Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1F76BD9
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/C1F76BD9
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 11 clinical features across 223 patients, 28 significant findings detected with P value < 0.05 and Q value < 0.25.

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

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'AGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 8 subtypes that correlate to 'Time to Death',  'AGE',  'NEOPLASM.DISEASESTAGE', and 'PATHOLOGY.T.STAGE'.

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

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE',  'PATHOLOGY.N.STAGE', and 'GENDER'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.

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

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE' and 'PATHOLOGY.N.STAGE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'NEOPLASM.DISEASESTAGE', and 'PATHOLOGY.T.STAGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 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, 28 significant findings detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		RPPA
CNMF
subtypes 		RPPA
cHierClus
subtypes 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.0138
(1.00) 		0.00117
(0.0998) 		0.32
(1.00) 		2.12e-07
(2.33e-05) 		0.0367
(1.00) 		0.238
(1.00) 		0.0277
(1.00) 		0.333
(1.00) 		0.00608
(0.486) 		0.0005
(0.045)
AGE Kruskal-Wallis (anova) 0.035
(1.00) 		0.0724
(1.00) 		0.000472
(0.0429) 		2.26e-06
(0.000246) 		0.115
(1.00) 		0.0127
(0.95) 		0.418
(1.00) 		0.216
(1.00) 		0.0193
(1.00) 		0.00953
(0.743)
NEOPLASM DISEASESTAGE Fisher's exact test 1e-05
(0.00108) 		1e-05
(0.00108) 		0.756
(1.00) 		1e-05
(0.00108) 		1e-05
(0.00108) 		1e-05
(0.00108) 		0.0267
(1.00) 		0.00055
(0.0484) 		0.00053
(0.0472) 		1e-05
(0.00108)
PATHOLOGY T STAGE Fisher's exact test 1e-05
(0.00108) 		1e-05
(0.00108) 		0.123
(1.00) 		2e-05
(0.00192) 		1e-05
(0.00108) 		1e-05
(0.00108) 		0.123
(1.00) 		0.0007
(0.0609) 		0.00324
(0.266) 		1e-05
(0.00108)
PATHOLOGY N STAGE Fisher's exact test 0.0319
(1.00) 		0.00034
(0.0313) 		0.408
(1.00) 		0.0876
(1.00) 		0.0001
(0.0095) 		0.00013
(0.0122) 		0.0232
(1.00) 		0.0122
(0.938) 		0.00208
(0.173) 		0.00376
(0.305)
PATHOLOGY M STAGE Fisher's exact test 0.0502
(1.00) 		0.00078
(0.0671) 		0.618
(1.00) 		0.192
(1.00) 		0.0122
(0.938) 		0.0322
(1.00) 		0.425
(1.00) 		0.203
(1.00) 		0.0171
(1.00) 		0.0137
(1.00)
GENDER Fisher's exact test 0.00187
(0.157) 		0.0149
(1.00) 		0.805
(1.00) 		0.0151
(1.00) 		1e-05
(0.00108) 		0.00017
(0.0158) 		0.39
(1.00) 		0.0181
(1.00) 		0.0179
(1.00) 		0.0191
(1.00)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.251
(1.00) 		0.0195
(1.00) 		0.0793
(1.00) 		0.365
(1.00) 		0.0265
(1.00) 		0.0691
(1.00) 		0.141
(1.00) 		0.622
(1.00) 		0.13
(1.00) 		0.0538
(1.00)
NUMBERPACKYEARSSMOKED Kruskal-Wallis (anova) 0.668
(1.00) 		0.557
(1.00) 		0.475
(1.00) 		0.883
(1.00) 		0.231
(1.00) 		0.0337
(1.00) 		0.386
(1.00) 		0.913
(1.00) 		0.426
(1.00) 		0.297
(1.00)
RACE Fisher's exact test 0.0914
(1.00) 		0.412
(1.00) 		0.522
(1.00) 		0.909
(1.00) 		0.128
(1.00) 		0.955
(1.00) 		0.294
(1.00) 		0.00631
(0.498) 		0.987
(1.00) 		0.685
(1.00)
ETHNICITY Fisher's exact test 0.644
(1.00) 		0.548
(1.00) 		0.444
(1.00) 		0.262
(1.00) 		0.623
(1.00) 		0.222
(1.00) 		0.834
(1.00) 		0.246
(1.00) 		0.633
(1.00) 		0.136
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 87 52 37 45
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0138 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 194 7 2.0 - 5925.0 (540.5)
subtype1 72 1 2.0 - 3950.0 (578.5)
subtype2 50 1 4.0 - 2653.0 (654.0)
subtype3 33 0 2.0 - 5925.0 (540.0)
subtype4 39 5 2.0 - 2584.0 (270.0)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.035 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 216 60.4 (12.2)
subtype1 84 63.2 (11.5)
subtype2 51 57.5 (10.6)
subtype3 37 60.2 (11.8)
subtype4 44 59.0 (14.7)

