Correlation between aggregated molecular cancer subtypes and selected clinical features
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 12 clinical features across 393 patients, 52 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY_N_STAGE' and 'ETHNICITY'.

  • 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.

  • CNMF clustering analysis on RPPA data identified 5 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'RACE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'KARNOFSKY_PERFORMANCE_SCORE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE', and 'NUMBER_OF_LYMPH_NODES'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER',  'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'KARNOFSKY_PERFORMANCE_SCORE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

  • 6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'KARNOFSKY_PERFORMANCE_SCORE',  'NUMBER_OF_LYMPH_NODES', and 'RACE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 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, 52 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
MIRseq
Mature
CNMF
subtypes
MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.546
(0.636)
0.0717
(0.154)
0.0799
(0.166)
0.379
(0.478)
0.00626
(0.0203)
0.00852
(0.0269)
0.0413
(0.0972)
0.338
(0.443)
0.00476
(0.0163)
0.0227
(0.058)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.0651
(0.142)
0.0139
(0.0378)
0.457
(0.554)
0.322
(0.435)
0.195
(0.308)
9.15e-05
(0.000578)
0.0458
(0.106)
0.00037
(0.00171)
5.11e-05
(0.000361)
0.000205
(0.00112)
NEOPLASM DISEASESTAGE Fisher's exact test 0.14
(0.24)
0.00076
(0.00314)
0.0963
(0.186)
0.107
(0.198)
1e-05
(9.23e-05)
1e-05
(9.23e-05)
1e-05
(9.23e-05)
0.00061
(0.00261)
1e-05
(9.23e-05)
1e-05
(9.23e-05)
PATHOLOGY T STAGE Fisher's exact test 0.733
(0.799)
0.012
(0.0334)
0.727
(0.799)
0.36
(0.465)
1e-05
(9.23e-05)
1e-05
(9.23e-05)
5e-05
(0.000361)
8e-05
(0.000533)
1e-05
(9.23e-05)
2e-05
(0.000171)
PATHOLOGY N STAGE Fisher's exact test 0.00914
(0.0279)
0.117
(0.21)
0.31
(0.428)
0.552
(0.636)
0.0146
(0.039)
1e-05
(9.23e-05)
0.00034
(0.00163)
0.00929
(0.0279)
1e-05
(9.23e-05)
0.00011
(0.000629)
PATHOLOGY M STAGE Fisher's exact test 0.643
(0.721)
0.894
(0.917)
0.249
(0.37)
0.162
(0.269)
0.0824
(0.166)
0.464
(0.557)
0.828
(0.871)
0.133
(0.231)
0.615
(0.703)
0.215
(0.33)
GENDER Fisher's exact test 0.825
(0.871)
0.204
(0.318)
0.517
(0.614)
0.17
(0.279)
0.098
(0.187)
0.0101
(0.0295)
0.0829
(0.166)
0.0337
(0.0808)
0.317
(0.432)
0.627
(0.71)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.253
(0.37)
0.0032
(0.012)
0.369
(0.471)
0.393
(0.482)
0.0334
(0.0808)
0.00474
(0.0163)
0.263
(0.379)
0.0108
(0.031)
0.003
(0.0116)
0.000583
(0.00259)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.744
(0.804)
0.534
(0.628)
0.187
(0.299)
0.156
(0.264)
0.333
(0.443)
0.835
(0.871)
0.663
(0.736)
0.787
(0.843)
0.851
(0.88)
0.386
(0.481)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.11
(0.201)
0.0827
(0.166)
0.0885
(0.174)
0.232
(0.352)
0.252
(0.37)
5.19e-06
(9.23e-05)
0.00115
(0.00458)
0.0605
(0.135)
0.000269
(0.00135)
0.0159
(0.0415)
RACE Fisher's exact test 0.265
(0.379)
0.00027
(0.00135)
0.0607
(0.135)
0.00442
(0.0161)
1e-05
(9.23e-05)
1e-05
(9.23e-05)
0.174
(0.282)
0.00599
(0.02)
0.0001
(6e-04)
5e-05
(0.000361)
ETHNICITY Fisher's exact test 0.0285
(0.0713)
0.102
(0.19)
0.389
(0.481)
0.124
(0.219)
0.34
(0.443)
0.269
(0.38)
0.28
(0.39)
0.987
(0.987)
0.931
(0.947)
0.963
(0.971)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 165 132 55 37
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.546 (logrank test), Q value = 0.64

