Correlation between molecular cancer subtypes and selected clinical features
Head and Neck Squamous Cell Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
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 (2013): Head and Neck Squamous Cell 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/C1V9860F
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 12 clinical features across 327 patients, 6 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 6 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'NUMBERPACKYEARSSMOKED', and 'TOBACCOSMOKINGHISTORYINDICATOR'.

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

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'GENDER'.

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

  • 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 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.

  • 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 8 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 6 significant findings detected.

Clinical
Features
Statistical
Tests
Copy
Number
Ratio
CNMF
subtypes
METHLYATION
CNMF
RPPA
CNMF
subtypes
RPPA
cHierClus
subtypes
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
Time to Death logrank test 0.000432
(0.0389)
0.0554
(1.00)
0.684
(1.00)
0.137
(1.00)
0.448
(1.00)
0.205
(1.00)
0.11
(1.00)
0.144
(1.00)
AGE ANOVA 0.106
(1.00)
0.000106
(0.00964)
0.409
(1.00)
0.274
(1.00)
0.925
(1.00)
0.383
(1.00)
0.429
(1.00)
0.299
(1.00)
GENDER Fisher's exact test 0.405
(1.00)
0.0445
(1.00)
0.00242
(0.208)
0.973
(1.00)
0.188
(1.00)
0.00985
(0.827)
0.128
(1.00)
0.53
(1.00)
PATHOLOGY T Chi-square test 0.0177
(1.00)
0.0887
(1.00)
0.113
(1.00)
0.213
(1.00)
0.225
(1.00)
0.298
(1.00)
0.226
(1.00)
0.122
(1.00)
PATHOLOGY N Chi-square test 0.00805
(0.684)
0.439
(1.00)
0.146
(1.00)
0.156
(1.00)
0.194
(1.00)
0.0772
(1.00)
0.174
(1.00)
0.623
(1.00)
PATHOLOGICSPREAD(M) Fisher's exact test 0.628
(1.00)
0.494
(1.00)
1
(1.00)
TUMOR STAGE Chi-square test 0.0286
(1.00)
0.272
(1.00)
0.098
(1.00)
0.192
(1.00)
0.0364
(1.00)
0.39
(1.00)
0.443
(1.00)
0.0662
(1.00)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.32
(1.00)
0.715
(1.00)
0.154
(1.00)
0.611
(1.00)
0.413
(1.00)
0.546
(1.00)
0.743
(1.00)
0.734
(1.00)
NUMBERPACKYEARSSMOKED ANOVA 0.000531
(0.0462)
0.0491
(1.00)
0.0668
(1.00)
0.654
(1.00)
0.0646
(1.00)
0.0544
(1.00)
0.657
(1.00)
0.484
(1.00)
TOBACCOSMOKINGHISTORYINDICATOR Chi-square test 0.000469
(0.0418)
0.00052
(0.0457)
0.704
(1.00)
0.186
(1.00)
0.42
(1.00)
0.129
(1.00)
0.272
(1.00)
0.637
(1.00)
YEAROFTOBACCOSMOKINGONSET ANOVA 0.114
(1.00)
0.0163
(1.00)
0.534
(1.00)
0.709
(1.00)
0.133
(1.00)
0.125
(1.00)
0.409
(1.00)
0.139
(1.00)
NUMBER OF LYMPH NODES ANOVA 0.155
(1.00)
0.0311
(1.00)
0.105
(1.00)
0.624
(1.00)
0.175
(1.00)
0.366
(1.00)
0.504
(1.00)
0.748
(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 4 5 6
Number of samples 74 39 46 81 71 10
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.000432 (logrank test), Q value = 0.039

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

nPatients nDeath Duration Range (Median), Month
ALL 317 121 0.1 - 210.9 (14.0)
subtype1 73 35 0.2 - 142.5 (13.6)
subtype2 38 12 0.1 - 111.1 (14.7)
subtype3 46 22 1.0 - 129.2 (14.1)
subtype4 81 18 0.8 - 210.9 (21.5)
subtype5 69 28 0.1 - 126.1 (12.5)
subtype6 10 6 1.5 - 54.9 (10.2)

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 = 0.106 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 320 61.1 (12.1)
subtype1 73 61.8 (11.6)
subtype2 39 57.7 (12.6)
subtype3 46 63.3 (9.5)
subtype4 81 62.1 (12.7)
subtype5 71 60.8 (12.6)
subtype6 10 53.8 (11.9)