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 137 16 43 13
subtype1 55 9 20 2
subtype2 38 4 5 0
subtype3 30 1 1 2
subtype4 14 2 17 9

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

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

nPatients T1 T2 T3+T4
ALL 146 22 51
subtype1 59 7 19
subtype2 40 7 5
subtype3 31 3 3
subtype4 16 5 24

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

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

P value = 0.0319 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2
ALL 39 20 4
subtype1 17 8 1
subtype2 7 0 0
subtype3 7 1 0
subtype4 8 11 3

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0502 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 88 8 112
subtype1 39 2 45
subtype2 21 0 27
subtype3 14 0 19
subtype4 14 6 21

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00187 (Fisher's exact test), Q value = 0.16

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

nPatients FEMALE MALE
ALL 63 158
subtype1 28 59
subtype2 8 44
subtype3 6 31
subtype4 21 24

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.251 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 56 91.6 (16.0)
subtype1 13 90.8 (16.1)
subtype2 17 94.1 (8.7)
subtype3 11 96.4 (6.7)
subtype4 15 86.0 (24.7)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.668 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 35 34.0 (34.5)
subtype1 12 39.3 (48.9)
subtype2 8 22.6 (15.0)
subtype3 6 32.7 (18.8)
subtype4 9 37.9 (34.2)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0914 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 52 147
subtype1 0 1 21 61
subtype2 1 1 17 29
subtype3 1 0 5 29
subtype4 0 3 9 28

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.644 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 175
subtype1 5 64
subtype2 1 45
subtype3 2 30
subtype4 1 36

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S13.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 54 55 97
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00117 (logrank test), Q value = 0.1

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 180 6 2.0 - 5925.0 (510.5)
subtype1 44 0 4.0 - 2816.0 (472.5)
subtype2 46 6 5.0 - 5925.0 (396.0)
subtype3 90 0 2.0 - 3950.0 (543.5)

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0724 (Kruskal-Wallis (anova)), Q value = 1

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 202 60.8 (12.3)
subtype1 52 63.5 (12.1)
subtype2 54 61.2 (14.7)
subtype3 96 59.1 (10.7)

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 133 13 42 12
subtype1 38 6 6 2
subtype2 15 3 28 9
subtype3 80 4 8 1

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 140 15 49
subtype1 40 3 9
subtype2 17 5 33
subtype3 83 7 7

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.00034 (Fisher's exact test), Q value = 0.031

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 37 19 4
subtype1 10 2 1
subtype2 13 17 3
subtype3 14 0 0

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.00078 (Fisher's exact test), Q value = 0.067

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 79 7 112
subtype1 27 1 23
subtype2 22 6 26
subtype3 30 0 63

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0149 (Fisher's exact test), Q value = 1

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 59 147
subtype1 14 40
subtype2 24 31
subtype3 21 76