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

nPatients nDeath Duration Range (Median), Month
ALL 383 153 0.1 - 166.0 (15.7)
subtype1 161 63 0.1 - 140.8 (15.0)
subtype2 131 49 0.4 - 166.0 (17.2)
subtype3 55 26 1.6 - 123.8 (15.1)
subtype4 36 15 2.2 - 112.8 (18.3)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0651 (Kruskal-Wallis (anova)), Q value = 0.14

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

nPatients Mean (Std.Dev)
ALL 388 67.8 (10.6)
subtype1 165 66.2 (11.0)
subtype2 131 69.2 (9.7)
subtype3 55 69.6 (9.5)
subtype4 37 67.2 (12.6)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.14 (Fisher's exact test), Q value = 0.24

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 4 124 127 129
subtype1 2 59 61 41
subtype2 2 35 37 55
subtype3 0 18 15 22
subtype4 0 12 14 11

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.733 (Fisher's exact test), Q value = 0.8

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

nPatients T0+T1+T2 T3 T4
ALL 121 184 53
subtype1 55 72 26
subtype2 39 63 19
subtype3 16 29 5
subtype4 11 20 3

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00914 (Fisher's exact test), Q value = 0.028

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

nPatients N0 N1 N2 N3
ALL 224 42 75 8
subtype1 114 15 20 2
subtype2 65 17 34 4
subtype3 25 5 16 1
subtype4 20 5 5 1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.643 (Fisher's exact test), Q value = 0.72

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

nPatients 0 1
ALL 185 10
subtype1 91 5
subtype2 64 3
subtype3 18 2
subtype4 12 0

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.825 (Fisher's exact test), Q value = 0.87

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

nPatients FEMALE MALE
ALL 101 288
subtype1 40 125
subtype2 34 98
subtype3 16 39
subtype4 11 26

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.253 (Kruskal-Wallis (anova)), Q value = 0.37

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

nPatients Mean (Std.Dev)
ALL 125 82.7 (14.0)
subtype1 49 81.4 (13.2)
subtype2 43 84.7 (14.9)
subtype3 17 84.1 (10.0)
subtype4 16 80.0 (17.9)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.744 (Kruskal-Wallis (anova)), Q value = 0.8

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

nPatients Mean (Std.Dev)
ALL 212 38.7 (54.2)
subtype1 86 35.9 (27.2)
subtype2 72 44.9 (86.2)
subtype3 28 37.9 (21.3)
subtype4 26 32.0 (22.8)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.11 (Kruskal-Wallis (anova)), Q value = 0.2

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

nPatients Mean (Std.Dev)
ALL 284 2.0 (6.9)
subtype1 112 1.2 (3.4)
subtype2 103 2.8 (10.1)
subtype3 43 2.4 (5.2)
subtype4 26 1.8 (4.6)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.265 (Fisher's exact test), Q value = 0.38

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 42 21 311
subtype1 26 9 128
subtype2 11 8 104
subtype3 4 3 47
subtype4 1 1 32

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.0285 (Fisher's exact test), Q value = 0.071

Table S13.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 352
subtype1 1 157
subtype2 6 112
subtype3 0 51
subtype4 2 32

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 136 107 66 84
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0717 (logrank test), Q value = 0.15

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

nPatients nDeath Duration Range (Median), Month
ALL 387 155 0.1 - 166.0 (15.6)
subtype1 135 61 0.4 - 166.0 (14.4)
subtype2 107 34 0.4 - 130.9 (17.6)
subtype3 65 23 0.1 - 103.5 (15.0)
subtype4 80 37 0.5 - 86.3 (14.4)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0139 (Kruskal-Wallis (anova)), Q value = 0.038

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

nPatients Mean (Std.Dev)
ALL 392 67.8 (10.6)
subtype1 136 69.4 (9.8)
subtype2 106 69.0 (9.8)
subtype3 66 65.2 (10.9)
subtype4 84 65.5 (11.9)

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

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00076 (Fisher's exact test), Q value = 0.0031

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 4 126 128 130
subtype1 0 29 63 43
subtype2 3 37 27 39
subtype3 1 25 15 24
subtype4 0 35 23 24

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.012 (Fisher's exact test), Q value = 0.033

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1+T2 T3 T4
ALL 122 185 54
subtype1 27 80 20
subtype2 38 44 11
subtype3 24 26 11
subtype4 33 35 12