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 = 0.405 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 90 231
subtype1 19 55
subtype2 12 27
subtype3 9 37
subtype4 25 56
subtype5 24 47
subtype6 1 9

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 'PATHOLOGY.T'

P value = 0.0177 (Chi-square test), Q value = 1

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

nPatients T1 T2 T3 T4
ALL 28 81 65 104
subtype1 7 13 14 27
subtype2 2 15 7 10
subtype3 0 9 17 13
subtype4 11 25 11 19
subtype5 8 17 14 29
subtype6 0 2 2 6

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

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

P value = 0.00805 (Chi-square test), Q value = 0.68

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

nPatients N0 N1 N2 N3
ALL 107 36 99 5
subtype1 15 5 34 2
subtype2 13 5 12 1
subtype3 14 4 15 1
subtype4 35 8 13 0
subtype5 28 13 20 0
subtype6 2 1 5 1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.628 (Chi-square test), Q value = 1

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients M0 MX
ALL 59 6
subtype1 9 2
subtype2 5 1
subtype3 6 1
subtype4 21 1
subtype5 18 1
subtype6 0 0

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0286 (Chi-square test), Q value = 1

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

nPatients I II III IV
ALL 20 51 45 157
subtype1 6 5 5 44
subtype2 1 7 7 19
subtype3 0 7 11 21
subtype4 6 15 14 27
subtype5 7 15 8 38
subtype6 0 2 0 8

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

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

P value = 0.32 (Chi-square test), Q value = 1

Table S9.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 76 245
subtype1 22 52
subtype2 10 29
subtype3 11 35
subtype4 20 61
subtype5 13 58
subtype6 0 10

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.000531 (ANOVA), Q value = 0.046

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

nPatients Mean (Std.Dev)
ALL 175 50.8 (41.1)
subtype1 49 60.3 (49.6)
subtype2 20 39.8 (18.1)
subtype3 27 73.8 (31.4)
subtype4 43 34.6 (23.3)
subtype5 29 49.9 (53.8)
subtype6 7 29.6 (27.5)

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.000469 (Chi-square test), Q value = 0.042

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 84 57 103 66
subtype1 19 13 35 5
subtype2 8 6 11 13
subtype3 19 4 18 3
subtype4 19 19 19 23
subtype5 17 12 16 21
subtype6 2 3 4 1

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

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.114 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 182 1965.1 (12.4)
subtype1 51 1964.8 (11.9)
subtype2 18 1968.6 (6.7)
subtype3 26 1960.7 (10.2)
subtype4 46 1963.8 (14.8)
subtype5 32 1966.8 (12.6)
subtype6 9 1972.3 (12.2)

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

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

P value = 0.155 (ANOVA), Q value = 1

Table S13.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 242 2.6 (5.2)
subtype1 56 4.1 (6.8)
subtype2 33 2.2 (5.3)
subtype3 38 2.7 (6.8)
subtype4 49 1.3 (2.4)
subtype5 56 2.4 (3.9)
subtype6 10 2.8 (3.6)

Figure S12.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #2: 'METHLYATION CNMF'

Table S14.  Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6 7 8
Number of samples 43 47 40 44 58 53 5 19
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0554 (logrank test), Q value = 1

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 306 121 0.1 - 210.9 (14.6)
subtype1 43 15 0.1 - 142.5 (13.2)
subtype2 47 19 0.2 - 126.1 (13.6)
subtype3 39 20 0.1 - 156.5 (12.7)
subtype4 44 8 0.8 - 95.0 (22.3)
subtype5 58 30 0.5 - 135.3 (14.4)
subtype6 51 19 1.5 - 210.9 (14.3)
subtype7 5 1 5.0 - 28.6 (10.7)
subtype8 19 9 1.0 - 84.5 (18.0)

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000106 (ANOVA), Q value = 0.0096

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 309 61.1 (12.1)
subtype1 43 58.0 (10.6)
subtype2 47 55.5 (13.8)
subtype3 40 62.4 (11.1)
subtype4 44 58.1 (10.9)
subtype5 58 64.4 (10.1)
subtype6 53 63.9 (13.2)
subtype7 5 63.8 (11.7)
subtype8 19 67.4 (10.3)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0445 (Chi-square test), Q value = 1

