Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C17S7MT1
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C17S7MT1
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 12 different clustering approaches and 12 clinical features across 533 patients, 66 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE', and 'RACE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER', and 'RACE'.

  • CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE', and 'PATHOLOGY_M_STAGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE', and 'PATHOLOGY_M_STAGE'.

  • 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',  'PATHOLOGY_M_STAGE',  'GENDER', and 'RACE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER', 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',  'PATHOLOGY_M_STAGE', and 'GENDER'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER', and 'ETHNICITY'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'GENDER', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE',  'GENDER', and 'RACE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 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, 66 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		NEOPLASM
DISEASESTAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER KARNOFSKY
PERFORMANCE
SCORE 		NUMBER
PACK
YEARS
SMOKED 		YEAR
OF
TOBACCO
SMOKING
ONSET 		RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Kruskal-Wallis (anova) Kruskal-Wallis (anova) Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.0843
(0.164) 		0.873
(1.00) 		0.00582
(0.0178) 		0.00784
(0.0226) 		0.0703
(0.141) 		0.121
(0.206) 		0.635
(0.832) 		0.0075
(0.022) 		0.317
(0.448)
mRNA cHierClus subtypes 0.0254
(0.0589) 		0.168
(0.271) 		0.00051
(0.00193) 		0.00423
(0.0132) 		0.0993
(0.191) 		0.0109
(0.0296) 		5e-05
(0.000248) 		1e-05
(6.55e-05) 		0.472
(0.634)
Copy Number Ratio CNMF subtypes 0.000804
(0.0029) 		0.0681
(0.139) 		3e-05
(0.00018) 		8e-05
(0.000372) 		0.0108
(0.0296) 		6e-05
(0.000288) 		0.0065
(0.0195) 		0.278
(0.405) 		0.475
(0.634) 		0.86
(1.00) 		0.017
(0.043) 		0.788
(0.97)
METHLYATION CNMF 4.23e-06
(6.55e-05) 		0.0178
(0.0434) 		1e-05
(6.55e-05) 		1e-05
(6.55e-05) 		0.25
(0.375) 		0.00014
(0.000611) 		2e-05
(0.000125) 		0.343
(0.47) 		0.762
(0.946) 		0.663
(0.844) 		0.0347
(0.0769) 		1
(1.00)
RPPA CNMF subtypes 1.65e-11
(1.19e-09) 		0.173
(0.273) 		1e-05
(6.55e-05) 		1e-05
(6.55e-05) 		0.0121
(0.0318) 		1e-05
(6.55e-05) 		0.102
(0.191) 		0.109
(0.196) 		0.112
(0.197) 		0.351
(0.476)
RPPA cHierClus subtypes 1.32e-13
(1.9e-11) 		0.111
(0.197) 		1e-05
(6.55e-05) 		1e-05
(6.55e-05) 		0.0527
(0.113) 		1e-05
(6.55e-05) 		0.105
(0.194) 		0.0648
(0.135) 		0.85
(1.00) 		0.0686
(0.139)
RNAseq CNMF subtypes 7.09e-07
(2.55e-05) 		0.143
(0.237) 		1e-05
(6.55e-05) 		1e-05
(6.55e-05) 		0.0127
(0.0326) 		0.00039
(0.00156) 		5e-05
(0.000248) 		0.315
(0.448) 		0.807
(0.977) 		0.858
(1.00) 		0.0195
(0.0468) 		0.107
(0.195)
RNAseq cHierClus subtypes 3.67e-08
(1.76e-06) 		0.119
(0.204) 		1e-05
(6.55e-05) 		1e-05
(6.55e-05) 		0.00399
(0.0128) 		5e-05
(0.000248) 		1e-05
(6.55e-05) 		0.278
(0.405) 		0.685
(0.865) 		0.00226
(0.00757) 		0.656
(0.844)
MIRSEQ CNMF 8.12e-06
(6.55e-05) 		0.0383
(0.0836) 		0.00023
(0.000946) 		0.0005
(0.00193) 		0.0114
(0.0303) 		0.00073
(0.0027) 		0.00226
(0.00757) 		0.908
(1.00) 		0.322
(0.45) 		1
(1.00) 		0.208
(0.319) 		0.181
(0.283)
MIRSEQ CHIERARCHICAL 0.000219
(0.000926) 		0.0794
(0.157) 		5e-05
(0.000248) 		9e-05
(0.000405) 		0.0202
(0.0476) 		5e-05
(0.000248) 		0.00114
(0.004) 		0.166
(0.271) 		0.807
(0.977) 		0.658
(0.844) 		0.13
(0.218) 		0.0175
(0.0434)
MIRseq Mature CNMF subtypes 0.00975
(0.0275) 		0.706
(0.884) 		0.305
(0.439) 		0.102
(0.191) 		0.2
(0.309) 		0.116
(0.201) 		0.0331
(0.0744) 		0.985
(1.00) 		1e-05
(6.55e-05) 		0.325
(0.45)
MIRseq Mature cHierClus subtypes 0.577
(0.763) 		0.0635
(0.135) 		0.24
(0.363) 		0.0285
(0.0652) 		0.169
(0.271) 		0.866
(1.00) 		0.00292
(0.00956) 		0.848
(1.00) 		1
(1.00) 		1e-05
(6.55e-05) 		0.276
(0.405)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 34 24 14
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0843 (logrank test), Q value = 0.16

