2 2 0
subtype3 5 3 0 38 23 13 1 6 3 2

Figure S48.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S55.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 238 149 450
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.447 (logrank test), Q value = 1

Table S56.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 780 94 0.0 - 223.4 (18.6)
subtype1 220 32 0.0 - 211.5 (19.3)
subtype2 143 13 0.3 - 194.3 (27.2)
subtype3 417 49 0.0 - 223.4 (17.0)

Figure S49.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0198 (ANOVA), Q value = 1

Table S57.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 836 58.2 (13.1)
subtype1 238 57.4 (12.6)
subtype2 148 56.2 (12.8)
subtype3 450 59.3 (13.4)

Figure S50.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0218 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 828 9
subtype1 238 0
subtype2 149 0
subtype3 441 9

Figure S51.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.337 (Fisher's exact test), Q value = 1

Table S59.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 209 628
subtype1 62 176
subtype2 43 106
subtype3 104 346

Figure S52.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0982 (Chi-square test), Q value = 1

Table S60.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 621 9 67
subtype1 0 176 2 15
subtype2 2 109 2 15
subtype3 0 336 5 37

Figure S53.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.891 (Chi-square test), Q value = 1

Table S61.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B NX
ALL 220 13 87 1 84 109 26 2 20 42 45 12 27 1 10
subtype1 70 3 24 0 25 23 7 1 4 12 12 1 7 1 3
subtype2 40 4 17 0 14 22 5 0 5 3 6 3 7 0 2
subtype3 110 6 46 1 45 64 14 1 11 27 27 8 13 0 5

Figure S54.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.112 (ANOVA), Q value = 1

Table S62.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 696 2.2 (4.3)
subtype1 198 2.1 (4.4)
subtype2 137 2.8 (6.1)
subtype3 361 1.9 (3.2)

Figure S55.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0252 (Chi-square test), Q value = 1

Table S63.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 68 44 3 8 240 160 2 100 20 32 9 12
subtype1 15 8 0 2 79 45 0 25 3 9 2 4
subtype2 18 11 0 5 38 29 1 14 0 8 2 2
subtype3 35 25 3 1 123 86 1 61 17 15 5 6

Figure S56.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S64.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 219 410 208
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0495 (logrank test), Q value = 1

Table S65.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 780 94 0.0 - 223.4 (18.6)
subtype1 212 16 0.1 - 194.3 (25.6)
subtype2 376 48 0.0 - 223.4 (16.0)
subtype3 192 30 0.0 - 211.5 (19.7)

Figure S57.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0442 (ANOVA), Q value = 1

Table S66.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 836 58.2 (13.1)
subtype1 218 57.2 (12.7)
subtype2 410 59.4 (13.4)
subtype3 208 57.0 (12.9)

Figure S58.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00631 (Fisher's exact test), Q value = 0.45

Table S67.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 828 9
subtype1 219 0
subtype2 401 9
subtype3 208 0

Figure S59.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.252 (Fisher's exact test), Q value = 1

Table S68.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 209 628
subtype1 60 159
subtype2 92 318
subtype3 57 151

Figure S60.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.294 (Chi-square test), Q value = 1

Table S69.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 621 9 67
subtype1 2 161 2 20
subtype2 0 307 5 35
subtype3 0 153 2 12

Figure S61.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.113 (Chi-square test), Q value = 1

Table S70.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B NX
ALL 220 13 87 1 84 109 26 2 20 42 45 12 27 1 10
subtype1 57 7 29 1 19 30 6 1 8 5 9 3 8 0 2
subtype2 99 3 36 0 45 64 13 0 10 26 26 7 13 0 5
subtype3 64 3 22 0 20 15 7 1 2 11 10 2 6 1 3

Figure S62.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.949 (ANOVA), Q value = 1

Table S71.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 696 2.2 (4.3)
subtype1 202 2.2 (4.9)
subtype2 318 2.1 (3.5)
subtype3 176 2.1 (4.7)

Figure S63.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00165 (Chi-square test), Q value = 0.12

Table S72.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 68 44 3 8 240 160 2 100 20 32 9 12
subtype1 30 15 0 5 57 43 0 21 0 10 2 2
subtype2 23 21 3 2 112 82 1 60 18 14 5 6
subtype3 15 8 0 1 71 35 1 19 2 8 2 4

Figure S64.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S73.  Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 208 397 247
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.106 (logrank test), Q value = 1