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 85 224
subtype1 7 36
subtype2 17 30
subtype3 15 25
subtype4 5 39
subtype5 15 43
subtype6 18 35
subtype7 1 4
subtype8 7 12

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0887 (Chi-square test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 24 78 63 102
subtype1 2 10 11 17
subtype2 7 14 12 10
subtype3 1 10 5 19
subtype4 4 9 5 8
subtype5 1 13 13 23
subtype6 8 18 8 17
subtype7 1 0 2 1
subtype8 0 4 7 7

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.439 (Chi-square test), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 101 32 100 5
subtype1 21 4 11 1
subtype2 19 4 14 0
subtype3 15 4 9 2
subtype4 9 5 9 0
subtype5 12 5 24 2
subtype6 18 8 22 0
subtype7 1 0 3 0
subtype8 6 2 8 0

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

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

P value = 0.715 (Chi-square test), Q value = 1

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 78 231
subtype1 7 36
subtype2 10 37
subtype3 11 29
subtype4 10 34
subtype5 16 42
subtype6 17 36
subtype7 2 3
subtype8 5 14

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0491 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 168 49.5 (37.2)
subtype1 29 56.3 (30.5)
subtype2 15 33.5 (20.9)
subtype3 18 55.2 (64.5)
subtype4 22 41.8 (44.6)
subtype5 36 64.1 (33.8)
subtype6 31 42.9 (25.7)
subtype7 4 47.2 (23.6)
subtype8 13 34.2 (21.4)

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

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.00052 (Chi-square test), Q value = 1

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 82 55 99 62
subtype1 12 7 21 2
subtype2 7 10 9 18
subtype3 9 6 11 11
subtype4 14 6 11 13
subtype5 23 8 21 3
subtype6 8 14 17 13
subtype7 3 0 2 0
subtype8 6 4 7 2

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

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0163 (ANOVA), Q value = 0.046

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

nPatients Mean (Std.Dev)
ALL 175 1964.6 (12.1)
subtype1 25 1965.2 (9.2)
subtype2 18 1971.8 (12.9)
subtype3 19 1967.8 (11.9)
subtype4 26 1965.3 (11.8)
subtype5 36 1962.4 (11.1)
subtype6 34 1964.2 (12.4)
subtype7 5 1963.4 (11.3)
subtype8 12 1954.9 (14.8)

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

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

P value = 0.0311 (ANOVA), Q value = 1

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 233 2.7 (5.3)
subtype1 36 1.5 (2.8)
subtype2 36 1.6 (2.2)
subtype3 32 2.8 (5.0)
subtype4 18 2.1 (2.5)
subtype5 45 5.3 (9.9)
subtype6 44 2.2 (2.7)
subtype7 4 2.2 (2.6)
subtype8 18 2.4 (3.0)

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

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S25.  Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 73 52 29 58
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.684 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 212 107 0.1 - 210.9 (13.2)
subtype1 73 40 1.5 - 156.5 (13.2)
subtype2 52 23 1.4 - 135.3 (11.8)
subtype3 29 14 2.5 - 114.9 (21.8)
subtype4 58 30 0.1 - 210.9 (13.2)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.409 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 212 62.1 (12.2)
subtype1 73 60.2 (13.0)
subtype2 52 63.4 (10.0)
subtype3 29 63.4 (11.4)
subtype4 58 62.8 (13.4)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.00242 (Fisher's exact test), Q value = 0.21

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 62 150
subtype1 21 52
subtype2 6 46
subtype3 13 16
subtype4 22 36

Figure S25.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

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

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

nPatients T1 T2 T3 T4
ALL 13 59 53 79
subtype1 3 26 13 28
subtype2 4 5 17 22
subtype3 3 10 7 8
subtype4 3 18 16 21

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.146 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 73 20 79 4
subtype1 28 8 22 1
subtype2 14 2 27 1
subtype3 15 3 6 1
subtype4 16 7 24 1

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

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

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

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 56 156
subtype1 20 53
subtype2 17 35
subtype3 3 26
subtype4 16 42

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0668 (ANOVA), Q value = 1

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 105 48.9 (38.6)
subtype1 33 42.7 (27.9)
subtype2 29 59.8 (35.1)
subtype3 15 61.9 (68.9)
subtype4 28 38.0 (26.2)