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

nPatients nDeath Duration Range (Median), Month
ALL 72 14 0.5 - 101.1 (36.6)
subtype1 34 4 1.4 - 101.1 (32.0)
subtype2 24 9 0.5 - 93.3 (38.2)
subtype3 14 1 10.5 - 84.4 (38.8)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.873 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 71 60.5 (12.4)
subtype1 33 60.2 (13.8)
subtype2 24 59.9 (11.1)
subtype3 14 62.6 (11.3)

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

'mRNA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00582 (Fisher's exact test), Q value = 0.018

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 40 13 14 5
subtype1 23 4 6 1
subtype2 9 3 8 4
subtype3 8 6 0 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00784 (Fisher's exact test), Q value = 0.023

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

nPatients T1 T2 T3
ALL 41 14 17
subtype1 23 4 7
subtype2 10 4 10
subtype3 8 6 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0703 (Fisher's exact test), Q value = 0.14

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

nPatients 0 1
ALL 35 3
subtype1 18 0
subtype2 10 3
subtype3 7 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.121 (Fisher's exact test), Q value = 0.21

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

nPatients 0 1
ALL 67 5
subtype1 33 1
subtype2 20 4
subtype3 14 0

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.635 (Fisher's exact test), Q value = 0.83

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

nPatients FEMALE MALE
ALL 29 43
subtype1 15 19
subtype2 10 14
subtype3 4 10

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.0075 (Fisher's exact test), Q value = 0.022

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 5 62
subtype1 0 0 32
subtype2 0 2 20
subtype3 1 3 10

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.317 (Fisher's exact test), Q value = 0.45

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 42
subtype1 5 19
subtype2 2 11
subtype3 0 12

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S11.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 12 12 4 11 13 9 11
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0254 (logrank test), Q value = 0.059

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 72 14 0.5 - 101.1 (36.6)
subtype1 12 1 12.3 - 47.2 (31.2)
subtype2 12 2 0.5 - 90.3 (31.0)
subtype3 4 2 1.7 - 37.6 (17.3)
subtype4 11 6 14.2 - 93.3 (54.6)
subtype5 13 3 11.1 - 101.1 (48.4)
subtype6 9 0 1.4 - 59.8 (28.7)
subtype7 11 0 12.1 - 84.4 (40.7)

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.168 (Kruskal-Wallis (anova)), Q value = 0.27

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

nPatients Mean (Std.Dev)
ALL 71 60.5 (12.4)
subtype1 11 63.3 (13.7)
subtype2 12 62.2 (10.8)
subtype3 4 57.2 (10.9)
subtype4 11 56.2 (10.9)
subtype5 13 64.1 (13.5)
subtype6 9 50.7 (10.7)
subtype7 11 65.4 (10.9)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00051 (Fisher's exact test), Q value = 0.0019

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 40 13 14 5
subtype1 8 2 2 0
subtype2 8 2 2 0
subtype3 2 2 0 0
subtype4 0 1 6 4
subtype5 7 2 3 1
subtype6 8 0 1 0
subtype7 7 4 0 0

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00423 (Fisher's exact test), Q value = 0.013

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 41 14 17
subtype1 8 2 2
subtype2 8 2 2
subtype3 2 2 0
subtype4 1 2 8
subtype5 7 2 4
subtype6 8 0 1
subtype7 7 4 0

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0993 (Fisher's exact test), Q value = 0.19

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 35 3
subtype1 6 0
subtype2 5 0
subtype3 3 0
subtype4 5 3
subtype5 9 0
subtype6 3 0
subtype7 4 0

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0109 (Fisher's exact test), Q value = 0.03

