Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1416VXC
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1416VXC
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 11 clinical features across 525 patients, 39 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 do not correlate to any clinical features.

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

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'PATHOLOGY.M.STAGE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.M.STAGE', and 'GENDER'.

  • CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.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 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.M.STAGE', and 'GENDER'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE',  'PATHOLOGY.M.STAGE', and 'GENDER'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'GENDER'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.M.STAGE', and 'GENDER'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.M.STAGE' and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PATHOLOGY.M.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 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 39 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE NEOPLASM
DISEASESTAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER KARNOFSKY
PERFORMANCE
SCORE 		NUMBERPACKYEARSSMOKED 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) Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.257
(1.00) 		0.873
(1.00) 		0.00606
(0.497) 		0.00843
(0.666) 		0.0704
(1.00) 		0.12
(1.00) 		0.632
(1.00) 		0.00758
(0.606) 		0.315
(1.00)
mRNA cHierClus subtypes 2.96e-06
(0.000368) 		0.168
(1.00) 		0.00054
(0.0502) 		0.004
(0.336) 		0.1
(1.00) 		0.0107
(0.821) 		7e-05
(0.007) 		1e-05
(0.00123) 		0.471
(1.00)
Copy Number Ratio CNMF subtypes 0.442
(1.00) 		0.0569
(1.00) 		3e-05
(0.00312) 		3e-05
(0.00312) 		0.0102
(0.796) 		0.00043
(0.0404) 		0.00438
(0.364) 		0.414
(1.00) 		0.506
(1.00) 		0.0125
(0.936) 		0.732
(1.00)
METHLYATION CNMF 0.582
(1.00) 		0.00693
(0.562) 		1e-05
(0.00123) 		1e-05
(0.00123) 		0.25
(1.00) 		0.00014
(0.0136) 		3e-05
(0.00312) 		0.367
(1.00) 		0.672
(1.00) 		0.026
(1.00) 		1
(1.00)
RPPA CNMF subtypes 0.0393
(1.00) 		0.173
(1.00) 		1e-05
(0.00123) 		1e-05
(0.00123) 		0.013
(0.952) 		1e-05
(0.00123) 		0.102
(1.00) 		0.109
(1.00) 		0.112
(1.00) 		0.352
(1.00)
RPPA cHierClus subtypes 2.35e-05
(0.00247) 		0.111
(1.00) 		1e-05
(0.00123) 		1e-05
(0.00123) 		0.0539
(1.00) 		1e-05
(0.00123) 		0.104
(1.00) 		0.0648
(1.00) 		0.849
(1.00) 		0.0664
(1.00)
RNAseq CNMF subtypes 0.0137
(0.989) 		0.16
(1.00) 		1e-05
(0.00123) 		1e-05
(0.00123) 		0.0125
(0.936) 		0.00169
(0.15) 		2e-05
(0.00214) 		0.554
(1.00) 		0.541
(1.00) 		0.033
(1.00) 		0.122
(1.00)
RNAseq cHierClus subtypes 0.019
(1.00) 		0.162
(1.00) 		1e-05
(0.00123) 		1e-05
(0.00123) 		0.00114
(0.104) 		0.00187
(0.165) 		0.00012
(0.0118) 		0.198
(1.00) 		0.88
(1.00) 		0.0333
(1.00) 		0.756
(1.00)
MIRSEQ CNMF 0.042
(1.00) 		0.0281
(1.00) 		0.00038
(0.0361) 		0.00078
(0.0718) 		0.0119
(0.907) 		0.00335
(0.285) 		0.00247
(0.212) 		0.777
(1.00) 		0.591
(1.00) 		0.299
(1.00) 		0.224
(1.00)
MIRSEQ CHIERARCHICAL 0.104
(1.00) 		0.0704
(1.00) 		2e-05
(0.00214) 		8e-05
(0.00792) 		0.0194
(1.00) 		0.00027
(0.0259) 		0.00137
(0.123) 		0.156
(1.00) 		0.884
(1.00) 		0.14
(1.00) 		0.0181
(1.00)
MIRseq Mature CNMF subtypes 0.633
(1.00) 		0.68
(1.00) 		0.343
(1.00) 		0.116
(1.00) 		0.172
(1.00) 		1e-05
(0.00123) 		0.0251
(1.00) 		0.994
(1.00) 		1e-05
(0.00123) 		0.454
(1.00)
MIRseq Mature cHierClus subtypes 0.43
(1.00) 		0.0614
(1.00) 		0.342
(1.00) 		0.0441
(1.00) 		0.16
(1.00) 		6e-05
(0.00606) 		0.00216
(0.188) 		0.843
(1.00) 		1e-05
(0.00123) 		0.387
(1.00)
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 = 0.257 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 61 3 16.0 - 3074.0 (1137.0)
subtype1 30 0 43.0 - 3074.0 (1106.0)
subtype2 17 2 16.0 - 2839.0 (1314.0)
subtype3 14 1 319.0 - 2566.0 (1180.0)