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.704 (Chi-square test), Q value = 1

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 60 38 64 40
subtype1 21 10 22 16
subtype2 15 8 18 8
subtype3 9 4 8 7
subtype4 15 16 16 9

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.534 (ANOVA), Q value = 1

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 115 1964.2 (12.4)
subtype1 38 1964.6 (15.5)
subtype2 30 1963.1 (9.9)
subtype3 15 1968.3 (8.3)
subtype4 32 1962.9 (12.2)

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.105 (ANOVA), Q value = 1

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 172 2.8 (5.1)
subtype1 55 2.4 (3.7)
subtype2 46 4.0 (6.0)
subtype3 23 1.0 (1.5)
subtype4 48 3.2 (6.3)

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S36.  Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 116 78 18
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.137 (logrank test), Q value = 1

Table S37.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 212 107 0.1 - 210.9 (13.2)
subtype1 116 56 0.1 - 210.9 (14.2)
subtype2 78 41 1.5 - 156.5 (13.1)
subtype3 18 10 3.3 - 52.3 (11.0)

Figure S33.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.274 (ANOVA), Q value = 1

Table S38.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 212 62.1 (12.2)
subtype1 116 63.3 (11.2)
subtype2 78 60.8 (13.3)
subtype3 18 59.9 (13.6)

Figure S34.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.973 (Fisher's exact test), Q value = 1

Table S39.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 62 150
subtype1 35 81
subtype2 22 56
subtype3 5 13

Figure S35.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.213 (Chi-square test), Q value = 1

Table S40.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 13 59 53 79
subtype1 8 27 36 40
subtype2 4 28 12 31
subtype3 1 4 5 8

Figure S36.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.156 (Chi-square test), Q value = 1

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 73 20 79 4
subtype1 41 8 44 3
subtype2 30 9 24 1
subtype3 2 3 11 0

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

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

P value = 0.611 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 56 156
subtype1 28 88
subtype2 22 56
subtype3 6 12

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.654 (ANOVA), Q value = 1

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 105 48.9 (38.6)
subtype1 61 50.1 (45.2)
subtype2 35 44.7 (27.3)
subtype3 9 56.9 (27.0)

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.186 (Chi-square test), Q value = 1

Table S44.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 60 38 64 40
subtype1 35 27 32 18
subtype2 21 10 23 18
subtype3 4 1 9 4

Figure S40.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.709 (ANOVA), Q value = 1

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 115 1964.2 (12.4)
subtype1 67 1963.5 (11.1)
subtype2 39 1964.9 (15.4)
subtype3 9 1966.8 (7.5)

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.624 (ANOVA), Q value = 1

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 172 2.8 (5.1)
subtype1 96 3.2 (6.1)
subtype2 59 2.3 (3.6)
subtype3 17 2.7 (3.2)

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S47.  Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 118 93 91
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.448 (logrank test), Q value = 1

Table S48.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 299 120 0.1 - 210.9 (14.8)
subtype1 118 42 0.1 - 135.3 (14.2)
subtype2 91 40 0.2 - 142.5 (15.7)
subtype3 90 38 0.1 - 210.9 (14.0)

Figure S43.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.925 (ANOVA), Q value = 1

Table S49.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 302 61.0 (12.2)
subtype1 118 61.3 (11.1)
subtype2 93 60.6 (12.8)
subtype3 91 61.2 (12.9)

Figure S44.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.188 (Fisher's exact test), Q value = 1

Table S50.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 81 221
subtype1 25 93
subtype2 27 66
subtype3 29 62

Figure S45.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.225 (Chi-square test), Q value = 1

Table S51.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 23 78 60 101
subtype1 6 25 24 36
subtype2 13 25 19 29
subtype3 4 28 17 36

Figure S46.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.194 (Chi-square test), Q value = 1

Table S52.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 100 32 97 5
subtype1 34 10 32 3
subtype2 28 10 42 1
subtype3 38 12 23 1

Figure S47.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

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

P value = 0.413 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 75 227
subtype1 25 93
subtype2 27 66
subtype3 23 68

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0646 (ANOVA), Q value = 1

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 165 50.0 (37.4)
subtype1 75 56.7 (36.5)
subtype2 41 48.5 (50.4)
subtype3 49 40.9 (21.1)

