Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1028QXK
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 291 patients, 47 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 'GENDER'.

  • 6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH' and 'PATHOLOGY_T_STAGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'RACE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.

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

  • 6 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE', and 'GENDER'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE', and 'GENDER'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER', and 'KARNOFSKY_PERFORMANCE_SCORE'.

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, 47 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 0.0607
(0.152)
8.18e-06
(0.00015)
0.653
(0.761)
0.0027
(0.0124)
0.0707
(0.173)
0.00221
(0.0106)
0.162
(0.319)
0.343
(0.484)
0.000169
(0.00127)
4.03e-06
(0.00015)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.355
(0.487)
0.00931
(0.0339)
0.00195
(0.00975)
6.6e-05
(0.000566)
0.115
(0.241)
0.039
(0.104)
0.001
(0.00602)
0.0345
(0.094)
0.109
(0.234)
0.692
(0.798)
PATHOLOGIC STAGE Fisher's exact test 0.192
(0.35)
1e-05
(0.00015)
0.207
(0.356)
0.00049
(0.00327)
2e-05
(0.00024)
1e-05
(0.00015)
0.204
(0.356)
0.0929
(0.206)
0.00074
(0.00467)
6e-05
(0.000554)
PATHOLOGY T STAGE Fisher's exact test 0.169
(0.327)
1e-05
(0.00015)
0.00316
(0.0136)
0.00017
(0.00127)
1e-05
(0.00015)
1e-05
(0.00015)
0.0105
(0.036)
0.00642
(0.0257)
0.00145
(0.00791)
6e-05
(0.000554)
PATHOLOGY N STAGE Fisher's exact test 0.842
(0.892)
0.00041
(0.00289)
0.263
(0.41)
0.287
(0.431)
0.00171
(0.00892)
2e-05
(0.00024)
0.199
(0.356)
0.0827
(0.198)
0.00968
(0.0342)
0.0882
(0.2)
PATHOLOGY M STAGE Fisher's exact test 0.135
(0.279)
0.24
(0.39)
1
(1.00)
0.257
(0.406)
0.525
(0.642)
0.0442
(0.113)
0.726
(0.82)
0.453
(0.59)
0.0287
(0.0821)
0.0418
(0.109)
GENDER Fisher's exact test 0.0136
(0.044)
0.00113
(0.00646)
0.769
(0.839)
0.24
(0.39)
1e-05
(0.00015)
3e-05
(0.000327)
0.00318
(0.0136)
0.0112
(0.0372)
0.0216
(0.0665)
0.0305
(0.0852)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.183
(0.344)
0.0243
(0.0711)
0.476
(0.607)
0.807
(0.865)
0.00592
(0.0245)
0.00784
(0.0294)
0.0869
(0.2)
0.531
(0.644)
0.00689
(0.0267)
0.0172
(0.0543)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.375
(0.502)
0.848
(0.892)
0.962
(0.98)
0.9
(0.931)
0.376
(0.502)
0.314
(0.448)
0.233
(0.389)
0.877
(0.915)
0.355
(0.487)
0.207
(0.356)
YEAR OF TOBACCO SMOKING ONSET Kruskal-Wallis (anova) 0.155
(0.315)
0.295
(0.438)
0.776
(0.839)
0.101
(0.219)
0.253
(0.405)
0.518
(0.64)
0.963
(0.98)
0.757
(0.833)
0.731
(0.82)
0.753
(0.833)
RACE Fisher's exact test 0.635
(0.746)
0.084
(0.198)
0.485
(0.61)
0.0238
(0.0711)
0.606
(0.72)
0.3
(0.438)
0.303
(0.438)
0.278
(0.422)
0.488
(0.61)
0.407
(0.537)
ETHNICITY Fisher's exact test 0.718
(0.82)
0.464
(0.599)
0.173
(0.33)
0.271
(0.417)
0.161
(0.319)
0.192
(0.35)
0.603
(0.72)
0.357
(0.487)
1
(1.00)
0.225
(0.381)
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 106 73 49 60
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0607 (logrank test), Q value = 0.15

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

nPatients nDeath Duration Range (Median), Month
ALL 285 44 0.1 - 194.8 (25.3)
subtype1 105 15 0.1 - 125.3 (26.3)
subtype2 71 10 0.9 - 194.8 (29.4)
subtype3 49 5 0.5 - 117.4 (25.8)
subtype4 60 14 0.1 - 129.9 (18.6)

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 = 0.355 (Kruskal-Wallis (anova)), Q value = 0.49