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

'mRNA CNMF subtypes' versus 'AGE'

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

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

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: 'AGE'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00606 (Fisher's exact test), Q value = 0.5

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 = 0.00843 (Fisher's exact test), Q value = 0.67

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 = 0.0704 (Fisher's exact test), Q value = 1

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 = 0.12 (Fisher's exact test), Q value = 1

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

nPatients M0 M1
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 = 0.632 (Fisher's exact test), Q value = 1

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 = 0.00758 (Fisher's exact test), Q value = 0.61

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: '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 #10: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.315 (Fisher's exact test), Q value = 1

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: '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 #11: '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 = 2.96e-06 (logrank test), Q value = 0.00037

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

nPatients nDeath Duration Range (Median), Year
ALL 61 3 16.0 - 3074.0 (1137.0)
subtype1 11 0 374.0 - 1436.0 (952.0)
subtype2 10 0 16.0 - 2746.0 (941.5)
subtype3 4 2 51.0 - 1143.0 (527.0)
subtype4 6 1 1491.0 - 2839.0 (1961.0)
subtype5 10 0 523.0 - 3074.0 (1496.5)
subtype6 9 0 43.0 - 1820.0 (873.0)
subtype7 11 0 369.0 - 2566.0 (1238.0)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.168 (Kruskal-Wallis (anova)), Q value = 1

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

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: 'AGE'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00054 (Fisher's exact test), Q value = 0.05

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 = 0.004 (Fisher's exact test), Q value = 0.34

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 = 0.1 (Fisher's exact test), Q value = 1

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 = 0.0107 (Fisher's exact test), Q value = 0.82

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

nPatients M0 M1
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 = 7e-05 (Fisher's exact test), Q value = 0.007

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 = 0.0012

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: '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 #10: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.471 (Fisher's exact test), Q value = 1

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: '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 #11: '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 232 131 153
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.442 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 371 24 3.0 - 3668.0 (1280.0)
subtype1 183 9 7.0 - 3668.0 (1165.0)
subtype2 92 8 16.0 - 3431.0 (1416.5)
subtype3 96 7 3.0 - 3343.0 (1285.5)

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 'AGE'

P value = 0.0569 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 515 60.5 (12.1)
subtype1 232 59.9 (12.6)
subtype2 131 59.6 (11.4)
subtype3 152 62.4 (11.8)

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

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

P value = 3e-05 (Fisher's exact test), Q value = 0.0031

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 253 57 126 80
subtype1 136 29 48 19
subtype2 64 13 31 23
subtype3 53 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 = 3e-05 (Fisher's exact test), Q value = 0.0031

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 258 69 178 11
subtype1 136 34 61 1
subtype2 65 16 44 6
subtype3 57 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 = 0.0102 (Fisher's exact test), Q value = 0.8

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

nPatients 0 1
ALL 231 18
subtype1 107 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 = 0.00043 (Fisher's exact test), Q value = 0.04