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 67 5
subtype1 12 0
subtype2 12 0
subtype3 4 0
subtype4 7 4
subtype5 12 1
subtype6 9 0
subtype7 11 0

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 5e-05 (Fisher's exact test), Q value = 0.00025

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 29 43
subtype1 0 12
subtype2 3 9
subtype3 1 3
subtype4 6 5
subtype5 12 1
subtype6 3 6
subtype7 4 7

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 5 62
subtype1 0 0 11
subtype2 0 1 10
subtype3 1 3 0
subtype4 0 0 10
subtype5 0 0 13
subtype6 0 0 8
subtype7 0 1 10

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.472 (Fisher's exact test), Q value = 0.63

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 42
subtype1 1 7
subtype2 0 4
subtype3 0 2
subtype4 2 6
subtype5 3 7
subtype6 1 5
subtype7 0 11

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

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

Table S21.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 237 133 154
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.000804 (logrank test), Q value = 0.0029

Table S22.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 522 168 0.1 - 120.6 (36.4)
subtype1 237 57 0.1 - 120.6 (37.0)
subtype2 131 46 0.1 - 112.8 (38.6)
subtype3 154 65 0.1 - 109.9 (31.3)

Figure S19.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0681 (Kruskal-Wallis (anova)), Q value = 0.14

Table S23.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 523 60.6 (12.1)
subtype1 237 60.0 (12.7)
subtype2 133 59.7 (11.4)
subtype3 153 62.4 (11.7)

Figure S20.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00018

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 260 57 127 80
subtype1 140 29 49 19
subtype2 66 13 31 23
subtype3 54 15 47 38

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 8e-05 (Fisher's exact test), Q value = 0.00037

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 265 69 179 11
subtype1 140 34 62 1
subtype2 67 16 44 6
subtype3 58 19 73 4

Figure S22.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0108 (Fisher's exact test), Q value = 0.03

Table S26.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 232 18
subtype1 108 3
subtype2 58 4
subtype3 66 11

Figure S23.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.00029

Table S27.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 418 78
subtype1 203 19
subtype2 105 21
subtype3 110 38

Figure S24.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0065 (Fisher's exact test), Q value = 0.019

Table S28.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 188 336
subtype1 92 145
subtype2 56 77
subtype3 40 114

Figure S25.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.278 (Kruskal-Wallis (anova)), Q value = 0.4

Table S29.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 44 88.2 (21.4)
subtype1 18 92.8 (8.3)
subtype2 8 95.0 (7.6)
subtype3 18 80.6 (31.0)

Figure S26.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.475 (Kruskal-Wallis (anova)), Q value = 0.63

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 15 28.9 (18.1)
subtype1 7 33.0 (20.2)
subtype2 3 33.3 (20.8)
subtype3 5 20.6 (13.7)

Figure S27.  Get High-res Image Clustering Approach #3: '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 = 0.86 (Kruskal-Wallis (anova)), Q value = 1

Table S31.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 8 1979.6 (20.8)
subtype1 4 1970.2 (22.8)
subtype2 1 1996.0 (NA)
subtype3 3 1986.7 (18.8)

Figure S28.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.017 (Fisher's exact test), Q value = 0.043

Table S32.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 52 457
subtype1 2 20 212
subtype2 4 22 106
subtype3 2 10 139

Figure S29.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.788 (Fisher's exact test), Q value = 0.97

Table S33.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 26 345
subtype1 13 149
subtype2 7 95
subtype3 6 101

Figure S30.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S34.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 128 77 110
'METHLYATION CNMF' versus 'Time to Death'

P value = 4.23e-06 (logrank test), Q value = 6.5e-05

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 313 100 0.1 - 120.6 (29.1)
subtype1 127 21 0.1 - 120.6 (35.6)
subtype2 76 25 0.5 - 109.9 (28.4)
subtype3 110 54 0.2 - 90.1 (27.2)

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0178 (Kruskal-Wallis (anova)), Q value = 0.043

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 315 61.5 (11.8)
subtype1 128 59.4 (12.7)
subtype2 77 62.1 (11.4)
subtype3 110 63.5 (10.7)

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 153 31 75 56
subtype1 87 17 14 10
subtype2 43 2 20 12
subtype3 23 12 41 34

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 156 41 110 8
subtype1 87 21 20 0
subtype2 45 3 25 4
subtype3 24 17 65 4

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.25 (Fisher's exact test), Q value = 0.37

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 131 9
subtype1 52 1
subtype2 31 3
subtype3 48 5

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.00014 (Fisher's exact test), Q value = 0.00061