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

nPatients Mean (Std.Dev)
ALL 283 61.5 (12.1)
subtype1 102 63.1 (12.2)
subtype2 72 60.7 (12.7)
subtype3 49 60.2 (12.4)
subtype4 60 60.5 (10.9)

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 'PATHOLOGIC_STAGE'

P value = 0.192 (Fisher's exact test), Q value = 0.35

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 171 21 52 15
subtype1 60 6 24 4
subtype2 51 4 7 4
subtype3 31 5 7 2
subtype4 29 6 14 5

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.169 (Fisher's exact test), Q value = 0.33

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

nPatients T1 T2 T3+T4
ALL 191 33 62
subtype1 69 9 26
subtype2 55 9 9
subtype3 33 7 9
subtype4 34 8 18

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 = 0.842 (Fisher's exact test), Q value = 0.89

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

nPatients N0 N1 N2
ALL 49 24 4
subtype1 20 12 1
subtype2 10 3 1
subtype3 4 3 0
subtype4 15 6 2

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 = 0.135 (Fisher's exact test), Q value = 0.28

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

nPatients 0 1
ALL 94 9
subtype1 43 2
subtype2 19 1
subtype3 18 2
subtype4 14 4

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 = 0.0136 (Fisher's exact test), Q value = 0.044

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

nPatients FEMALE MALE
ALL 76 212
subtype1 37 69
subtype2 10 63
subtype3 14 35
subtype4 15 45

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 = 0.183 (Kruskal-Wallis (anova)), Q value = 0.34

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

nPatients Mean (Std.Dev)
ALL 76 87.5 (22.0)
subtype1 18 85.6 (21.8)
subtype2 23 90.4 (21.2)
subtype3 16 93.1 (7.9)
subtype4 19 81.1 (29.8)

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 = 0.375 (Kruskal-Wallis (anova)), Q value = 0.5

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

nPatients Mean (Std.Dev)
ALL 74 32.3 (27.3)
subtype1 28 39.9 (36.7)
subtype2 23 28.7 (16.4)
subtype3 9 23.6 (15.7)
subtype4 14 28.8 (23.4)

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 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.155 (Kruskal-Wallis (anova)), Q value = 0.31

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

nPatients Mean (Std.Dev)
ALL 54 1972.5 (15.7)
subtype1 22 1977.0 (15.8)
subtype2 20 1966.5 (14.0)
subtype3 6 1973.0 (13.0)
subtype4 6 1975.3 (20.2)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.635 (Fisher's exact test), Q value = 0.75

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 61 204
subtype1 0 4 25 72
subtype2 1 0 16 51
subtype3 1 1 9 35
subtype4 0 1 11 46

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 = 0.718 (Fisher's exact test), Q value = 0.82

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 240
subtype1 5 84
subtype2 4 63
subtype3 2 40
subtype4 1 53

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 5 6
Number of samples 56 43 59 59 29 29
'METHLYATION CNMF' versus 'Time to Death'

P value = 8.18e-06 (logrank test), Q value = 0.00015

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

nPatients nDeath Duration Range (Median), Month
ALL 272 40 0.1 - 194.8 (25.0)
subtype1 55 9 0.4 - 125.3 (27.7)
subtype2 43 4 7.0 - 194.8 (29.4)
subtype3 59 5 0.1 - 103.6 (26.2)
subtype4 58 5 0.1 - 129.9 (23.6)
subtype5 29 7 0.2 - 91.7 (16.9)
subtype6 28 10 0.2 - 75.4 (19.5)

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 = 0.00931 (Kruskal-Wallis (anova)), Q value = 0.034

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

nPatients Mean (Std.Dev)
ALL 270 61.7 (12.1)
subtype1 53 62.7 (12.6)
subtype2 43 66.2 (11.7)
subtype3 59 57.9 (11.7)
subtype4 58 62.9 (10.2)
subtype5 29 63.1 (10.1)
subtype6 28 57.2 (15.4)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00015

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 168 18 51 14
subtype1 30 6 9 2
subtype2 25 1 9 2
subtype3 52 2 3 0
subtype4 40 7 6 2
subtype5 8 1 15 3
subtype6 13 1 9 5

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00015

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

nPatients T1 T2 T3+T4
ALL 187 26 60
subtype1 35 6 13
subtype2 31 2 10
subtype3 53 3 3
subtype4 44 10 5
subtype5 11 3 15
subtype6 13 2 14

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 = 0.00041 (Fisher's exact test), Q value = 0.0029

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

nPatients N0 N1 N2
ALL 48 23 4
subtype1 9 3 1
subtype2 10 2 0
subtype3 8 0 0
subtype4 11 1 0
subtype5 6 9 0
subtype6 4 8 3

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 = 0.24 (Fisher's exact test), Q value = 0.39