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

nPatients M0 M1 MX
ALL 417 78 19
subtype1 202 19 10
subtype2 105 21 4
subtype3 110 38 5

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 = 0.00438 (Fisher's exact test), Q value = 0.36

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

nPatients FEMALE MALE
ALL 186 330
subtype1 92 140
subtype2 55 76
subtype3 39 114

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 = 0.414 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 41 88.5 (21.6)
subtype1 16 92.5 (8.6)
subtype2 8 95.0 (7.6)
subtype3 17 81.8 (31.5)

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 'NUMBERPACKYEARSSMOKED'

P value = 0.506 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 12 27.2 (16.2)
subtype1 4 30.8 (17.2)
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: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0125 (Fisher's exact test), Q value = 0.94

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 44 457
subtype1 2 15 212
subtype2 4 20 106
subtype3 2 9 139

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.732 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 26 338
subtype1 13 145
subtype2 7 93
subtype3 6 100

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

Clustering Approach #4: 'METHLYATION CNMF'

Table S33.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 122 76 109
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.582 (logrank test), Q value = 1

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 217 11 3.0 - 3668.0 (1082.0)
subtype1 105 4 3.0 - 3668.0 (1124.0)
subtype2 53 3 15.0 - 3343.0 (932.0)
subtype3 59 4 7.0 - 2741.0 (1280.0)

Figure S30.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00693 (Kruskal-Wallis (anova)), Q value = 0.56

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 307 61.4 (11.8)
subtype1 122 59.1 (12.6)
subtype2 76 62.1 (11.5)
subtype3 109 63.6 (10.7)

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 146 31 74 56
subtype1 82 17 13 10
subtype2 42 2 20 12
subtype3 22 12 41 34

Figure S32.  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 = 0.0012

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 149 41 109 8
subtype1 82 21 19 0
subtype2 44 3 25 4
subtype3 23 17 65 4

Figure S33.  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 = 1

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 130 9
subtype1 51 1
subtype2 31 3
subtype3 48 5

Figure S34.  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 = 0.014

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 233 53 19
subtype1 102 10 9
subtype2 58 10 7
subtype3 73 33 3

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 3e-05 (Fisher's exact test), Q value = 0.0031

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 111 196
subtype1 59 63
subtype2 31 45
subtype3 21 88

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.367 (Kruskal-Wallis (anova)), Q value = 1

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 33 92.1 (7.8)
subtype1 18 91.7 (7.9)
subtype2 8 95.0 (7.6)
subtype3 7 90.0 (8.2)

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.672 (Kruskal-Wallis (anova)), Q value = 1

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 12 27.2 (16.2)
subtype1 3 28.3 (18.6)
subtype2 6 31.2 (18.0)
subtype3 3 18.0 (10.6)

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'RACE'

P value = 0.026 (Fisher's exact test), Q value = 1

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 37 266
subtype1 0 13 109
subtype2 0 16 60
subtype3 1 8 97

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

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

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 248
subtype1 4 93
subtype2 2 62
subtype3 4 93

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S45.  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 = 0.0393 (logrank test), Q value = 1

Table S46.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 319 23 7.0 - 3668.0 (1373.0)
subtype1 84 4 7.0 - 3668.0 (1503.0)
subtype2 24 5 16.0 - 3117.0 (1408.5)
subtype3 57 4 7.0 - 3431.0 (1106.0)
subtype4 59 5 15.0 - 3037.0 (1133.0)
subtype5 55 5 18.0 - 3222.0 (1459.0)
subtype6 40 0 29.0 - 3146.0 (1149.0)

Figure S41.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.173 (Kruskal-Wallis (anova)), Q value = 1

Table S47.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

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 S42.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S48.  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 S43.  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 = 0.0012

Table S49.  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 S44.  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 = 0.013 (Fisher's exact test), Q value = 0.95

Table S50.  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 S45.  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 = 0.0012