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.117 (Fisher's exact test), Q value = 0.21

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

nPatients N0 N1 N2 N3
ALL 227 42 76 8
subtype1 82 14 24 4
subtype2 55 15 21 2
subtype3 37 2 20 1
subtype4 53 11 11 1

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

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.894 (Fisher's exact test), Q value = 0.92

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 189 10
subtype1 53 4
subtype2 54 2
subtype3 45 2
subtype4 37 2

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.204 (Fisher's exact test), Q value = 0.32

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 102 291
subtype1 40 96
subtype2 31 76
subtype3 11 55
subtype4 20 64

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0032 (Kruskal-Wallis (anova)), Q value = 0.012

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 126 82.8 (14.0)
subtype1 36 87.5 (10.5)
subtype2 37 85.1 (11.2)
subtype3 21 78.6 (12.0)
subtype4 32 77.5 (18.8)

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.534 (Kruskal-Wallis (anova)), Q value = 0.63

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 214 38.8 (54.1)
subtype1 78 37.4 (28.2)
subtype2 57 33.8 (25.9)
subtype3 32 31.7 (21.6)
subtype4 47 52.0 (104.0)

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0827 (Kruskal-Wallis (anova)), Q value = 0.17

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 284 2.0 (6.9)
subtype1 110 1.3 (2.6)
subtype2 71 1.6 (3.4)
subtype3 41 3.5 (7.1)
subtype4 62 2.8 (12.6)

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00027 (Fisher's exact test), Q value = 0.0013

Table S25.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 43 22 311
subtype1 5 8 118
subtype2 10 6 86
subtype3 18 2 44
subtype4 10 6 63

Figure S23.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.102 (Fisher's exact test), Q value = 0.19

Table S26.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 354
subtype1 2 124
subtype2 2 94
subtype3 0 63
subtype4 5 73

Figure S24.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S27.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 37 29 36 5 20
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0799 (logrank test), Q value = 0.17

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 122 59 0.1 - 140.8 (17.0)
subtype1 33 9 0.1 - 130.9 (17.9)
subtype2 28 18 1.8 - 140.8 (15.5)
subtype3 36 18 0.4 - 123.8 (14.9)
subtype4 5 3 3.1 - 17.7 (15.9)
subtype5 20 11 7.2 - 61.9 (20.6)

Figure S25.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.457 (Kruskal-Wallis (anova)), Q value = 0.55

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 126 67.2 (10.5)
subtype1 36 65.3 (10.7)
subtype2 29 69.4 (9.5)
subtype3 36 68.7 (10.6)
subtype4 5 64.4 (11.4)
subtype5 20 65.0 (11.0)

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0963 (Fisher's exact test), Q value = 0.19

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 1 35 43 44
subtype1 1 11 9 13
subtype2 0 5 8 16
subtype3 0 8 17 10
subtype4 0 1 3 1
subtype5 0 10 6 4

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.727 (Fisher's exact test), Q value = 0.8

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1+T2 T3 T4
ALL 27 63 20
subtype1 10 15 5
subtype2 6 15 8
subtype3 8 21 5
subtype4 0 3 1
subtype5 3 9 1

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.31 (Fisher's exact test), Q value = 0.43

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 71 13 26 6
subtype1 20 1 10 2
subtype2 13 4 10 2
subtype3 23 5 4 2
subtype4 4 1 0 0
subtype5 11 2 2 0

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.249 (Fisher's exact test), Q value = 0.37

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 73 5
subtype1 25 2
subtype2 10 2
subtype3 25 0
subtype4 4 0
subtype5 9 1

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.517 (Fisher's exact test), Q value = 0.61

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 33 94
subtype1 7 30
subtype2 10 19
subtype3 8 28
subtype4 2 3
subtype5 6 14

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.369 (Kruskal-Wallis (anova)), Q value = 0.47

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 35 79.1 (15.8)
subtype1 12 75.0 (17.8)
subtype2 2 80.0 (14.1)
subtype3 10 77.0 (20.0)
subtype4 1 90.0 (NA)
subtype5 10 85.0 (7.1)

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.187 (Kruskal-Wallis (anova)), Q value = 0.3

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 79 36.8 (23.8)
subtype1 24 34.7 (28.9)
subtype2 18 44.4 (22.5)
subtype3 22 36.8 (20.3)
subtype4 2 35.5 (6.4)
subtype5 13 30.1 (22.2)