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

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

P value = 0.0195 (Kruskal-Wallis (anova)), Q value = 1

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 56 93.4 (10.1)
subtype1 11 85.5 (16.3)
subtype2 9 95.6 (5.3)
subtype3 36 95.3 (7.4)

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.557 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 37 32.7 (34.0)
subtype1 9 46.1 (55.2)
subtype2 9 34.6 (34.9)
subtype3 19 25.4 (16.5)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.412 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 45 139
subtype1 1 3 14 32
subtype2 0 1 13 38
subtype3 1 1 18 69

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.548 (Fisher's exact test), Q value = 1

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 160
subtype1 1 42
subtype2 3 38
subtype3 5 80

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

Clustering Approach #3: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 67 62 39
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.32 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 149 5 2.0 - 5925.0 (460.0)
subtype1 59 3 2.0 - 3035.0 (445.0)
subtype2 57 2 2.0 - 5925.0 (460.0)
subtype3 33 0 32.0 - 3760.0 (493.0)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.000472 (Kruskal-Wallis (anova)), Q value = 0.043

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

nPatients Mean (Std.Dev)
ALL 164 60.1 (12.1)
subtype1 66 58.8 (12.2)
subtype2 60 57.3 (11.8)
subtype3 38 66.7 (10.2)

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

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 102 10 39 12
subtype1 39 5 16 4
subtype2 41 4 11 5
subtype3 22 1 12 3

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.123 (Fisher's exact test), Q value = 1

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

nPatients T1 T2 T3+T4
ALL 107 15 46
subtype1 43 5 19
subtype2 41 9 12
subtype3 23 1 15

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.408 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2
ALL 36 18 4
subtype1 17 7 2
subtype2 13 8 0
subtype3 6 3 2

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.618 (Fisher's exact test), Q value = 1

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 67 7 87
subtype1 24 2 37
subtype2 24 3 34
subtype3 19 2 16

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.805 (Fisher's exact test), Q value = 1

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 119
subtype1 18 49
subtype2 20 42
subtype3 11 28

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0793 (Kruskal-Wallis (anova)), Q value = 1

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 41 95.6 (5.5)
subtype1 14 93.6 (6.3)
subtype2 22 95.9 (5.0)
subtype3 5 100.0 (0.0)

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.475 (Kruskal-Wallis (anova)), Q value = 1

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 28 32.0 (33.7)
subtype1 13 25.8 (16.2)
subtype2 10 32.1 (15.1)
subtype3 5 47.6 (77.0)

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.522 (Fisher's exact test), Q value = 1

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 5 37 112
subtype1 0 3 16 40
subtype2 0 2 12 46
subtype3 1 0 9 26

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.444 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 132
subtype1 1 50
subtype2 1 54
subtype3 2 28

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S37.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7 8
Number of samples 14 17 21 19 23 32 19 23
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 2.12e-07 (logrank test), Q value = 2.3e-05

Table S38.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 149 5 2.0 - 5925.0 (460.0)
subtype1 13 0 3.0 - 2653.0 (364.0)
subtype2 16 0 62.0 - 1856.0 (546.0)
subtype3 19 0 27.0 - 3950.0 (649.0)
subtype4 17 0 2.0 - 3035.0 (921.0)
subtype5 18 5 4.0 - 1973.0 (317.0)
subtype6 28 0 32.0 - 3760.0 (544.0)
subtype7 19 0 5.0 - 5925.0 (419.0)
subtype8 19 0 2.0 - 2816.0 (578.0)

Figure S34.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 2.26e-06 (Kruskal-Wallis (anova)), Q value = 0.00025

Table S39.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 164 60.1 (12.1)
subtype1 13 67.4 (11.8)
subtype2 16 58.6 (9.8)
subtype3 20 52.9 (7.5)
subtype4 18 60.6 (11.1)
subtype5 23 51.6 (13.3)
subtype6 32 68.3 (9.6)
subtype7 19 60.5 (10.5)
subtype8 23 59.5 (12.5)