Table S51.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 379 75
subtype1 94 14
subtype2 38 24
subtype3 66 11
subtype4 72 9
subtype5 64 17
subtype6 45 0

Figure S46.  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 = 1

Table S52.  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 S47.  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 = 1

Table S53.  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 S48.  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 = 1

Table S54.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: '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 S49.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.352 (Fisher's exact test), Q value = 1

Table S55.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: '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 S50.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S56.  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 = 2.35e-05 (logrank test), Q value = 0.0025

Table S57.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 319 23 7.0 - 3668.0 (1373.0)
subtype1 93 3 11.0 - 3668.0 (1487.0)
subtype2 160 7 16.0 - 3222.0 (1378.0)
subtype3 66 13 7.0 - 3431.0 (1137.5)

Figure S51.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.111 (Kruskal-Wallis (anova)), Q value = 1

Table S58.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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 S52.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S59.  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 S53.  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 = 0.0012

Table S60.  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 S54.  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 = 0.0539 (Fisher's exact test), Q value = 1

Table S61.  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 S55.  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 = 0.0012

Table S62.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1
ALL 379 75
subtype1 109 13
subtype2 182 18
subtype3 88 44

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.104 (Fisher's exact test), Q value = 1

Table S63.  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 S57.  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 = 1

Table S64.  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 S58.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.849 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: '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 S59.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0664 (Fisher's exact test), Q value = 1

Table S66.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

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

Figure S60.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S67.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 107 218 194
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0137 (logrank test), Q value = 0.99

Table S68.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 373 23 3.0 - 3668.0 (1307.0)
subtype1 84 8 13.0 - 3431.0 (1311.0)
subtype2 174 4 3.0 - 3668.0 (1299.0)
subtype3 115 11 7.0 - 3343.0 (1280.0)

Figure S61.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.16 (Kruskal-Wallis (anova)), Q value = 1

Table S69.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 518 60.7 (12.1)
subtype1 107 58.7 (12.8)
subtype2 217 61.4 (12.1)
subtype3 194 61.0 (11.7)

Figure S62.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S70.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 256 57 125 81
subtype1 66 12 19 10
subtype2 129 23 41 25
subtype3 61 22 65 46

Figure S63.  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 = 0.0012

Table S71.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 262 69 177 11
subtype1 67 13 24 3
subtype2 129 27 60 2
subtype3 66 29 93 6

Figure S64.  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 = 0.0125 (Fisher's exact test), Q value = 0.94

Table S72.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 237 17
subtype1 48 3
subtype2 100 2
subtype3 89 12

Figure S65.  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 = 0.00169 (Fisher's exact test), Q value = 0.15

Table S73.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 421 79 18
subtype1 94 9 4
subtype2 185 24 8
subtype3 142 46 6

Figure S66.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 2e-05 (Fisher's exact test), Q value = 0.0021

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

nPatients FEMALE MALE
ALL 184 335
subtype1 36 71
subtype2 101 117
subtype3 47 147

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

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.554 (Kruskal-Wallis (anova)), Q value = 1

Table S75.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 40 90.8 (16.5)
subtype1 8 95.0 (7.6)
subtype2 18 91.7 (7.9)
subtype3 14 87.1 (26.1)

Figure S68.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.541 (Kruskal-Wallis (anova)), Q value = 1

Table S76.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 10 30.2 (16.1)
subtype1 2 40.0 (14.1)
subtype2 3 31.7 (19.9)
subtype3 5 25.4 (16.0)

Figure S69.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.033 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 42 462
subtype1 4 15 87
subtype2 2 14 199
subtype3 2 13 176

Figure S70.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.122 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 26 342
subtype1 3 75
subtype2 16 137
subtype3 7 130

Figure S71.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S79.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 162 232 125
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.019 (logrank test), Q value = 1

Table S80.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 373 23 3.0 - 3668.0 (1307.0)
subtype1 126 7 13.0 - 3431.0 (1154.0)
subtype2 183 7 3.0 - 3668.0 (1291.0)
subtype3 64 9 7.0 - 3343.0 (1425.5)