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0885 (Kruskal-Wallis (anova)), Q value = 0.17

Table S37.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 91 1.8 (3.8)
subtype1 23 1.8 (3.0)
subtype2 26 3.2 (5.4)
subtype3 27 0.9 (2.4)
subtype4 4 0.2 (0.5)
subtype5 11 1.4 (3.6)

Figure S34.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0607 (Fisher's exact test), Q value = 0.13

Table S38.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 9 103
subtype1 7 1 27
subtype2 0 2 26
subtype3 1 3 29
subtype4 0 0 5
subtype5 0 3 16

Figure S35.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.389 (Fisher's exact test), Q value = 0.48

Table S39.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 114
subtype1 0 33
subtype2 2 26
subtype3 1 31
subtype4 0 5
subtype5 0 19

Figure S36.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S40.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 43 38 27 19
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.379 (logrank test), Q value = 0.48

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 122 59 0.1 - 140.8 (17.0)
subtype1 39 13 0.1 - 112.4 (17.6)
subtype2 37 21 1.8 - 140.8 (16.7)
subtype3 27 15 0.4 - 130.9 (16.1)
subtype4 19 10 7.2 - 61.9 (19.4)

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.322 (Kruskal-Wallis (anova)), Q value = 0.43

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 126 67.2 (10.5)
subtype1 42 65.4 (10.8)
subtype2 38 68.7 (10.4)
subtype3 27 69.0 (10.9)
subtype4 19 65.3 (9.0)

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.107 (Fisher's exact test), Q value = 0.2

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 1 35 43 44
subtype1 1 14 12 13
subtype2 0 7 12 19
subtype3 0 5 14 7
subtype4 0 9 5 5

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.36 (Fisher's exact test), Q value = 0.46

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

nPatients T0+T1+T2 T3 T4
ALL 27 63 20
subtype1 14 16 7
subtype2 7 22 8
subtype3 4 18 4
subtype4 2 7 1

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.552 (Fisher's exact test), Q value = 0.64

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 71 13 26 6
subtype1 24 4 9 1
subtype2 19 6 11 2
subtype3 19 3 4 1
subtype4 9 0 2 2

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.162 (Fisher's exact test), Q value = 0.27

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 73 5
subtype1 30 1
subtype2 18 2
subtype3 16 0
subtype4 9 2

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.17 (Fisher's exact test), Q value = 0.28

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 33 94
subtype1 7 36
subtype2 14 24
subtype3 6 21
subtype4 6 13

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.393 (Kruskal-Wallis (anova)), Q value = 0.48

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 35 79.1 (15.8)
subtype1 12 75.8 (17.8)
subtype2 5 74.0 (20.7)
subtype3 8 78.8 (17.3)
subtype4 10 86.0 (7.0)

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.156 (Kruskal-Wallis (anova)), Q value = 0.26

Table S49.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 79 36.8 (23.8)
subtype1 27 34.8 (26.9)
subtype2 24 43.2 (24.2)
subtype3 16 35.9 (18.1)
subtype4 12 29.5 (21.8)

Figure S45.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.232 (Kruskal-Wallis (anova)), Q value = 0.35

Table S50.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 91 1.8 (3.8)
subtype1 28 1.6 (2.8)
subtype2 33 2.6 (4.9)
subtype3 21 0.5 (0.9)
subtype4 9 2.9 (5.2)

Figure S46.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.00442 (Fisher's exact test), Q value = 0.016

Table S51.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 9 103
subtype1 7 1 32
subtype2 0 1 35
subtype3 1 4 20
subtype4 0 3 16

Figure S47.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.124 (Fisher's exact test), Q value = 0.22

Table S52.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 114
subtype1 0 38
subtype2 3 33
subtype3 0 24
subtype4 0 19

Figure S48.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S53.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 110 172 107
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00626 (logrank test), Q value = 0.02

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 383 153 0.1 - 166.0 (15.6)
subtype1 109 55 0.7 - 166.0 (18.0)
subtype2 167 45 0.1 - 112.4 (15.0)
subtype3 107 53 0.4 - 125.5 (15.1)

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.195 (Kruskal-Wallis (anova)), Q value = 0.31

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 388 67.7 (10.6)
subtype1 110 68.8 (9.1)
subtype2 171 66.6 (11.3)
subtype3 107 68.4 (11.0)