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 234 53
subtype1 103 10
subtype2 58 10
subtype3 73 33

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 2e-05 (Fisher's exact test), Q value = 0.00013

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 113 202
subtype1 61 67
subtype2 31 46
subtype3 21 89

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.343 (Kruskal-Wallis (anova)), Q value = 0.47

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 36 91.4 (9.3)
subtype1 20 90.5 (10.5)
subtype2 8 95.0 (7.6)
subtype3 8 90.0 (7.6)

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.762 (Kruskal-Wallis (anova)), Q value = 0.95

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 15 28.9 (18.1)
subtype1 5 32.0 (24.0)
subtype2 6 31.2 (18.0)
subtype3 4 21.8 (11.4)

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.663 (Kruskal-Wallis (anova)), Q value = 0.84

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 8 1979.6 (20.8)
subtype1 3 1971.0 (27.8)
subtype2 2 1980.5 (21.9)
subtype3 3 1987.7 (17.1)

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0347 (Fisher's exact test), Q value = 0.077

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 45 266
subtype1 0 19 109
subtype2 0 17 60
subtype3 1 9 97

Figure S41.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

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

Table S46.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 255
subtype1 4 98
subtype2 2 63
subtype3 4 94

Figure S42.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S47.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 108 62 77 81 81 45
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 1.65e-11 (logrank test), Q value = 1.2e-09

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

nPatients nDeath Duration Range (Median), Month
ALL 454 158 0.1 - 120.6 (37.1)
subtype1 108 28 0.2 - 120.6 (46.5)
subtype2 62 43 0.5 - 102.5 (22.7)
subtype3 77 24 0.2 - 112.8 (36.4)
subtype4 81 27 0.5 - 99.8 (37.2)
subtype5 81 31 0.1 - 105.9 (40.7)
subtype6 45 5 0.9 - 103.4 (37.0)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.173 (Kruskal-Wallis (anova)), Q value = 0.27

Table S49.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 107 61.8 (10.9)
subtype2 62 62.1 (11.9)
subtype3 77 61.6 (12.4)
subtype4 81 60.2 (11.7)
subtype5 81 57.7 (12.3)
subtype6 45 58.0 (16.0)

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

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S50.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 219 44 115 76
subtype1 58 12 24 14
subtype2 8 4 26 24
subtype3 33 10 23 11
subtype4 47 7 18 9
subtype5 38 9 16 18
subtype6 35 2 8 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S51.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 224 54 165 11
subtype1 58 16 33 1
subtype2 9 9 40 4
subtype3 35 10 32 0
subtype4 48 7 25 1
subtype5 39 10 27 5
subtype6 35 2 8 0

Figure S46.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0121 (Fisher's exact test), Q value = 0.032

Table S52.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 208 16
subtype1 51 1
subtype2 26 8
subtype3 40 2
subtype4 38 3
subtype5 39 2
subtype6 14 0

Figure S47.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S53.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 379 75
subtype1 94 14
subtype2 38 24
subtype3 66 11
subtype4 72 9
subtype5 64 17
subtype6 45 0

Figure S48.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

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

Table S54.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 46 62
subtype2 16 46
subtype3 23 54
subtype4 22 59
subtype5 25 56
subtype6 19 26

Figure S49.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.109 (Kruskal-Wallis (anova)), Q value = 0.2

Table S55.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 7 91.4 (6.9)
subtype2 3 93.3 (11.5)
subtype3 10 90.0 (9.4)
subtype4 3 100.0 (0.0)
subtype5 7 94.3 (5.3)
subtype6 4 100.0 (0.0)

Figure S50.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.112 (Fisher's exact test), Q value = 0.2

Table S56.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 20 420
subtype1 0 5 101
subtype2 2 0 60
subtype3 1 1 71
subtype4 1 7 73
subtype5 3 5 73
subtype6 1 2 42

Figure S51.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.351 (Fisher's exact test), Q value = 0.48

Table S57.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 19 295
subtype1 6 72
subtype2 3 45
subtype3 5 53
subtype4 1 45
subtype5 1 55
subtype6 3 25

Figure S52.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S58.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 122 200 132
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 1.32e-13 (logrank test), Q value = 1.9e-11

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

nPatients nDeath Duration Range (Median), Month
ALL 454 158 0.1 - 120.6 (37.1)
subtype1 122 32 0.4 - 120.6 (44.3)
subtype2 200 47 0.5 - 105.9 (38.8)
subtype3 132 79 0.1 - 112.8 (27.1)

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.111 (Kruskal-Wallis (anova)), Q value = 0.2