Figure S72.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.162 (Kruskal-Wallis (anova)), Q value = 1

Table S81.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 518 60.7 (12.1)
subtype1 162 59.3 (13.1)
subtype2 231 60.9 (11.9)
subtype3 125 62.0 (11.2)

Figure S73.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S82.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 256 57 125 81
subtype1 92 19 35 16
subtype2 131 26 44 31
subtype3 33 12 46 34

Figure S74.  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 = 0.0012

Table S83.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 262 69 177 11
subtype1 96 22 42 2
subtype2 132 33 65 2
subtype3 34 14 70 7

Figure S75.  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 = 0.00114 (Fisher's exact test), Q value = 0.1

Table S84.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 237 17
subtype1 72 6
subtype2 103 1
subtype3 62 10

Figure S76.  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 = 0.00187 (Fisher's exact test), Q value = 0.16

Table S85.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 421 79 18
subtype1 138 16 8
subtype2 193 30 8
subtype3 90 33 2

Figure S77.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00012 (Fisher's exact test), Q value = 0.012

Table S86.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 184 335
subtype1 50 112
subtype2 104 128
subtype3 30 95

Figure S78.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.198 (Kruskal-Wallis (anova)), Q value = 1

Table S87.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 40 90.8 (16.5)
subtype1 13 94.6 (9.7)
subtype2 18 92.8 (5.7)
subtype3 9 81.1 (31.4)

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

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.88 (Kruskal-Wallis (anova)), Q value = 1

Table S88.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 10 30.2 (16.1)
subtype1 5 31.4 (16.0)
subtype2 3 31.7 (19.9)
subtype3 2 25.0 (21.2)

Figure S80.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0333 (Fisher's exact test), Q value = 1

Table S89.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 42 462
subtype1 4 21 136
subtype2 2 16 208
subtype3 2 5 118

Figure S81.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.756 (Fisher's exact test), Q value = 1

Table S90.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 26 342
subtype1 8 113
subtype2 13 144
subtype3 5 85

Figure S82.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S91.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 121 201 182
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.042 (logrank test), Q value = 1

Table S92.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 360 23 3.0 - 3668.0 (1307.5)
subtype1 93 4 3.0 - 3431.0 (1459.0)
subtype2 158 7 7.0 - 3668.0 (1318.0)
subtype3 109 12 7.0 - 2859.0 (1143.0)

Figure S83.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0281 (Kruskal-Wallis (anova)), Q value = 1

Table S93.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 504 60.6 (12.1)
subtype1 121 58.2 (12.2)
subtype2 201 62.2 (12.2)
subtype3 182 60.3 (11.6)

Figure S84.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00038 (Fisher's exact test), Q value = 0.036

Table S94.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 244 55 125 80
subtype1 72 11 28 10
subtype2 108 20 45 28
subtype3 64 24 52 42

Figure S85.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.00078 (Fisher's exact test), Q value = 0.072

Table S95.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 249 67 177 11
subtype1 73 11 33 4
subtype2 109 26 64 2
subtype3 67 30 80 5

Figure S86.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.0119 (Fisher's exact test), Q value = 0.91

Table S96.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 225 18
subtype1 55 3
subtype2 87 2
subtype3 83 13

Figure S87.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.00335 (Fisher's exact test), Q value = 0.28

Table S97.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 405 78 19
subtype1 104 9 6
subtype2 168 27 6
subtype3 133 42 7

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00247 (Fisher's exact test), Q value = 0.21

Table S98.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 178 326
subtype1 43 78
subtype2 87 114
subtype3 48 134

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.777 (Kruskal-Wallis (anova)), Q value = 1

Table S99.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 41 88.5 (21.6)
subtype1 11 93.6 (8.1)
subtype2 19 92.1 (7.9)
subtype3 11 77.3 (38.8)