Figure S50.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 4 125 127 128
subtype1 0 17 46 47
subtype2 4 76 42 47
subtype3 0 32 39 34

Figure S51.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1+T2 T3 T4
ALL 121 183 53
subtype1 17 73 16
subtype2 74 57 22
subtype3 30 53 15

Figure S52.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0146 (Fisher's exact test), Q value = 0.039

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 225 41 75 8
subtype1 59 13 30 2
subtype2 105 10 32 3
subtype3 61 18 13 3

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0824 (Fisher's exact test), Q value = 0.17

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 189 10
subtype1 38 5
subtype2 105 3
subtype3 46 2

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.098 (Fisher's exact test), Q value = 0.19

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

nPatients FEMALE MALE
ALL 101 288
subtype1 27 83
subtype2 38 134
subtype3 36 71

Figure S55.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0334 (Kruskal-Wallis (anova)), Q value = 0.081

Table S61.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 124 82.7 (14.1)
subtype1 30 87.3 (9.1)
subtype2 61 80.2 (15.0)
subtype3 33 83.0 (15.3)

Figure S56.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.333 (Kruskal-Wallis (anova)), Q value = 0.44

Table S62.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 212 38.9 (54.3)
subtype1 68 36.4 (28.1)
subtype2 89 34.4 (27.3)
subtype3 55 49.4 (95.8)

Figure S57.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.252 (Kruskal-Wallis (anova)), Q value = 0.37

Table S63.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 280 2.0 (6.9)
subtype1 94 2.2 (4.5)
subtype2 106 2.5 (10.1)
subtype3 80 1.1 (3.0)

Figure S58.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S64.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 43 22 308
subtype1 3 5 101
subtype2 37 9 120
subtype3 3 8 87

Figure S59.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.34 (Fisher's exact test), Q value = 0.44

Table S65.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 351
subtype1 2 104
subtype2 2 156
subtype3 4 91

Figure S60.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S66.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 108 109 172
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00852 (logrank test), Q value = 0.027

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 383 153 0.1 - 166.0 (15.6)
subtype1 107 47 0.5 - 130.9 (18.0)
subtype2 104 23 0.1 - 91.6 (13.8)
subtype3 172 83 0.4 - 166.0 (14.7)

Figure S61.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 9.15e-05 (Kruskal-Wallis (anova)), Q value = 0.00058

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 388 67.7 (10.6)
subtype1 108 70.9 (9.5)
subtype2 108 64.5 (10.6)
subtype3 172 67.8 (10.8)

Figure S62.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S69.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 4 125 127 128
subtype1 0 15 33 59
subtype2 4 63 24 16
subtype3 0 47 70 53

Figure S63.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1+T2 T3 T4
ALL 121 183 53
subtype1 20 62 21
subtype2 59 26 10
subtype3 42 95 22

Figure S64.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 225 41 75 8
subtype1 47 14 40 3
subtype2 77 4 10 1
subtype3 101 23 25 4

Figure S65.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.464 (Fisher's exact test), Q value = 0.56

Table S72.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 189 10
subtype1 47 3
subtype2 75 2
subtype3 67 5

Figure S66.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0101 (Fisher's exact test), Q value = 0.03

Table S73.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 101 288
subtype1 27 81
subtype2 18 91
subtype3 56 116

Figure S67.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00474 (Kruskal-Wallis (anova)), Q value = 0.016

Table S74.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 124 82.7 (14.1)
subtype1 28 85.0 (12.6)
subtype2 41 77.8 (15.6)
subtype3 55 85.1 (12.9)

Figure S68.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.835 (Kruskal-Wallis (anova)), Q value = 0.87

Table S75.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 212 38.9 (54.3)
subtype1 53 38.6 (32.3)
subtype2 62 34.4 (25.4)
subtype3 97 42.0 (74.0)

Figure S69.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 5.19e-06 (Kruskal-Wallis (anova)), Q value = 9.2e-05

Table S76.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 280 2.0 (6.9)
subtype1 90 2.4 (3.6)
subtype2 57 2.9 (13.5)
subtype3 133 1.4 (3.9)

Figure S70.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S77.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 43 22 308
subtype1 5 8 93
subtype2 32 3 70
subtype3 6 11 145

Figure S71.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.269 (Fisher's exact test), Q value = 0.38