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

nPatients 0 1
ALL 85 8
subtype1 17 1
subtype2 17 1
subtype3 15 0
subtype4 17 1
subtype5 8 2
subtype6 11 3

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00113 (Fisher's exact test), Q value = 0.0065

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

nPatients FEMALE MALE
ALL 73 202
subtype1 11 45
subtype2 12 31
subtype3 12 47
subtype4 12 47
subtype5 8 21
subtype6 18 11

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0243 (Kruskal-Wallis (anova)), Q value = 0.071

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

nPatients Mean (Std.Dev)
ALL 76 88.8 (19.6)
subtype1 10 89.0 (8.8)
subtype2 6 93.3 (8.2)
subtype3 23 95.7 (7.9)
subtype4 20 94.0 (8.2)
subtype5 10 76.0 (32.7)
subtype6 7 65.7 (37.4)

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 = 0.848 (Kruskal-Wallis (anova)), Q value = 0.89

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

nPatients Mean (Std.Dev)
ALL 71 32.9 (27.7)
subtype1 18 39.5 (40.3)
subtype2 8 31.8 (19.3)
subtype3 16 26.1 (15.2)
subtype4 17 29.4 (21.5)
subtype5 10 37.1 (33.8)
subtype6 2 40.0 (14.1)

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

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.295 (Kruskal-Wallis (anova)), Q value = 0.44

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

nPatients Mean (Std.Dev)
ALL 53 1971.8 (15.7)
subtype1 12 1977.2 (18.2)
subtype2 6 1961.7 (9.8)
subtype3 13 1970.8 (17.9)
subtype4 13 1969.3 (14.3)
subtype5 7 1976.0 (10.7)
subtype6 2 1979.5 (20.5)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.084 (Fisher's exact test), Q value = 0.2

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 54 198
subtype1 0 2 10 39
subtype2 1 0 7 34
subtype3 0 1 14 41
subtype4 1 0 7 47
subtype5 0 0 7 22
subtype6 0 3 9 15

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.464 (Fisher's exact test), Q value = 0.6

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 227
subtype1 1 44
subtype2 4 34
subtype3 4 52
subtype4 2 50
subtype5 0 25
subtype6 1 22

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
Number of samples 75 83 57
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.653 (logrank test), Q value = 0.76

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

nPatients nDeath Duration Range (Median), Month
ALL 214 32 0.1 - 194.8 (25.2)
subtype1 75 13 0.1 - 103.6 (21.6)
subtype2 83 12 0.1 - 194.8 (25.8)
subtype3 56 7 1.1 - 125.3 (29.4)

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 = 0.00195 (Kruskal-Wallis (anova)), Q value = 0.0098

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

nPatients Mean (Std.Dev)
ALL 211 61.6 (12.1)
subtype1 74 59.2 (12.2)
subtype2 82 60.7 (11.9)
subtype3 55 66.2 (11.1)

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

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.207 (Fisher's exact test), Q value = 0.36

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 129 18 46 14
subtype1 42 8 18 4
subtype2 57 7 11 5
subtype3 30 3 17 5

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00316 (Fisher's exact test), Q value = 0.014

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

nPatients T1 T2 T3+T4
ALL 137 25 53
subtype1 46 8 21
subtype2 59 14 10
subtype3 32 3 22

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 = 0.263 (Fisher's exact test), Q value = 0.41

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

nPatients N0 N1 N2
ALL 47 21 3
subtype1 18 7 2
subtype2 20 6 0
subtype3 9 8 1

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

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

nPatients 0 1
ALL 73 8
subtype1 24 2
subtype2 26 3
subtype3 23 3

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 = 0.769 (Fisher's exact test), Q value = 0.84

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

nPatients FEMALE MALE
ALL 56 159
subtype1 18 57
subtype2 21 62
subtype3 17 40

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 = 0.476 (Kruskal-Wallis (anova)), Q value = 0.61

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

nPatients Mean (Std.Dev)
ALL 58 89.1 (20.8)
subtype1 21 87.6 (21.7)
subtype2 26 89.2 (22.6)
subtype3 11 91.8 (15.4)

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 = 0.962 (Kruskal-Wallis (anova)), Q value = 0.98

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

nPatients Mean (Std.Dev)
ALL 57 31.2 (27.2)
subtype1 20 27.5 (15.9)
subtype2 24 30.4 (20.3)
subtype3 13 38.6 (46.5)

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

'RPPA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.776 (Kruskal-Wallis (anova)), Q value = 0.84