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.42 (Chi-square test), Q value = 1

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 82 53 98 58
subtype1 37 17 42 17
subtype2 24 16 29 21
subtype3 21 20 27 20

Figure S50.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.133 (ANOVA), Q value = 1

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 173 1964.5 (12.1)
subtype1 73 1963.0 (11.0)
subtype2 49 1967.4 (11.5)
subtype3 51 1964.1 (13.8)

Figure S51.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

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

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 230 2.7 (5.3)
subtype1 72 2.9 (6.5)
subtype2 83 3.3 (5.6)
subtype3 75 1.7 (3.2)

Figure S52.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S58.  Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 97 85 120
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.205 (logrank test), Q value = 1

Table S59.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 299 120 0.1 - 210.9 (14.8)
subtype1 95 43 1.5 - 142.5 (15.1)
subtype2 84 36 0.1 - 210.9 (14.0)
subtype3 120 41 0.1 - 135.3 (15.1)

Figure S53.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.383 (ANOVA), Q value = 1

Table S60.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 302 61.0 (12.2)
subtype1 97 60.0 (13.3)
subtype2 85 62.5 (12.9)
subtype3 120 60.9 (10.5)

Figure S54.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00985 (Fisher's exact test), Q value = 0.83

Table S61.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 81 221
subtype1 31 66
subtype2 29 56
subtype3 21 99

Figure S55.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.298 (Chi-square test), Q value = 1

Table S62.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 23 78 60 101
subtype1 13 27 22 30
subtype2 4 26 15 32
subtype3 6 25 23 39

Figure S56.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0772 (Chi-square test), Q value = 1

Table S63.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 100 32 97 5
subtype1 27 12 45 1
subtype2 37 10 20 1
subtype3 36 10 32 3

Figure S57.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

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

P value = 0.546 (Fisher's exact test), Q value = 1

Table S64.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 75 227
subtype1 28 69
subtype2 20 65
subtype3 27 93

Figure S58.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0544 (ANOVA), Q value = 1

Table S65.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 165 50.0 (37.4)
subtype1 44 46.6 (49.1)
subtype2 42 40.5 (20.5)
subtype3 79 56.9 (35.8)

Figure S59.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.129 (Chi-square test), Q value = 1

Table S66.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 82 53 98 58
subtype1 24 19 29 23
subtype2 21 17 22 20
subtype3 37 17 47 15

Figure S60.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TOBACCOSMOKINGHISTORYINDICATOR'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.125 (ANOVA), Q value = 1

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 173 1964.5 (12.1)
subtype1 51 1967.4 (11.8)
subtype2 44 1963.9 (14.2)
subtype3 78 1963.0 (10.8)

Figure S61.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.366 (ANOVA), Q value = 1

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 230 2.7 (5.3)
subtype1 85 3.1 (5.4)
subtype2 68 1.9 (3.7)
subtype3 77 2.8 (6.3)

Figure S62.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S69.  Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 98 128 99
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.11 (logrank test), Q value = 1

Table S70.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 321 123 0.1 - 210.9 (14.3)
subtype1 96 30 0.1 - 210.9 (12.7)
subtype2 128 51 0.1 - 142.5 (14.2)
subtype3 97 42 1.0 - 100.5 (16.9)

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.429 (ANOVA), Q value = 1

Table S71.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 324 61.2 (12.1)
subtype1 97 61.3 (11.6)
subtype2 128 62.0 (11.4)
subtype3 99 59.9 (13.2)

Figure S64.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.128 (Fisher's exact test), Q value = 1

Table S72.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 92 233
subtype1 22 76
subtype2 35 93
subtype3 35 64

Figure S65.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.226 (Chi-square test), Q value = 1

Table S73.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 28 83 65 106
subtype1 8 29 14 32
subtype2 7 26 28 45
subtype3 13 28 23 29

Figure S66.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.174 (Chi-square test), Q value = 1

Table S74.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 106 36 103 5
subtype1 36 10 24 2
subtype2 43 11 39 3
subtype3 27 15 40 0

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.494 (Fisher's exact test), Q value = 1

Table S75.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients M0 MX
ALL 59 6
subtype1 16 3
subtype2 21 1
subtype3 22 2

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.443 (Chi-square test), Q value = 1