Table S60.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 121 60.9 (12.0)
subtype2 200 59.0 (13.0)
subtype3 132 62.0 (11.2)

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

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S61.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 219 44 115 76
subtype1 54 15 39 14
subtype2 130 16 37 17
subtype3 35 13 39 45

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S62.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 224 54 165 11
subtype1 55 17 49 1
subtype2 132 19 48 1
subtype3 37 18 68 9

Figure S56.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0527 (Fisher's exact test), Q value = 0.11

Table S63.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 208 16
subtype1 63 2
subtype2 78 4
subtype3 67 10

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S64.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 379 75
subtype1 109 13
subtype2 182 18
subtype3 88 44

Figure S58.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.105 (Fisher's exact test), Q value = 0.19

Table S65.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 34 88
subtype2 77 123
subtype3 40 92

Figure S59.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0648 (Kruskal-Wallis (anova)), Q value = 0.14

Table S66.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 8 90.0 (9.3)
subtype2 16 96.9 (4.8)
subtype3 10 91.0 (8.8)

Figure S60.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.85 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 20 420
subtype1 1 4 115
subtype2 4 9 184
subtype3 3 7 121

Figure S61.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0686 (Fisher's exact test), Q value = 0.14

Table S68.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 19 295
subtype1 9 82
subtype2 8 117
subtype3 2 96

Figure S62.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S69.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 114 222 193
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 7.09e-07 (logrank test), Q value = 2.6e-05

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

nPatients nDeath Duration Range (Median), Month
ALL 527 168 0.1 - 120.6 (36.4)
subtype1 113 32 0.1 - 112.8 (37.6)
subtype2 221 48 0.1 - 120.6 (37.4)
subtype3 193 88 0.2 - 109.9 (30.5)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.143 (Kruskal-Wallis (anova)), Q value = 0.24

Table S71.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 528 60.7 (12.2)
subtype1 114 58.6 (12.7)
subtype2 221 61.5 (12.1)
subtype3 193 60.9 (11.7)

Figure S64.  Get High-res Image Clustering Approach #7: '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 = 6.5e-05

Table S72.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 265 57 126 81
subtype1 70 12 20 12
subtype2 134 23 41 24
subtype3 61 22 65 45

Figure S65.  Get High-res Image Clustering Approach #7: '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 = 6.5e-05

Table S73.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 270 69 179 11
subtype1 70 13 28 3
subtype2 134 27 59 2
subtype3 66 29 92 6

Figure S66.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0127 (Fisher's exact test), Q value = 0.033

Table S74.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 238 17
subtype1 50 3
subtype2 100 2
subtype3 88 12

Figure S67.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00039 (Fisher's exact test), Q value = 0.0016

Table S75.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 422 79
subtype1 96 11
subtype2 185 23
subtype3 141 45

Figure S68.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 5e-05 (Fisher's exact test), Q value = 0.00025

Table S76.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 187 342
subtype1 39 75
subtype2 101 121
subtype3 47 146

Figure S69.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.315 (Kruskal-Wallis (anova)), Q value = 0.45

Table S77.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 43 90.2 (16.7)
subtype1 9 95.6 (7.3)
subtype2 19 90.0 (10.5)
subtype3 15 87.3 (25.2)

Figure S70.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.807 (Kruskal-Wallis (anova)), Q value = 0.98

Table S78.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 15 28.9 (18.1)
subtype1 4 26.0 (18.2)
subtype2 5 34.0 (24.2)
subtype3 6 26.7 (14.6)

Figure S71.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.858 (Kruskal-Wallis (anova)), Q value = 1

Table S79.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 8 1979.6 (20.8)
subtype1 1 1999.0 (NA)
subtype2 3 1971.0 (27.8)
subtype3 4 1981.2 (17.1)

Figure S72.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0195 (Fisher's exact test), Q value = 0.047

Table S80.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 52 462
subtype1 4 19 90
subtype2 2 19 197
subtype3 2 14 175

Figure S73.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.107 (Fisher's exact test), Q value = 0.19

Table S81.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 26 350
subtype1 3 80
subtype2 16 140
subtype3 7 130

Figure S74.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S82.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 118 34 183 132 35 27
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 3.67e-08 (logrank test), Q value = 1.8e-06

Table S83.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 527 168 0.1 - 120.6 (36.4)
subtype1 118 32 0.1 - 97.4 (30.3)
subtype2 33 6 0.5 - 112.8 (37.1)
subtype3 182 38 0.1 - 120.6 (42.3)
subtype4 132 71 0.2 - 109.9 (31.5)
subtype5 35 15 0.1 - 102.5 (36.4)
subtype6 27 6 3.6 - 84.4 (43.2)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.119 (Kruskal-Wallis (anova)), Q value = 0.2