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

nPatients Mean (Std.Dev)
ALL 45 1970.1 (15.2)
subtype1 15 1973.5 (16.7)
subtype2 19 1968.1 (13.8)
subtype3 11 1969.1 (16.0)

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.485 (Fisher's exact test), Q value = 0.61

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 6 45 150
subtype1 0 3 17 47
subtype2 0 3 16 63
subtype3 1 0 12 40

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.173 (Fisher's exact test), Q value = 0.33

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 177
subtype1 1 58
subtype2 2 76
subtype3 4 43

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 39 67 65 44
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0027 (logrank test), Q value = 0.012

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

nPatients nDeath Duration Range (Median), Month
ALL 214 32 0.1 - 194.8 (25.2)
subtype1 38 1 0.1 - 117.4 (24.7)
subtype2 67 9 0.1 - 194.8 (25.3)
subtype3 65 19 0.2 - 103.6 (20.1)
subtype4 44 3 1.1 - 125.3 (29.9)

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 = 6.6e-05 (Kruskal-Wallis (anova)), Q value = 0.00057

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

nPatients Mean (Std.Dev)
ALL 211 61.6 (12.1)
subtype1 37 62.9 (11.5)
subtype2 66 60.3 (11.3)
subtype3 64 57.6 (13.0)
subtype4 44 68.3 (9.4)

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

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00049 (Fisher's exact test), Q value = 0.0033

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 129 18 46 14
subtype1 30 4 4 0
subtype2 47 5 8 4
subtype3 25 5 24 9
subtype4 27 4 10 1

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00017 (Fisher's exact test), Q value = 0.0013

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

nPatients T1 T2 T3+T4
ALL 137 25 53
subtype1 31 4 4
subtype2 49 10 8
subtype3 28 7 30
subtype4 29 4 11

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 = 0.287 (Fisher's exact test), Q value = 0.43

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

nPatients N0 N1 N2
ALL 47 21 3
subtype1 7 1 0
subtype2 14 3 0
subtype3 18 14 3
subtype4 8 3 0

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 = 0.257 (Fisher's exact test), Q value = 0.41

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

nPatients 0 1
ALL 73 8
subtype1 10 0
subtype2 19 2
subtype3 28 6
subtype4 16 0

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 = 0.24 (Fisher's exact test), Q value = 0.39

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

nPatients FEMALE MALE
ALL 56 159
subtype1 8 31
subtype2 16 51
subtype3 23 42
subtype4 9 35

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 = 0.807 (Kruskal-Wallis (anova)), Q value = 0.86

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

nPatients Mean (Std.Dev)
ALL 58 89.1 (20.8)
subtype1 10 92.0 (7.9)
subtype2 23 88.7 (24.0)
subtype3 17 85.9 (25.5)
subtype4 8 93.8 (9.2)

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 = 0.9 (Kruskal-Wallis (anova)), Q value = 0.93

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

nPatients Mean (Std.Dev)
ALL 57 31.2 (27.2)
subtype1 9 24.8 (11.0)
subtype2 21 31.6 (21.5)
subtype3 16 29.6 (15.7)
subtype4 11 38.4 (51.5)

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

'RPPA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.101 (Kruskal-Wallis (anova)), Q value = 0.22

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

nPatients Mean (Std.Dev)
ALL 45 1970.1 (15.2)
subtype1 6 1986.2 (14.8)
subtype2 17 1967.5 (14.1)
subtype3 12 1969.0 (13.5)
subtype4 10 1966.4 (14.9)

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

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.0238 (Fisher's exact test), Q value = 0.071

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 6 45 150
subtype1 0 0 10 25
subtype2 0 0 11 54
subtype3 0 6 14 39
subtype4 1 0 10 32

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.271 (Fisher's exact test), Q value = 0.42

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 177
subtype1 0 33
subtype2 1 59
subtype3 4 49
subtype4 2 36

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 4 5
Number of samples 32 91 71 32 64
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0707 (logrank test), Q value = 0.17

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

nPatients nDeath Duration Range (Median), Month
ALL 287 44 0.1 - 194.8 (25.4)
subtype1 29 5 0.5 - 125.3 (21.7)
subtype2 91 11 0.1 - 117.4 (25.2)
subtype3 71 6 0.4 - 194.8 (27.1)
subtype4 32 6 0.9 - 129.9 (34.0)
subtype5 64 16 0.2 - 123.6 (21.9)

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 = 0.115 (Kruskal-Wallis (anova)), Q value = 0.24

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

nPatients Mean (Std.Dev)
ALL 285 61.4 (12.1)
subtype1 31 62.7 (11.8)
subtype2 90 60.8 (11.2)
subtype3 70 64.8 (11.5)
subtype4 32 58.7 (12.9)
subtype5 62 59.5 (13.1)