Table S78.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 351
subtype1 1 97
subtype2 1 102
subtype3 6 152

Figure S72.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S79.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 116 93 182
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0413 (logrank test), Q value = 0.097

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 385 154 0.1 - 166.0 (15.7)
subtype1 116 58 0.4 - 166.0 (14.9)
subtype2 90 35 1.2 - 130.9 (15.2)
subtype3 179 61 0.1 - 125.5 (16.7)

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0458 (Kruskal-Wallis (anova)), Q value = 0.11

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 390 67.7 (10.6)
subtype1 116 69.1 (9.8)
subtype2 92 68.6 (11.2)
subtype3 182 66.4 (10.7)

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S82.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 4 126 127 129
subtype1 0 22 43 48
subtype2 1 23 29 39
subtype3 3 81 55 42

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00036

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1+T2 T3 T4
ALL 122 184 53
subtype1 21 76 13
subtype2 28 45 15
subtype3 73 63 25

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00034 (Fisher's exact test), Q value = 0.0016

Table S84.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 226 41 76 8
subtype1 60 13 35 0
subtype2 46 13 21 4
subtype3 120 15 20 4

Figure S77.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.828 (Fisher's exact test), Q value = 0.87

Table S85.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 188 10
subtype1 43 2
subtype2 40 3
subtype3 105 5

Figure S78.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0829 (Fisher's exact test), Q value = 0.17

Table S86.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 101 290
subtype1 38 78
subtype2 18 75
subtype3 45 137

Figure S79.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.263 (Kruskal-Wallis (anova)), Q value = 0.38

Table S87.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 126 82.8 (14.0)
subtype1 29 85.2 (12.7)
subtype2 25 83.6 (15.0)
subtype3 72 81.5 (14.2)

Figure S80.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.663 (Kruskal-Wallis (anova)), Q value = 0.74

Table S88.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 212 39.0 (54.3)
subtype1 64 49.0 (91.4)
subtype2 48 35.2 (28.6)
subtype3 100 34.4 (21.8)

Figure S81.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00115 (Kruskal-Wallis (anova)), Q value = 0.0046

Table S89.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 283 2.0 (6.9)
subtype1 92 2.0 (4.2)
subtype2 73 3.6 (11.9)
subtype3 118 1.1 (3.4)

Figure S82.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.174 (Fisher's exact test), Q value = 0.28

Table S90.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 43 22 309
subtype1 7 7 101
subtype2 10 4 75
subtype3 26 11 133

Figure S83.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.28 (Fisher's exact test), Q value = 0.39

Table S91.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 352
subtype1 1 111
subtype2 4 81
subtype3 4 160

Figure S84.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S92.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 85 95 94 66 51
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.338 (logrank test), Q value = 0.44

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 385 154 0.1 - 166.0 (15.7)
subtype1 85 41 0.4 - 166.0 (16.1)
subtype2 93 40 1.2 - 130.9 (15.7)
subtype3 90 29 0.1 - 103.5 (16.3)
subtype4 66 23 0.4 - 110.6 (14.5)
subtype5 51 21 0.5 - 140.8 (16.2)

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00037 (Kruskal-Wallis (anova)), Q value = 0.0017

Table S94.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 390 67.7 (10.6)
subtype1 85 67.6 (11.2)
subtype2 94 70.1 (10.1)
subtype3 94 64.1 (10.8)
subtype4 66 67.4 (9.9)
subtype5 51 70.6 (9.6)

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00061 (Fisher's exact test), Q value = 0.0026

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 4 126 127 129
subtype1 0 21 35 27
subtype2 1 23 30 40
subtype3 2 47 21 23
subtype4 1 25 24 16
subtype5 0 10 17 23

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 8e-05 (Fisher's exact test), Q value = 0.00053

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1+T2 T3 T4
ALL 122 184 53
subtype1 18 50 9
subtype2 26 47 16
subtype3 49 25 12
subtype4 18 30 9
subtype5 11 32 7

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.00929 (Fisher's exact test), Q value = 0.028

Table S97.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 226 41 76 8
subtype1 48 14 11 2
subtype2 49 13 23 4
subtype3 58 6 14 1
subtype4 44 5 8 1
subtype5 27 3 20 0

Figure S89.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.133 (Fisher's exact test), Q value = 0.23

Table S98.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 188 10
subtype1 38 0
subtype2 40 1
subtype3 60 3
subtype4 33 4
subtype5 17 2