Table S84.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 528 60.7 (12.2)
subtype1 118 58.8 (12.3)
subtype2 34 57.8 (13.8)
subtype3 182 61.5 (12.2)
subtype4 132 62.4 (11.4)
subtype5 35 58.3 (11.9)
subtype6 27 61.4 (11.5)

Figure S76.  Get High-res Image Clustering Approach #8: '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 = 6.5e-05

Table S85.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 265 57 126 81
subtype1 62 12 24 20
subtype2 25 4 2 3
subtype3 116 19 36 12
subtype4 37 13 47 35
subtype5 5 5 15 10
subtype6 20 4 2 1

Figure S77.  Get High-res Image Clustering Approach #8: '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 = 6.5e-05

Table S86.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 270 69 179 11
subtype1 63 17 36 2
subtype2 25 4 5 0
subtype3 116 21 46 0
subtype4 39 15 71 7
subtype5 7 8 19 1
subtype6 20 4 2 1

Figure S78.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00399 (Fisher's exact test), Q value = 0.013

Table S87.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 238 17
subtype1 43 1
subtype2 15 1
subtype3 84 1
subtype4 66 10
subtype5 17 4
subtype6 13 0

Figure S79.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00025

Table S88.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 422 79
subtype1 94 18
subtype2 28 3
subtype3 158 13
subtype4 95 34
subtype5 22 10
subtype6 25 1

Figure S80.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S89.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 187 342
subtype1 16 102
subtype2 16 18
subtype3 101 82
subtype4 31 101
subtype5 17 18
subtype6 6 21

Figure S81.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.278 (Kruskal-Wallis (anova)), Q value = 0.4

Table S90.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 43 90.2 (16.7)
subtype1 8 95.0 (5.3)
subtype2 6 95.0 (12.2)
subtype3 16 90.6 (10.0)
subtype4 9 81.1 (31.4)
subtype5 1 80.0 (NA)
subtype6 3 96.7 (5.8)

Figure S82.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.685 (Kruskal-Wallis (anova)), Q value = 0.86

Table S91.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 15 28.9 (18.1)
subtype1 4 30.8 (13.1)
subtype2 2 45.0 (7.1)
subtype3 4 31.0 (26.8)
subtype4 3 20.0 (17.3)
subtype5 2 18.5 (16.3)

Figure S83.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.00226 (Fisher's exact test), Q value = 0.0076

Table S92.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 52 462
subtype1 3 11 100
subtype2 2 9 23
subtype3 1 16 164
subtype4 2 6 124
subtype5 0 4 30
subtype6 0 6 21

Figure S84.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.656 (Fisher's exact test), Q value = 0.84

Table S93.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 26 350
subtype1 6 74
subtype2 2 29
subtype3 12 110
subtype4 5 88
subtype5 1 26
subtype6 0 23

Figure S85.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S94.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 125 204 183
'MIRSEQ CNMF' versus 'Time to Death'

P value = 8.12e-06 (logrank test), Q value = 6.5e-05

Table S95.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 510 166 0.1 - 120.6 (36.3)
subtype1 124 32 0.1 - 112.8 (41.2)
subtype2 203 49 0.2 - 120.6 (36.9)
subtype3 183 85 0.2 - 94.0 (31.9)

Figure S86.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0383 (Kruskal-Wallis (anova)), Q value = 0.084

Table S96.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 512 60.6 (12.1)
subtype1 125 58.5 (12.1)
subtype2 204 62.2 (12.3)
subtype3 183 60.3 (11.6)

Figure S87.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00023 (Fisher's exact test), Q value = 0.00095

Table S97.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 251 55 126 80
subtype1 76 11 28 10
subtype2 110 20 46 28
subtype3 65 24 52 42

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 5e-04 (Fisher's exact test), Q value = 0.0019

Table S98.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 256 67 178 11
subtype1 77 11 33 4
subtype2 111 26 65 2
subtype3 68 30 80 5

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0114 (Fisher's exact test), Q value = 0.03

Table S99.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 226 18
subtype1 56 3
subtype2 87 2
subtype3 83 13

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.00073 (Fisher's exact test), Q value = 0.0027

Table S100.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 406 78
subtype1 105 9
subtype2 168 27
subtype3 133 42

Figure S91.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00226 (Fisher's exact test), Q value = 0.0076