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

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00024

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 172 21 52 15
subtype1 20 5 3 0
subtype2 69 6 6 2
subtype3 41 5 12 4
subtype4 20 2 6 2
subtype5 22 3 25 7

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00015

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

nPatients T1 T2 T3+T4
ALL 193 33 62
subtype1 21 7 3
subtype2 75 11 5
subtype3 49 6 15
subtype4 22 2 8
subtype5 26 7 31

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 = 0.00171 (Fisher's exact test), Q value = 0.0089

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

nPatients N0 N1 N2
ALL 50 24 4
subtype1 7 1 0
subtype2 13 0 0
subtype3 16 6 0
subtype4 2 4 0
subtype5 12 13 4

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 = 0.525 (Fisher's exact test), Q value = 0.64

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

nPatients 0 1
ALL 95 9
subtype1 9 0
subtype2 25 1
subtype3 26 2
subtype4 13 2
subtype5 22 4

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 = 1e-05 (Fisher's exact test), Q value = 0.00015

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

nPatients FEMALE MALE
ALL 76 214
subtype1 4 28
subtype2 16 75
subtype3 13 58
subtype4 12 20
subtype5 31 33

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 = 0.00592 (Kruskal-Wallis (anova)), Q value = 0.024

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

nPatients Mean (Std.Dev)
ALL 77 87.7 (21.9)
subtype1 4 90.0 (8.2)
subtype2 32 95.9 (7.6)
subtype3 19 87.4 (20.5)
subtype4 9 86.7 (18.7)
subtype5 13 67.7 (37.5)

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 = 0.376 (Kruskal-Wallis (anova)), Q value = 0.5

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

nPatients Mean (Std.Dev)
ALL 75 32.1 (27.2)
subtype1 10 23.2 (13.1)
subtype2 25 26.2 (17.3)
subtype3 17 31.3 (19.8)
subtype4 8 37.1 (22.4)
subtype5 15 46.0 (47.3)

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

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.253 (Kruskal-Wallis (anova)), Q value = 0.41

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

nPatients Mean (Std.Dev)
ALL 55 1972.3 (15.6)
subtype1 5 1979.8 (14.1)
subtype2 21 1967.8 (15.1)
subtype3 12 1969.5 (15.2)
subtype4 4 1981.8 (12.0)
subtype5 13 1976.4 (16.9)

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.606 (Fisher's exact test), Q value = 0.72

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 61 206
subtype1 0 0 8 21
subtype2 1 0 19 65
subtype3 1 1 14 54
subtype4 0 1 6 23
subtype5 0 4 14 43

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.161 (Fisher's exact test), Q value = 0.32

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 242
subtype1 1 27
subtype2 3 81
subtype3 3 61
subtype4 4 22
subtype5 1 51

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 118 125 47
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00221 (logrank test), Q value = 0.011

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

nPatients nDeath Duration Range (Median), Month
ALL 287 44 0.1 - 194.8 (25.4)
subtype1 117 19 0.2 - 194.8 (25.6)
subtype2 124 12 0.1 - 129.9 (26.1)
subtype3 46 13 0.2 - 92.6 (21.8)

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 = 0.039 (Kruskal-Wallis (anova)), Q value = 0.1

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

nPatients Mean (Std.Dev)
ALL 285 61.4 (12.1)
subtype1 115 63.6 (11.7)
subtype2 124 59.7 (11.1)
subtype3 46 60.6 (14.6)

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00015

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 172 21 52 15
subtype1 56 10 34 5
subtype2 93 9 8 2
subtype3 23 2 10 8

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00015

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

nPatients T1 T2 T3+T4
ALL 193 33 62
subtype1 65 14 37
subtype2 103 15 7
subtype3 25 4 18

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 = 2e-05 (Fisher's exact test), Q value = 0.00024

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

nPatients N0 N1 N2
ALL 50 24 4
subtype1 25 14 1
subtype2 20 0 0
subtype3 5 10 3

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 = 0.0442 (Fisher's exact test), Q value = 0.11

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

nPatients 0 1
ALL 95 9
subtype1 41 3
subtype2 36 1
subtype3 18 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 = 3e-05 (Fisher's exact test), Q value = 0.00033

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

nPatients FEMALE MALE
ALL 76 214
subtype1 29 89
subtype2 22 103
subtype3 25 22

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 = 0.00784 (Kruskal-Wallis (anova)), Q value = 0.029

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

nPatients Mean (Std.Dev)
ALL 77 87.7 (21.9)
subtype1 28 82.5 (26.9)
subtype2 42 94.8 (7.7)
subtype3 7 65.7 (37.4)