Table S74.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 793 95 0.0 - 223.4 (18.2)
subtype1 200 23 0.2 - 194.3 (20.3)
subtype2 369 37 0.0 - 223.4 (18.1)
subtype3 224 35 0.0 - 211.5 (18.6)

Figure S65.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.072 (ANOVA), Q value = 1

Table S75.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 851 58.4 (13.2)
subtype1 207 57.4 (12.7)
subtype2 397 59.5 (13.5)
subtype3 247 57.4 (13.2)

Figure S66.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

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

Table S76.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 843 9
subtype1 208 0
subtype2 391 6
subtype3 244 3

Figure S67.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.374 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 210 642
subtype1 44 164
subtype2 100 297
subtype3 66 181

Figure S68.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.766 (Chi-square test), Q value = 1

Table S78.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 634 8 72
subtype1 1 165 1 22
subtype2 1 300 5 29
subtype3 0 169 2 21

Figure S69.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.27 (Chi-square test), Q value = 1

Table S79.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B NX
ALL 230 13 87 1 85 111 26 2 21 42 46 13 27 2 10
subtype1 58 6 20 1 23 28 8 1 9 6 11 6 10 1 1
subtype2 99 5 45 0 41 62 11 1 9 23 18 5 10 0 6
subtype3 73 2 22 0 21 21 7 0 3 13 17 2 7 1 3

Figure S70.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0313 (ANOVA), Q value = 1

Table S80.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 713 2.2 (4.4)
subtype1 193 2.8 (5.9)
subtype2 312 1.8 (3.2)
subtype3 208 2.2 (4.2)

Figure S71.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0014 (Chi-square test), Q value = 0.11

Table S81.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 71 46 3 8 242 162 2 105 21 35 8 12
subtype1 30 12 0 6 53 42 1 23 4 16 1 1
subtype2 31 26 3 1 111 82 1 48 11 10 5 6
subtype3 10 8 0 1 78 38 0 34 6 9 2 5

Figure S72.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S82.  Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 366 260 226
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0899 (logrank test), Q value = 1

Table S83.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 793 95 0.0 - 223.4 (18.2)
subtype1 329 37 0.1 - 223.4 (17.0)
subtype2 251 20 0.0 - 162.0 (18.1)
subtype3 213 38 0.0 - 211.5 (20.4)

Figure S73.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0986 (ANOVA), Q value = 1

Table S84.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 851 58.4 (13.2)
subtype1 366 59.5 (14.0)
subtype2 259 57.9 (12.4)
subtype3 226 57.2 (12.8)

Figure S74.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.913 (Fisher's exact test), Q value = 1

Table S85.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 843 9
subtype1 362 4
subtype2 258 2
subtype3 223 3

Figure S75.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.903 (Fisher's exact test), Q value = 1

Table S86.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 210 642
subtype1 88 278
subtype2 64 196
subtype3 58 168

Figure S76.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.107 (Chi-square test), Q value = 1

Table S87.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients CM0 (I+) M0 M1 MX
ALL 2 634 8 72
subtype1 1 274 2 23
subtype2 1 202 2 34
subtype3 0 158 4 15

Figure S77.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

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

P value = 0.0717 (Chi-square test), Q value = 1

Table S88.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N0 (I+) N0 (I-) N0 (MOL+) N1 N1A N1B N1C N1MI N2 N2A N3 N3A N3B NX
ALL 230 13 87 1 85 111 26 2 21 42 46 13 27 2 10
subtype1 90 2 35 0 40 50 13 1 9 23 17 5 8 0 7
subtype2 69 8 28 1 27 44 5 0 11 10 17 6 11 1 1
subtype3 71 3 24 0 18 17 8 1 1 9 12 2 8 1 2

Figure S78.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.228 (ANOVA), Q value = 1

Table S89.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 713 2.2 (4.4)
subtype1 281 2.0 (3.2)
subtype2 239 2.6 (5.4)
subtype3 193 2.0 (4.5)

Figure S79.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00611 (Chi-square test), Q value = 0.45

Table S90.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE X
ALL 71 46 3 8 242 162 2 105 21 35 8 12
subtype1 26 20 3 0 100 68 0 52 12 10 2 7
subtype2 30 19 0 6 67 59 2 31 6 16 2 1
subtype3 15 7 0 2 75 35 0 22 3 9 4 4

Figure S80.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NEOPLASM.DISEASESTAGE'

Methods & Data
Input

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

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

  • Number of patients = 873

  • Number of clustering approaches = 10

  • Number of selected clinical features = 9

  • 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

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

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

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] 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)
[7] 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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