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.591 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 12 27.2 (16.2)
subtype1 4 18.2 (14.7)
subtype2 2 43.0 (4.2)
subtype3 6 27.8 (16.7)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.299 (Fisher's exact test), Q value = 1

Table S101.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 44 445
subtype1 1 15 104
subtype2 3 12 182
subtype3 4 17 159

Figure S92.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.224 (Fisher's exact test), Q value = 1

Table S102.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 333
subtype1 2 78
subtype2 12 130
subtype3 10 125

Figure S93.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S103.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 142 134 191 37
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.104 (logrank test), Q value = 1

Table S104.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 360 23 3.0 - 3668.0 (1307.5)
subtype1 99 7 15.0 - 3431.0 (1385.0)
subtype2 95 10 7.0 - 2859.0 (1107.0)
subtype3 152 5 3.0 - 3668.0 (1372.0)
subtype4 14 1 665.0 - 2718.0 (1740.5)

Figure S94.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0704 (Kruskal-Wallis (anova)), Q value = 1

Table S105.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 504 60.6 (12.1)
subtype1 142 59.1 (12.5)
subtype2 134 59.5 (11.9)
subtype3 191 61.7 (11.9)
subtype4 37 64.2 (11.0)

Figure S95.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.0021

Table S106.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 244 55 125 80
subtype1 79 10 36 17
subtype2 49 17 35 33
subtype3 107 24 42 18
subtype4 9 4 12 12

Figure S96.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 8e-05 (Fisher's exact test), Q value = 0.0079

Table S107.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 249 67 177 11
subtype1 80 11 46 5
subtype2 51 23 57 3
subtype3 109 28 53 1
subtype4 9 5 21 2

Figure S97.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.0194 (Fisher's exact test), Q value = 1

Table S108.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 225 18
subtype1 60 8
subtype2 65 8
subtype3 84 1
subtype4 16 1

Figure S98.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.00027 (Fisher's exact test), Q value = 0.026

Table S109.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 405 78 19
subtype1 118 15 7
subtype2 98 32 4
subtype3 165 18 8
subtype4 24 13 0

Figure S99.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00137 (Fisher's exact test), Q value = 0.12

Table S110.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 178 326
subtype1 54 88
subtype2 36 98
subtype3 82 109
subtype4 6 31

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.156 (Kruskal-Wallis (anova)), Q value = 1

Table S111.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 41 88.5 (21.6)
subtype1 14 87.9 (26.1)
subtype2 10 79.0 (29.6)
subtype3 16 94.4 (5.1)
subtype4 1 100.0 (NA)

Figure S101.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.884 (Kruskal-Wallis (anova)), Q value = 1

Table S112.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 12 27.2 (16.2)
subtype1 4 23.5 (14.0)
subtype2 5 27.4 (18.7)
subtype3 3 31.7 (19.9)

Figure S102.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.14 (Fisher's exact test), Q value = 1

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 8 44 445
subtype1 4 18 120
subtype2 2 12 117
subtype3 2 14 171
subtype4 0 0 37

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.0181 (Fisher's exact test), Q value = 1

Table S114.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 333
subtype1 2 91
subtype2 4 96
subtype3 14 120
subtype4 4 26

Figure S104.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S115.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 30 22 49 35
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.633 (logrank test), Q value = 1

Table S116.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 99 6 3.0 - 3668.0 (1168.0)
subtype1 22 1 3.0 - 2353.0 (599.0)
subtype2 18 0 24.0 - 3431.0 (1265.0)
subtype3 41 4 25.0 - 3668.0 (1398.0)
subtype4 18 1 15.0 - 2660.0 (860.0)

Figure S105.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.68 (Kruskal-Wallis (anova)), Q value = 1

Table S117.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 136 60.6 (11.6)
subtype1 30 60.3 (10.4)
subtype2 22 58.0 (12.0)
subtype3 49 61.7 (12.3)
subtype4 35 61.2 (11.3)

Figure S106.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.343 (Fisher's exact test), Q value = 1