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 = 0.314 (Kruskal-Wallis (anova)), Q value = 0.45

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

nPatients Mean (Std.Dev)
ALL 75 32.1 (27.2)
subtype1 37 34.6 (34.2)
subtype2 30 27.9 (19.0)
subtype3 8 36.0 (13.2)

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

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.518 (Kruskal-Wallis (anova)), Q value = 0.64

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

nPatients Mean (Std.Dev)
ALL 55 1972.3 (15.6)
subtype1 25 1974.0 (16.0)
subtype2 24 1969.5 (15.5)
subtype3 6 1976.2 (15.6)

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.3 (Fisher's exact test), Q value = 0.44

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 61 206
subtype1 1 2 24 88
subtype2 1 1 25 90
subtype3 0 3 12 28

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.192 (Fisher's exact test), Q value = 0.35

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 242
subtype1 4 100
subtype2 4 109
subtype3 4 33

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 4 5 6
Number of samples 29 39 66 43 81 33
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.162 (logrank test), Q value = 0.32

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

nPatients nDeath Duration Range (Median), Month
ALL 288 44 0.1 - 194.8 (25.3)
subtype1 29 2 3.8 - 125.3 (19.0)
subtype2 38 3 6.7 - 106.5 (28.6)
subtype3 66 10 0.1 - 110.7 (23.6)
subtype4 43 6 0.5 - 123.6 (28.2)
subtype5 80 19 0.1 - 194.8 (24.4)
subtype6 32 4 0.4 - 103.6 (26.0)

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 = 0.001 (Kruskal-Wallis (anova)), Q value = 0.006

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

nPatients Mean (Std.Dev)
ALL 286 61.5 (12.1)
subtype1 28 67.1 (9.0)
subtype2 38 66.4 (11.3)
subtype3 65 62.6 (10.5)
subtype4 42 59.4 (12.7)
subtype5 81 59.7 (13.3)
subtype6 32 56.2 (11.3)

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

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.204 (Fisher's exact test), Q value = 0.36

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 173 21 52 15
subtype1 18 4 4 0
subtype2 23 1 7 1
subtype3 47 6 7 3
subtype4 19 2 12 2
subtype5 45 4 18 8
subtype6 21 4 4 1

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

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0105 (Fisher's exact test), Q value = 0.036

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

nPatients T1 T2 T3+T4
ALL 194 33 62
subtype1 20 4 4
subtype2 28 2 9
subtype3 50 9 7
subtype4 23 8 12
subtype5 48 5 27
subtype6 25 5 3

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 = 0.199 (Fisher's exact test), Q value = 0.36

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

nPatients N0 N1 N2
ALL 50 24 4
subtype1 8 0 0
subtype2 6 3 0
subtype3 9 2 0
subtype4 9 4 0
subtype5 13 12 4
subtype6 5 3 0

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 = 0.726 (Fisher's exact test), Q value = 0.82

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

nPatients 0 1
ALL 95 9
subtype1 11 0
subtype2 12 0
subtype3 19 2
subtype4 11 2
subtype5 34 4
subtype6 8 1

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00318 (Fisher's exact test), Q value = 0.014

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

nPatients FEMALE MALE
ALL 77 214
subtype1 6 23
subtype2 9 30
subtype3 12 54
subtype4 19 24
subtype5 28 53
subtype6 3 30

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

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0869 (Kruskal-Wallis (anova)), Q value = 0.2

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

nPatients Mean (Std.Dev)
ALL 77 87.7 (21.9)
subtype1 6 88.3 (11.7)
subtype2 8 68.8 (34.0)
subtype3 18 95.6 (6.2)
subtype4 7 94.3 (7.9)
subtype5 26 87.3 (23.4)
subtype6 12 85.0 (28.1)

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 = 0.233 (Kruskal-Wallis (anova)), Q value = 0.39

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

nPatients Mean (Std.Dev)
ALL 76 31.7 (27.2)
subtype1 6 55.3 (65.6)
subtype2 9 28.7 (18.2)
subtype3 19 30.1 (17.9)
subtype4 13 34.0 (30.0)
subtype5 22 32.4 (20.1)
subtype6 7 13.5 (6.2)

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

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.963 (Kruskal-Wallis (anova)), Q value = 0.98

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

nPatients Mean (Std.Dev)
ALL 56 1972.2 (15.5)
subtype1 3 1970.0 (17.1)
subtype2 6 1967.3 (10.8)
subtype3 18 1972.2 (16.3)
subtype4 10 1975.7 (16.1)
subtype5 14 1972.1 (15.5)
subtype6 5 1972.4 (21.1)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.303 (Fisher's exact test), Q value = 0.44