Figure S35.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S40.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 102 10 39 12
subtype1 13 1 0 0
subtype2 14 0 2 0
subtype3 19 0 1 0
subtype4 8 0 10 1
subtype5 7 1 7 8
subtype6 20 2 9 0
subtype7 13 3 1 2
subtype8 8 3 9 1

Figure S36.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.0019

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 107 15 46
subtype1 13 1 0
subtype2 15 0 2
subtype3 19 2 0
subtype4 9 1 9
subtype5 7 2 14
subtype6 21 2 9
subtype7 13 4 2
subtype8 10 3 10

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0876 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 36 18 4
subtype1 2 0 0
subtype2 4 1 0
subtype3 3 0 0
subtype4 8 3 0
subtype5 4 10 3
subtype6 5 1 1
subtype7 2 1 0
subtype8 8 2 0

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.192 (Fisher's exact test), Q value = 1

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 67 7 87
subtype1 5 0 8
subtype2 5 0 10
subtype3 9 0 12
subtype4 10 1 8
subtype5 8 5 10
subtype6 14 0 16
subtype7 5 0 13
subtype8 11 1 10

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0151 (Fisher's exact test), Q value = 1

Table S44.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 119
subtype1 3 11
subtype2 4 13
subtype3 5 16
subtype4 8 11
subtype5 14 9
subtype6 6 26
subtype7 6 13
subtype8 3 20

Figure S40.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.365 (Kruskal-Wallis (anova)), Q value = 1

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 41 95.6 (5.5)
subtype1 2 90.0 (0.0)
subtype2 6 93.3 (5.2)
subtype3 11 96.4 (5.0)
subtype4 4 92.5 (5.0)
subtype5 6 95.0 (8.4)
subtype6 3 100.0 (0.0)
subtype7 5 98.0 (4.5)
subtype8 4 97.5 (5.0)

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.883 (Kruskal-Wallis (anova)), Q value = 1

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 28 32.0 (33.7)
subtype1 2 24.5 (0.7)
subtype2 3 19.7 (12.7)
subtype3 2 25.0 (21.2)
subtype4 4 24.5 (17.5)
subtype5 4 33.2 (15.1)
subtype6 4 56.2 (86.1)
subtype7 3 36.3 (20.3)
subtype8 6 28.7 (18.9)

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.909 (Fisher's exact test), Q value = 1

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 5 37 112
subtype1 0 0 3 8
subtype2 0 0 5 11
subtype3 0 0 4 16
subtype4 0 1 4 13
subtype5 0 2 6 13
subtype6 1 0 8 22
subtype7 0 0 4 14
subtype8 0 2 3 15

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.262 (Fisher's exact test), Q value = 1

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 132
subtype1 0 11
subtype2 0 14
subtype3 0 17
subtype4 2 14
subtype5 1 17
subtype6 0 25
subtype7 0 17
subtype8 1 17

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S49.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 55 90 77
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0367 (logrank test), Q value = 1

Table S50.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 195 7 2.0 - 5925.0 (541.0)
subtype1 49 0 2.0 - 3035.0 (597.0)
subtype2 83 1 2.0 - 3950.0 (578.0)
subtype3 63 6 4.0 - 5925.0 (403.0)

Figure S45.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.115 (Kruskal-Wallis (anova)), Q value = 1

Table S51.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 217 60.5 (12.2)
subtype1 53 63.4 (11.7)
subtype2 89 58.9 (11.0)
subtype3 75 60.3 (13.5)

Figure S46.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S52.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 138 16 43 13
subtype1 37 7 7 1
subtype2 73 4 6 1
subtype3 28 5 30 11

Figure S47.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S53.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 147 22 51
subtype1 40 6 7
subtype2 75 10 5
subtype3 32 6 39