Table S76.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'TUMOR.STAGE'

nPatients I II III IV
ALL 20 51 44 162
subtype1 7 19 10 44
subtype2 4 17 19 65
subtype3 9 15 15 53

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'TUMOR.STAGE'

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

P value = 0.743 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 77 248
subtype1 23 75
subtype2 28 100
subtype3 26 73

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.657 (ANOVA), Q value = 1

Table S78.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 179 50.6 (40.9)
subtype1 62 49.6 (40.4)
subtype2 73 53.7 (36.3)
subtype3 44 46.7 (48.6)

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

'MIRSEQ CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.272 (Chi-square test), Q value = 1

Table S79.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 85 58 105 66
subtype1 27 17 34 18
subtype2 36 19 45 21
subtype3 22 22 26 27

Figure S72.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.409 (ANOVA), Q value = 1

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 186 1965.0 (12.3)
subtype1 60 1964.7 (12.9)
subtype2 72 1963.8 (11.7)
subtype3 54 1966.8 (12.4)

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.504 (ANOVA), Q value = 1

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 245 2.6 (5.2)
subtype1 71 2.0 (3.3)
subtype2 91 2.7 (6.0)
subtype3 83 3.0 (5.5)

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S82.  Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 123 11 191
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.144 (logrank test), Q value = 1

Table S83.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 321 123 0.1 - 210.9 (14.3)
subtype1 121 49 0.2 - 129.2 (15.7)
subtype2 11 7 3.5 - 90.1 (12.0)
subtype3 189 67 0.1 - 210.9 (14.0)

Figure S75.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.299 (ANOVA), Q value = 1

Table S84.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 324 61.2 (12.1)
subtype1 123 59.9 (12.7)
subtype2 11 59.9 (14.9)
subtype3 190 62.0 (11.4)

Figure S76.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.53 (Fisher's exact test), Q value = 1

Table S85.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 92 233
subtype1 39 84
subtype2 2 9
subtype3 51 140

Figure S77.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.122 (Chi-square test), Q value = 1

Table S86.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 28 83 65 106
subtype1 18 33 26 37
subtype2 0 5 3 3
subtype3 10 45 36 66

Figure S78.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.623 (Chi-square test), Q value = 1

Table S87.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 106 36 103 5
subtype1 38 16 48 1
subtype2 4 2 4 0
subtype3 64 18 51 4

Figure S79.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

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

Table S88.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients M0 MX
ALL 59 6
subtype1 27 3
subtype2 0 0
subtype3 32 3

Figure S80.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.0662 (Chi-square test), Q value = 1

Table S89.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'TUMOR.STAGE'

nPatients I II III IV
ALL 20 51 44 162
subtype1 14 18 16 65
subtype2 0 4 3 4
subtype3 6 29 25 93

Figure S81.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'TUMOR.STAGE'

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

P value = 0.734 (Fisher's exact test), Q value = 1

Table S90.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 77 248
subtype1 32 91
subtype2 2 9
subtype3 43 148

Figure S82.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.484 (ANOVA), Q value = 1

Table S91.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 179 50.6 (40.9)
subtype1 57 45.5 (43.9)
subtype2 6 59.9 (25.9)
subtype3 116 52.6 (40.0)

Figure S83.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.637 (Chi-square test), Q value = 1

Table S92.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT SMOKER LIFELONG NON-SMOKER
ALL 85 58 105 66
subtype1 30 25 35 30
subtype2 2 2 4 3
subtype3 53 31 66 33

Figure S84.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'TOBACCOSMOKINGHISTORYINDICATOR'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.139 (ANOVA), Q value = 1

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

nPatients Mean (Std.Dev)
ALL 186 1965.0 (12.3)
subtype1 65 1967.4 (12.1)
subtype2 4 1966.5 (12.0)
subtype3 117 1963.6 (12.3)

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.748 (ANOVA), Q value = 1

Table S94.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 245 2.6 (5.2)
subtype1 106 2.7 (4.9)
subtype2 7 3.9 (5.1)
subtype3 132 2.4 (5.4)

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Methods & Data
Input
  • Cluster data file = HNSC-TP.mergedcluster.txt

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

  • Number of patients = 327

  • Number of clustering approaches = 8

  • Number of selected clinical features = 12

  • 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)