Figure S90.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0337 (Fisher's exact test), Q value = 0.081

Table S99.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 101 290
subtype1 28 57
subtype2 18 77
subtype3 20 74
subtype4 15 51
subtype5 20 31

Figure S91.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0108 (Kruskal-Wallis (anova)), Q value = 0.031

Table S100.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 126 82.8 (14.0)
subtype1 25 83.6 (15.2)
subtype2 25 86.4 (9.5)
subtype3 37 76.5 (17.2)
subtype4 26 85.0 (9.9)
subtype5 13 87.7 (10.9)

Figure S92.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.787 (Kruskal-Wallis (anova)), Q value = 0.84

Table S101.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 212 39.0 (54.3)
subtype1 43 53.3 (108.7)
subtype2 52 33.6 (24.2)
subtype3 53 34.4 (22.1)
subtype4 35 33.5 (24.6)
subtype5 29 42.6 (36.1)

Figure S93.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0605 (Kruskal-Wallis (anova)), Q value = 0.13

Table S102.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 283 2.0 (6.9)
subtype1 63 1.1 (2.3)
subtype2 78 3.4 (11.5)
subtype3 52 1.9 (6.1)
subtype4 45 0.8 (1.7)
subtype5 45 2.2 (3.7)

Figure S94.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.00599 (Fisher's exact test), Q value = 0.02

Table S103.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 43 22 309
subtype1 4 4 72
subtype2 8 5 80
subtype3 22 5 63
subtype4 7 5 48
subtype5 2 3 46

Figure S95.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.987 (Fisher's exact test), Q value = 0.99

Table S104.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 352
subtype1 2 77
subtype2 2 86
subtype3 2 84
subtype4 2 56
subtype5 1 49

Figure S96.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S105.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 96 40 129 115
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00476 (logrank test), Q value = 0.016

Table S106.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 374 152 0.4 - 166.0 (16.0)
subtype1 96 40 0.4 - 125.5 (15.5)
subtype2 39 24 1.5 - 130.9 (12.8)
subtype3 124 34 0.4 - 110.6 (17.0)
subtype4 115 54 0.5 - 166.0 (16.3)

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 5.11e-05 (Kruskal-Wallis (anova)), Q value = 0.00036

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 379 67.8 (10.5)
subtype1 96 67.9 (11.1)
subtype2 39 71.9 (8.5)
subtype3 129 64.6 (10.5)
subtype4 115 70.1 (9.7)

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S108.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 4 119 123 129
subtype1 1 29 34 31
subtype2 1 4 11 23
subtype3 2 63 36 27
subtype4 0 23 42 48

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S109.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1+T2 T3 T4
ALL 118 180 53
subtype1 26 51 10
subtype2 9 21 10
subtype3 59 37 18
subtype4 24 71 15

Figure S100.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S110.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 219 41 76 8
subtype1 57 15 11 3
subtype2 14 6 14 3
subtype3 87 8 14 2
subtype4 61 12 37 0

Figure S101.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.615 (Fisher's exact test), Q value = 0.7

Table S111.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 181 10
subtype1 36 2
subtype2 14 1
subtype3 83 3
subtype4 48 4

Figure S102.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.317 (Fisher's exact test), Q value = 0.43

Table S112.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 101 279
subtype1 28 68
subtype2 9 31
subtype3 28 101
subtype4 36 79

Figure S103.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.003 (Kruskal-Wallis (anova)), Q value = 0.012

Table S113.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 121 82.8 (14.2)
subtype1 28 84.6 (13.7)
subtype2 11 85.5 (12.1)
subtype3 47 77.4 (16.5)
subtype4 35 87.7 (9.1)

Figure S104.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.851 (Kruskal-Wallis (anova)), Q value = 0.88

Table S114.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 207 39.4 (54.7)
subtype1 53 37.2 (23.2)
subtype2 26 37.7 (26.0)
subtype3 70 34.6 (24.3)
subtype4 58 48.1 (96.0)

Figure S105.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.000269 (Kruskal-Wallis (anova)), Q value = 0.0013

Table S115.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 279 2.1 (7.0)
subtype1 72 0.9 (1.9)
subtype2 35 3.6 (5.7)
subtype3 78 2.3 (11.5)
subtype4 94 2.2 (4.2)

Figure S106.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 1e-04 (Fisher's exact test), Q value = 6e-04