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 61 207
subtype1 0 0 5 23
subtype2 1 0 6 31
subtype3 1 0 11 50
subtype4 0 2 11 28
subtype5 0 3 17 57
subtype6 0 1 11 18

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.603 (Fisher's exact test), Q value = 0.72

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 243
subtype1 0 24
subtype2 1 32
subtype3 2 58
subtype4 4 35
subtype5 4 65
subtype6 1 29

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
Number of samples 121 81 89
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.343 (logrank test), Q value = 0.48

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

nPatients nDeath Duration Range (Median), Month
ALL 288 44 0.1 - 194.8 (25.3)
subtype1 119 17 0.4 - 125.3 (27.1)
subtype2 80 9 0.1 - 110.7 (23.2)
subtype3 89 18 0.1 - 194.8 (24.9)

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 = 0.0345 (Kruskal-Wallis (anova)), Q value = 0.094

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

nPatients Mean (Std.Dev)
ALL 286 61.5 (12.1)
subtype1 119 63.1 (11.6)
subtype2 80 62.8 (11.0)
subtype3 87 58.2 (13.3)

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.0929 (Fisher's exact test), Q value = 0.21

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 173 21 52 15
subtype1 67 9 23 4
subtype2 54 8 9 2
subtype3 52 4 20 9

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00642 (Fisher's exact test), Q value = 0.026

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

nPatients T1 T2 T3+T4
ALL 194 33 62
subtype1 77 14 28
subtype2 62 12 7
subtype3 55 7 27

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 = 0.0827 (Fisher's exact test), Q value = 0.2

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

nPatients N0 N1 N2
ALL 50 24 4
subtype1 23 8 1
subtype2 12 2 0
subtype3 15 14 3

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 = 0.453 (Fisher's exact test), Q value = 0.59

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

nPatients 0 1
ALL 95 9
subtype1 38 3
subtype2 24 1
subtype3 33 5

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0112 (Fisher's exact test), Q value = 0.037

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

nPatients FEMALE MALE
ALL 77 214
subtype1 27 94
subtype2 16 65
subtype3 34 55

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

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.531 (Kruskal-Wallis (anova)), Q value = 0.64

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

nPatients Mean (Std.Dev)
ALL 77 87.7 (21.9)
subtype1 29 86.2 (27.4)
subtype2 24 92.1 (9.3)
subtype3 24 85.0 (23.6)

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 = 0.877 (Kruskal-Wallis (anova)), Q value = 0.92

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

nPatients Mean (Std.Dev)
ALL 76 31.7 (27.2)
subtype1 36 32.1 (23.7)
subtype2 16 27.5 (17.4)
subtype3 24 34.1 (36.6)

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

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.757 (Kruskal-Wallis (anova)), Q value = 0.83

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

nPatients Mean (Std.Dev)
ALL 56 1972.2 (15.5)
subtype1 23 1970.3 (14.0)
subtype2 15 1971.9 (15.3)
subtype3 18 1974.8 (17.9)

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.278 (Fisher's exact test), Q value = 0.42

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 61 207
subtype1 1 1 28 86
subtype2 1 1 12 61
subtype3 0 4 21 60

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.357 (Fisher's exact test), Q value = 0.49

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 243
subtype1 4 103
subtype2 2 71
subtype3 6 69

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
Number of samples 71 76 77
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.000169 (logrank test), Q value = 0.0013

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

nPatients nDeath Duration Range (Median), Month
ALL 222 37 0.1 - 194.8 (25.3)
subtype1 70 8 0.4 - 106.5 (26.0)
subtype2 75 5 0.1 - 123.6 (26.2)
subtype3 77 24 0.2 - 194.8 (22.3)

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 = 0.109 (Kruskal-Wallis (anova)), Q value = 0.23

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

nPatients Mean (Std.Dev)
ALL 220 60.9 (12.2)
subtype1 68 63.1 (10.7)
subtype2 76 59.1 (11.4)
subtype3 76 60.7 (14.0)

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 'PATHOLOGIC_STAGE'

P value = 0.00074 (Fisher's exact test), Q value = 0.0047

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 133 14 43 14
subtype1 38 6 16 2
subtype2 58 4 8 1
subtype3 37 4 19 11

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00145 (Fisher's exact test), Q value = 0.0079

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

nPatients T1 T2 T3+T4
ALL 147 21 54
subtype1 46 8 17
subtype2 61 7 8
subtype3 40 6 29

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 = 0.00968 (Fisher's exact test), Q value = 0.034