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 1e-04 (Fisher's exact test), Q value = 0.0095

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 39 20 4
subtype1 13 1 1
subtype2 12 0 0
subtype3 14 19 3

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0122 (Fisher's exact test), Q value = 0.94

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 89 8 112
subtype1 22 1 29
subtype2 32 0 51
subtype3 35 7 32

Figure S50.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 63 159
subtype1 7 48
subtype2 19 71
subtype3 37 40

Figure S51.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0265 (Kruskal-Wallis (anova)), Q value = 1

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 57 91.8 (15.9)
subtype1 14 92.1 (8.0)
subtype2 32 95.9 (6.7)
subtype3 11 79.1 (31.1)

Figure S52.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.231 (Kruskal-Wallis (anova)), Q value = 1

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 36 33.4 (34.2)
subtype1 10 23.3 (19.1)
subtype2 16 27.1 (16.6)
subtype3 10 53.7 (55.5)

Figure S53.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'RACE'

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

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 52 148
subtype1 1 0 9 42
subtype2 1 1 26 55
subtype3 0 4 17 51

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.623 (Fisher's exact test), Q value = 1

Table S60.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 176
subtype1 1 46
subtype2 5 75
subtype3 3 55

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

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S61.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 94 59 33 36
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.238 (logrank test), Q value = 1

Table S62.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 195 7 2.0 - 5925.0 (541.0)
subtype1 83 3 4.0 - 5925.0 (554.0)
subtype2 54 1 2.0 - 3950.0 (559.0)
subtype3 32 0 2.0 - 1965.0 (654.0)
subtype4 26 3 7.0 - 2816.0 (392.0)

Figure S56.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0127 (Kruskal-Wallis (anova)), Q value = 0.95

Table S63.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 217 60.5 (12.2)
subtype1 91 63.4 (12.2)
subtype2 58 59.7 (10.8)
subtype3 33 55.6 (9.7)
subtype4 35 58.7 (14.7)

Figure S57.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S64.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 138 16 43 13
subtype1 49 7 30 4
subtype2 46 5 4 1
subtype3 27 1 2 0
subtype4 16 3 7 8

Figure S58.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S65.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 147 22 51
subtype1 52 8 32
subtype2 48 8 3
subtype3 29 2 2
subtype4 18 4 14

Figure S59.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00013 (Fisher's exact test), Q value = 0.012

Table S66.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 39 20 4
subtype1 24 11 1
subtype2 10 0 0
subtype3 3 0 0
subtype4 2 9 3

Figure S60.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0322 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 89 8 112
subtype1 39 3 46
subtype2 19 0 35
subtype3 15 0 17
subtype4 16 5 14

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00017 (Fisher's exact test), Q value = 0.016

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 63 159
subtype1 25 69
subtype2 7 52
subtype3 11 22
subtype4 20 16

Figure S62.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0691 (Kruskal-Wallis (anova)), Q value = 1

Table S69.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 57 91.8 (15.9)
subtype1 20 87.5 (21.7)
subtype2 20 95.0 (8.3)
subtype3 13 96.9 (4.8)
subtype4 4 80.0 (27.1)

Figure S63.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0337 (Kruskal-Wallis (anova)), Q value = 1

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 36 33.4 (34.2)
subtype1 17 38.3 (47.1)
subtype2 7 40.1 (14.2)
subtype3 7 16.3 (11.2)
subtype4 5 31.6 (13.1)

Figure S64.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.955 (Fisher's exact test), Q value = 1

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 52 148
subtype1 1 2 22 66
subtype2 1 1 15 38
subtype3 0 0 7 22
subtype4 0 2 8 22

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.222 (Fisher's exact test), Q value = 1

Table S72.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 176
subtype1 2 76
subtype2 2 50
subtype3 2 27
subtype4 3 23

Figure S66.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S73.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 66 81 76
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0277 (logrank test), Q value = 1