Table S116.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 40 21 302
subtype1 3 7 80
subtype2 1 2 36
subtype3 28 6 85
subtype4 8 6 101

Figure S107.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.931 (Fisher's exact test), Q value = 0.95

Table S117.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 341
subtype1 3 84
subtype2 0 37
subtype3 3 111
subtype4 3 109

Figure S108.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S118.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 71 43 92 63 30 81
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0227 (logrank test), Q value = 0.058

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 374 152 0.4 - 166.0 (16.0)
subtype1 71 30 0.5 - 140.8 (15.8)
subtype2 42 22 1.5 - 130.9 (13.0)
subtype3 89 23 0.4 - 110.6 (17.2)
subtype4 63 36 1.8 - 166.0 (17.7)
subtype5 28 5 0.7 - 82.5 (15.5)
subtype6 81 36 0.4 - 125.5 (15.9)

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000205 (Kruskal-Wallis (anova)), Q value = 0.0011

Table S120.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 379 67.8 (10.5)
subtype1 71 69.0 (9.8)
subtype2 42 71.1 (10.0)
subtype3 92 64.7 (10.1)
subtype4 63 70.7 (8.9)
subtype5 30 63.2 (10.6)
subtype6 81 68.2 (11.7)

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S121.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 4 119 123 129
subtype1 0 18 25 27
subtype2 1 8 12 21
subtype3 2 40 23 27
subtype4 0 12 25 25
subtype5 1 20 6 2
subtype6 0 21 32 27

Figure S111.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00017

Table S122.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1+T2 T3 T4
ALL 118 180 53
subtype1 17 42 8
subtype2 11 22 9
subtype3 36 28 11
subtype4 13 38 10
subtype5 21 4 4
subtype6 20 46 11

Figure S112.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00011 (Fisher's exact test), Q value = 0.00063

Table S123.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 219 41 76 8
subtype1 39 4 23 0
subtype2 18 6 13 3
subtype3 53 6 14 2
subtype4 36 11 15 0
subtype5 25 0 1 0
subtype6 48 14 10 3

Figure S113.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.215 (Fisher's exact test), Q value = 0.33

Table S124.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 181 10
subtype1 30 2
subtype2 19 0
subtype3 51 6
subtype4 26 2
subtype5 25 0
subtype6 30 0

Figure S114.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.627 (Fisher's exact test), Q value = 0.71

Table S125.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 101 279
subtype1 23 48
subtype2 10 33
subtype3 22 70
subtype4 18 45
subtype5 5 25
subtype6 23 58

Figure S115.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000583 (Kruskal-Wallis (anova)), Q value = 0.0026

Table S126.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 121 82.8 (14.2)
subtype1 19 86.3 (10.1)
subtype2 11 84.5 (12.1)
subtype3 36 81.7 (15.6)
subtype4 19 88.9 (7.4)
subtype5 11 66.4 (16.3)
subtype6 25 83.6 (14.1)

Figure S116.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.386 (Kruskal-Wallis (anova)), Q value = 0.48

Table S127.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 207 39.4 (54.7)
subtype1 37 38.3 (34.3)
subtype2 24 33.4 (22.5)
subtype3 50 34.4 (29.6)
subtype4 33 60.2 (122.0)
subtype5 17 39.9 (19.8)
subtype6 46 34.0 (21.3)

Figure S117.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0159 (Kruskal-Wallis (anova)), Q value = 0.042

Table S128.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 279 2.1 (7.0)
subtype1 54 1.9 (3.5)
subtype2 34 3.7 (5.9)
subtype3 58 2.6 (12.8)
subtype4 55 1.3 (1.9)
subtype5 13 2.5 (8.9)
subtype6 65 1.4 (4.1)

Figure S118.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00036

Table S129.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 40 21 302
subtype1 5 3 63
subtype2 3 2 36
subtype3 13 6 66
subtype4 2 4 53
subtype5 13 2 14
subtype6 4 4 70

Figure S119.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.963 (Fisher's exact test), Q value = 0.97

Table S130.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 341
subtype1 1 68
subtype2 1 37
subtype3 3 77
subtype4 2 57
subtype5 0 28
subtype6 2 74

Figure S120.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/BLCA-TP/15111049/BLCA-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BLCA-TP/15076720/BLCA-TP.merged_data.txt

  • Number of patients = 393

  • Number of clustering approaches = 10

  • 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

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.

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] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)