Table S118.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 68 17 25 26
subtype1 17 5 3 5
subtype2 15 1 4 2
subtype3 22 7 12 8
subtype4 14 4 6 11

Figure S107.  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 = 0.116 (Fisher's exact test), Q value = 1

Table S119.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 70 19 42 5
subtype1 17 6 6 1
subtype2 16 1 4 1
subtype3 22 8 19 0
subtype4 15 4 13 3

Figure S108.  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 = 0.172 (Fisher's exact test), Q value = 1

Table S120.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 55 5
subtype1 6 1
subtype2 9 1
subtype3 23 0
subtype4 17 3

Figure S109.  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 = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S121.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 97 23 14
subtype1 13 4 12
subtype2 20 1 0
subtype3 41 8 0
subtype4 23 10 2

Figure S110.  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 = 0.0251 (Fisher's exact test), Q value = 1

Table S122.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 47 89
subtype1 15 15
subtype2 6 16
subtype3 20 29
subtype4 6 29

Figure S111.  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 = 0.994 (Kruskal-Wallis (anova)), Q value = 1

Table S123.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 18 92.8 (8.3)
subtype1 5 94.0 (5.5)
subtype3 9 92.2 (9.7)
subtype4 4 92.5 (9.6)

Figure S112.  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 = 0.0012

Table S124.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 22 108
subtype1 0 16 14
subtype2 1 2 18
subtype3 0 0 48
subtype4 2 4 28

Figure S113.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.454 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 96
subtype1 0 24
subtype2 1 13
subtype3 4 37
subtype4 1 22

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

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S126.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 32 56 48
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.43 (logrank test), Q value = 1

Table S127.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 99 6 3.0 - 3668.0 (1168.0)
subtype1 24 1 3.0 - 2353.0 (723.5)
subtype2 36 1 15.0 - 3431.0 (1328.5)
subtype3 39 4 25.0 - 3668.0 (1371.0)

Figure S115.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0614 (Kruskal-Wallis (anova)), Q value = 1

Table S128.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 136 60.6 (11.6)
subtype1 32 60.7 (10.2)
subtype2 56 58.1 (11.7)
subtype3 48 63.6 (11.8)

Figure S116.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.342 (Fisher's exact test), Q value = 1

Table S129.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 68 17 25 26
subtype1 20 4 3 5
subtype2 27 4 12 13
subtype3 21 9 10 8

Figure S117.  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 = 0.0441 (Fisher's exact test), Q value = 1

Table S130.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 70 19 42 5
subtype1 21 5 5 1
subtype2 27 4 21 4
subtype3 22 10 16 0

Figure S118.  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 = 0.16 (Fisher's exact test), Q value = 1

Table S131.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 55 5
subtype1 7 1
subtype2 26 4
subtype3 22 0

Figure S119.  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 = 6e-05 (Fisher's exact test), Q value = 0.0061

Table S132.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 97 23 14
subtype1 16 4 11
subtype2 41 11 3
subtype3 40 8 0

Figure S120.  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 = 0.00216 (Fisher's exact test), Q value = 0.19

Table S133.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 47 89
subtype1 15 17
subtype2 10 46
subtype3 22 26

Figure S121.  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 = 0.843 (Kruskal-Wallis (anova)), Q value = 1

Table S134.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 18 92.8 (8.3)
subtype1 5 94.0 (5.5)
subtype2 5 94.0 (8.9)
subtype3 8 91.2 (9.9)

Figure S122.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S135.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 22 108
subtype1 0 15 17
subtype2 3 6 46
subtype3 0 1 45

Figure S123.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.387 (Fisher's exact test), Q value = 1

Table S136.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 6 96
subtype1 0 26
subtype2 3 35
subtype3 3 35

Figure S124.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Methods & Data
Input

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

  • Clinical data file = KIRC-TP.merged_data.txt

  • Number of patients = 525

  • Number of clustering approaches = 12

  • Number of selected clinical features = 11

  • 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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