Table S74.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 196 7 2.0 - 5925.0 (540.5)
subtype1 58 0 2.0 - 3035.0 (412.0)
subtype2 75 1 3.0 - 3760.0 (546.0)
subtype3 63 6 2.0 - 5925.0 (612.0)

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.418 (Kruskal-Wallis (anova)), Q value = 1

Table S75.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 218 60.6 (12.2)
subtype1 65 62.2 (11.9)
subtype2 78 60.8 (10.7)
subtype3 75 58.9 (13.9)

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0267 (Fisher's exact test), Q value = 1

Table S76.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 139 16 43 13
subtype1 37 8 17 1
subtype2 57 3 11 3
subtype3 45 5 15 9

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.123 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 148 22 51
subtype1 39 10 16
subtype2 61 7 13
subtype3 48 5 22

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0232 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 39 20 4
subtype1 18 4 1
subtype2 11 3 0
subtype3 10 13 3

Figure S71.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.425 (Fisher's exact test), Q value = 1

Table S79.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 89 8 113
subtype1 28 1 32
subtype2 27 2 44
subtype3 34 5 37

Figure S72.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.39 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 64 159
subtype1 15 51
subtype2 24 57
subtype3 25 51

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

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

P value = 0.141 (Kruskal-Wallis (anova)), Q value = 1

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 57 91.8 (15.9)
subtype1 12 91.7 (8.3)
subtype2 22 95.9 (7.3)
subtype3 23 87.8 (23.0)

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.386 (Kruskal-Wallis (anova)), Q value = 1

Table S82.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 37 32.7 (34.0)
subtype1 12 31.1 (32.8)
subtype2 15 41.5 (43.2)
subtype3 10 21.3 (11.6)

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.294 (Fisher's exact test), Q value = 1

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 52 149
subtype1 0 1 22 40
subtype2 1 2 13 58
subtype3 1 2 17 51

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.834 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 177
subtype1 2 55
subtype2 3 64
subtype3 4 58

Figure S77.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S85.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 41 46 66 70
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.333 (logrank test), Q value = 1

Table S86.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 196 7 2.0 - 5925.0 (540.5)
subtype1 34 2 2.0 - 2584.0 (587.0)
subtype2 41 0 14.0 - 3760.0 (510.0)
subtype3 63 1 2.0 - 3035.0 (511.0)
subtype4 58 4 4.0 - 5925.0 (654.0)

Figure S78.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.216 (Kruskal-Wallis (anova)), Q value = 1

Table S87.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 218 60.6 (12.2)
subtype1 40 64.0 (12.4)
subtype2 45 60.6 (12.0)
subtype3 65 61.1 (10.9)
subtype4 68 58.0 (13.2)

Figure S79.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00055 (Fisher's exact test), Q value = 0.048

Table S88.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 139 16 43 13
subtype1 18 7 13 2
subtype2 32 1 6 1
subtype3 51 4 7 1
subtype4 38 4 17 9

Figure S80.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 7e-04 (Fisher's exact test), Q value = 0.061

Table S89.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 148 22 51
subtype1 20 5 14
subtype2 34 5 7
subtype3 54 7 5
subtype4 40 5 25

Figure S81.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0122 (Fisher's exact test), Q value = 0.94

Table S90.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 39 20 4
subtype1 11 6 1
subtype2 7 0 0
subtype3 11 1 0
subtype4 10 13 3

Figure S82.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.203 (Fisher's exact test), Q value = 1

Table S91.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 89 8 113
subtype1 21 2 17
subtype2 16 1 23
subtype3 23 0 38
subtype4 29 5 35

Figure S83.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S92.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 64 159
subtype1 6 35
subtype2 13 33
subtype3 16 50
subtype4 29 41

Figure S84.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.622 (Kruskal-Wallis (anova)), Q value = 1