Table S101.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 180 332
subtype1 44 81
subtype2 88 116
subtype3 48 135

Figure S92.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.908 (Kruskal-Wallis (anova)), Q value = 1

Table S102.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 44 88.2 (21.4)
subtype1 14 91.4 (11.7)
subtype2 19 92.1 (7.9)
subtype3 11 77.3 (38.8)

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

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.322 (Kruskal-Wallis (anova)), Q value = 0.45

Table S103.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 15 28.9 (18.1)
subtype1 5 21.2 (14.3)
subtype2 4 40.2 (22.8)
subtype3 6 27.8 (16.7)

Figure S94.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 1 (Kruskal-Wallis (anova)), Q value = 1

Table S104.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 8 1979.6 (20.8)
subtype1 3 1977.3 (18.8)
subtype2 3 1971.0 (27.8)
subtype3 2 1996.0 (0.0)

Figure S95.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.208 (Fisher's exact test), Q value = 0.32

Table S105.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 52 445
subtype1 1 19 104
subtype2 3 15 182
subtype3 4 18 159

Figure S96.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.181 (Fisher's exact test), Q value = 0.28

Table S106.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 340
subtype1 2 82
subtype2 12 132
subtype3 10 126

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S107.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 144 136 195 37
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.000219 (logrank test), Q value = 0.00093

Table S108.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 510 166 0.1 - 120.6 (36.3)
subtype1 143 49 0.1 - 112.8 (33.5)
subtype2 136 49 0.1 - 94.0 (32.2)
subtype3 194 44 0.1 - 120.6 (38.1)
subtype4 37 24 1.4 - 89.4 (44.0)

Figure S98.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.0794 (Kruskal-Wallis (anova)), Q value = 0.16

Table S109.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 512 60.6 (12.1)
subtype1 144 59.2 (12.4)
subtype2 136 59.6 (11.9)
subtype3 195 61.7 (12.0)
subtype4 37 64.2 (11.0)

Figure S99.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00025

Table S110.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 251 55 126 80
subtype1 81 10 36 17
subtype2 51 17 35 33
subtype3 110 24 43 18
subtype4 9 4 12 12

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 9e-05 (Fisher's exact test), Q value = 4e-04

Table S111.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 256 67 178 11
subtype1 82 11 46 5
subtype2 53 23 57 3
subtype3 112 28 54 1
subtype4 9 5 21 2

Figure S101.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.0202 (Fisher's exact test), Q value = 0.048

Table S112.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 226 18
subtype1 61 8
subtype2 65 8
subtype3 84 1
subtype4 16 1

Figure S102.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00025

Table S113.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 406 78
subtype1 119 15
subtype2 98 32
subtype3 165 18
subtype4 24 13

Figure S103.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00114 (Fisher's exact test), Q value = 0.004

Table S114.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 180 332
subtype1 54 90
subtype2 36 100
subtype3 84 111
subtype4 6 31

Figure S104.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.166 (Kruskal-Wallis (anova)), Q value = 0.27

Table S115.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 44 88.2 (21.4)
subtype1 15 88.7 (25.3)
subtype2 11 80.0 (28.3)
subtype3 17 92.4 (9.7)
subtype4 1 100.0 (NA)

Figure S105.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.807 (Kruskal-Wallis (anova)), Q value = 0.98

Table S116.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 15 28.9 (18.1)
subtype1 4 23.5 (14.0)
subtype2 6 28.3 (16.9)
subtype3 5 34.0 (24.2)

Figure S106.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.658 (Kruskal-Wallis (anova)), Q value = 0.84

Table S117.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

nPatients Mean (Std.Dev)
ALL 8 1979.6 (20.8)
subtype1 2 1982.0 (24.0)
subtype2 3 1986.7 (16.2)
subtype3 3 1971.0 (27.8)

Figure S107.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.13 (Fisher's exact test), Q value = 0.22

Table S118.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 52 445
subtype1 4 20 120
subtype2 2 14 117
subtype3 2 18 171
subtype4 0 0 37

Figure S108.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.0175 (Fisher's exact test), Q value = 0.043

Table S119.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 340
subtype1 2 93
subtype2 4 98
subtype3 14 123
subtype4 4 26

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S120.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 36 22 49 35
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00975 (logrank test), Q value = 0.028

Table S121.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 140 42 0.1 - 120.6 (33.2)
subtype1 34 8 0.1 - 77.4 (19.0)
subtype2 22 4 6.9 - 112.8 (35.9)
subtype3 49 12 0.8 - 120.6 (38.7)
subtype4 35 18 0.5 - 87.5 (30.6)