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

nPatients N0 N1 N2
ALL 41 23 4
subtype1 17 6 0
subtype2 12 2 0
subtype3 12 15 4

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 = 0.0287 (Fisher's exact test), Q value = 0.082

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

nPatients 0 1
ALL 76 8
subtype1 23 1
subtype2 23 0
subtype3 30 7

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 = 0.0216 (Fisher's exact test), Q value = 0.066

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

nPatients FEMALE MALE
ALL 62 162
subtype1 17 54
subtype2 15 61
subtype3 30 47

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 = 0.00689 (Kruskal-Wallis (anova)), Q value = 0.027

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

nPatients Mean (Std.Dev)
ALL 65 88.5 (21.4)
subtype1 17 85.9 (18.7)
subtype2 28 96.4 (6.2)
subtype3 20 79.5 (31.7)

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 = 0.355 (Kruskal-Wallis (anova)), Q value = 0.49

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

nPatients Mean (Std.Dev)
ALL 59 33.8 (29.0)
subtype1 17 41.1 (45.8)
subtype2 24 27.1 (16.8)
subtype3 18 35.8 (19.9)

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 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.731 (Kruskal-Wallis (anova)), Q value = 0.82

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

nPatients Mean (Std.Dev)
ALL 45 1970.8 (16.4)
subtype1 17 1971.5 (18.1)
subtype2 19 1968.7 (16.1)
subtype3 9 1974.2 (14.9)

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.488 (Fisher's exact test), Q value = 0.61

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 40 166
subtype1 1 1 12 54
subtype2 1 1 11 59
subtype3 0 4 17 53

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

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 184
subtype1 3 59
subtype2 4 64
subtype3 3 61

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
Number of samples 93 99 32
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 4.03e-06 (logrank test), Q value = 0.00015

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

nPatients nDeath Duration Range (Median), Month
ALL 222 37 0.1 - 194.8 (25.3)
subtype1 91 6 0.5 - 123.6 (26.2)
subtype2 99 31 0.2 - 194.8 (23.3)
subtype3 32 0 0.1 - 87.1 (25.3)

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 = 0.692 (Kruskal-Wallis (anova)), Q value = 0.8

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

nPatients Mean (Std.Dev)
ALL 220 60.9 (12.2)
subtype1 91 61.1 (10.8)
subtype2 97 61.2 (13.6)
subtype3 32 59.5 (11.8)

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 'PATHOLOGIC_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.00055

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 133 14 43 14
subtype1 58 9 13 2
subtype2 47 5 28 12
subtype3 28 0 2 0

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

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.00055

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

nPatients T1 T2 T3+T4
ALL 147 21 54
subtype1 67 12 14
subtype2 51 8 38
subtype3 29 1 2

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 = 0.0882 (Fisher's exact test), Q value = 0.2

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

nPatients N0 N1 N2
ALL 41 23 4
subtype1 16 4 0
subtype2 22 19 4
subtype3 3 0 0

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 = 0.0418 (Fisher's exact test), Q value = 0.11

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

nPatients 0 1
ALL 76 8
subtype1 26 0
subtype2 40 8
subtype3 10 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 = 0.0305 (Fisher's exact test), Q value = 0.085

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

nPatients FEMALE MALE
ALL 62 162
subtype1 18 75
subtype2 36 63
subtype3 8 24

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 = 0.0172 (Kruskal-Wallis (anova)), Q value = 0.054

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

nPatients Mean (Std.Dev)
ALL 65 88.5 (21.4)
subtype1 28 93.2 (8.2)
subtype2 26 79.2 (30.6)
subtype3 11 98.2 (4.0)

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 = 0.207 (Kruskal-Wallis (anova)), Q value = 0.36

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

nPatients Mean (Std.Dev)
ALL 59 33.8 (29.0)
subtype1 19 34.6 (26.4)
subtype2 23 39.8 (37.0)
subtype3 17 24.8 (15.7)

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 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.753 (Kruskal-Wallis (anova)), Q value = 0.83

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

nPatients Mean (Std.Dev)
ALL 45 1970.8 (16.4)
subtype1 15 1970.5 (16.4)
subtype2 16 1973.2 (16.9)
subtype3 14 1968.6 (16.7)

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.407 (Fisher's exact test), Q value = 0.54

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 6 40 166
subtype1 2 1 15 71
subtype2 0 5 20 70
subtype3 0 0 5 25

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 = 0.225 (Fisher's exact test), Q value = 0.38

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 184
subtype1 2 80
subtype2 5 76
subtype3 3 28

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/KIRP-TP/22542911/KIRP-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIRP-TP/22507257/KIRP-TP.merged_data.txt

  • Number of patients = 291

  • 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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[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)