Figure S110.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.706 (Kruskal-Wallis (anova)), Q value = 0.88

Table S122.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 142 60.7 (11.6)
subtype1 36 60.7 (10.9)
subtype2 22 58.0 (12.0)
subtype3 49 61.7 (12.3)
subtype4 35 61.2 (11.3)

Figure S111.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.305 (Fisher's exact test), Q value = 0.44

Table S123.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 73 17 26 26
subtype1 22 5 4 5
subtype2 15 1 4 2
subtype3 22 7 12 8
subtype4 14 4 6 11

Figure S112.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

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

Table S124.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 75 19 43 5
subtype1 22 6 7 1
subtype2 16 1 4 1
subtype3 22 8 19 0
subtype4 15 4 13 3

Figure S113.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.2 (Fisher's exact test), Q value = 0.31

Table S125.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 56 5
subtype1 7 1
subtype2 9 1
subtype3 23 0
subtype4 17 3

Figure S114.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.116 (Fisher's exact test), Q value = 0.2

Table S126.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 98 23
subtype1 14 4
subtype2 20 1
subtype3 41 8
subtype4 23 10

Figure S115.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0331 (Fisher's exact test), Q value = 0.074

Table S127.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 93
subtype1 17 19
subtype2 6 16
subtype3 20 29
subtype4 6 29

Figure S116.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.985 (Kruskal-Wallis (anova)), Q value = 1

Table S128.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 20 91.5 (10.9)
subtype1 7 90.0 (14.1)
subtype3 9 92.2 (9.7)
subtype4 4 92.5 (9.6)

Figure S117.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 28 108
subtype1 0 22 14
subtype2 1 2 18
subtype3 0 0 48
subtype4 2 4 28

Figure S118.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.325 (Fisher's exact test), Q value = 0.45

Table S130.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 101
subtype1 0 29
subtype2 1 13
subtype3 4 37
subtype4 1 22

Figure S119.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S131.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 38 56 48
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.577 (logrank test), Q value = 0.76

Table S132.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 140 42 0.1 - 120.6 (33.2)
subtype1 36 8 0.1 - 77.4 (17.9)
subtype2 56 21 0.5 - 112.8 (35.9)
subtype3 48 13 0.8 - 120.6 (36.5)

Figure S120.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0635 (Kruskal-Wallis (anova)), Q value = 0.13

Table S133.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 142 60.7 (11.6)
subtype1 38 61.0 (10.7)
subtype2 56 58.1 (11.7)
subtype3 48 63.6 (11.8)

Figure S121.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.24 (Fisher's exact test), Q value = 0.36

Table S134.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 73 17 26 26
subtype1 25 4 4 5
subtype2 27 4 12 13
subtype3 21 9 10 8

Figure S122.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0285 (Fisher's exact test), Q value = 0.065

Table S135.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 75 19 43 5
subtype1 26 5 6 1
subtype2 27 4 21 4
subtype3 22 10 16 0

Figure S123.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.169 (Fisher's exact test), Q value = 0.27

Table S136.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 56 5
subtype1 8 1
subtype2 26 4
subtype3 22 0

Figure S124.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.866 (Fisher's exact test), Q value = 1

Table S137.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 98 23
subtype1 17 4
subtype2 41 11
subtype3 40 8

Figure S125.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.00292 (Fisher's exact test), Q value = 0.0096

Table S138.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 93
subtype1 17 21
subtype2 10 46
subtype3 22 26

Figure S126.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.848 (Kruskal-Wallis (anova)), Q value = 1

Table S139.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 20 91.5 (10.9)
subtype1 7 90.0 (14.1)
subtype2 5 94.0 (8.9)
subtype3 8 91.2 (9.9)

Figure S127.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 1 (Kruskal-Wallis (anova)), Q value = 1

Table S140.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 8 27.2 (20.2)
subtype1 4 28.5 (26.2)
subtype2 3 21.3 (16.3)
subtype3 1 40.0 (NA)

Figure S128.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 6.5e-05

Table S141.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 28 108
subtype1 0 21 17
subtype2 3 6 46
subtype3 0 1 45

Figure S129.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.276 (Fisher's exact test), Q value = 0.4

Table S142.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 101
subtype1 0 31
subtype2 3 35
subtype3 3 35

Figure S130.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KIRC-TP/15107132/KIRC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIRC-TP/15081616/KIRC-TP.merged_data.txt

  • Number of patients = 533

  • Number of clustering approaches = 12

  • 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)
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