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 57 91.8 (15.9)
subtype1 6 78.3 (39.2)
subtype2 15 94.7 (6.4)
subtype3 19 94.7 (8.4)
subtype4 17 90.6 (14.3)

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.913 (Kruskal-Wallis (anova)), Q value = 1

Table S94.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 37 32.7 (34.0)
subtype1 8 38.5 (37.4)
subtype2 7 26.4 (17.0)
subtype3 11 29.5 (19.5)
subtype4 11 35.6 (50.6)

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.00631 (Fisher's exact test), Q value = 0.5

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 52 149
subtype1 0 1 8 31
subtype2 1 0 20 21
subtype3 1 1 9 49
subtype4 0 3 15 48

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.246 (Fisher's exact test), Q value = 1

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 177
subtype1 0 36
subtype2 2 35
subtype3 2 55
subtype4 5 51

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S97.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 51 62 58
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00608 (logrank test), Q value = 0.49

Table S98.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 147 6 2.0 - 5925.0 (513.0)
subtype1 45 0 2.0 - 2948.0 (452.0)
subtype2 58 0 5.0 - 3760.0 (543.5)
subtype3 44 6 4.0 - 5925.0 (539.5)

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0193 (Kruskal-Wallis (anova)), Q value = 1

Table S99.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 167 60.0 (12.3)
subtype1 48 62.0 (10.7)
subtype2 62 56.8 (10.9)
subtype3 57 61.9 (14.4)

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.00053 (Fisher's exact test), Q value = 0.047

Table S100.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 104 12 36 13
subtype1 30 4 12 1
subtype2 48 2 9 1
subtype3 26 6 15 11

Figure S91.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00324 (Fisher's exact test), Q value = 0.27

Table S101.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 110 14 45
subtype1 32 6 13
subtype2 50 4 8
subtype3 28 4 24

Figure S92.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00208 (Fisher's exact test), Q value = 0.17

Table S102.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 33 20 4
subtype1 15 2 1
subtype2 10 3 0
subtype3 8 15 3

Figure S93.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0171 (Fisher's exact test), Q value = 1

Table S103.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 71 8 85
subtype1 23 1 23
subtype2 23 0 37
subtype3 25 7 25

Figure S94.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0179 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 52 119
subtype1 15 36
subtype2 12 50
subtype3 25 33

Figure S95.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.13 (Kruskal-Wallis (anova)), Q value = 1

Table S105.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 48 91.2 (16.8)
subtype1 10 92.0 (7.9)
subtype2 24 95.8 (5.8)
subtype3 14 82.9 (28.4)

Figure S96.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.426 (Kruskal-Wallis (anova)), Q value = 1

Table S106.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 29 34.6 (37.4)
subtype1 7 60.0 (67.1)
subtype2 14 24.4 (17.1)
subtype3 8 30.1 (17.6)

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.987 (Fisher's exact test), Q value = 1

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 33 122
subtype1 1 1 9 38
subtype2 1 2 12 43
subtype3 0 2 12 41

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.633 (Fisher's exact test), Q value = 1

Table S108.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 134
subtype1 1 41
subtype2 4 50
subtype3 2 43

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S109.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 71 62 13 25
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 5e-04 (logrank test), Q value = 0.045

Table S110.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 147 6 2.0 - 5925.0 (513.0)
subtype1 67 0 2.0 - 3760.0 (554.0)
subtype2 48 5 4.0 - 5925.0 (606.0)
subtype3 7 1 7.0 - 1049.0 (84.0)
subtype4 25 0 5.0 - 2653.0 (399.0)

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.00953 (Kruskal-Wallis (anova)), Q value = 0.74

Table S111.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 167 60.0 (12.3)
subtype1 69 60.0 (10.5)
subtype2 61 63.6 (12.3)
subtype3 12 50.5 (17.8)
subtype4 25 56.1 (11.1)

Figure S101.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S112.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 104 12 36 13
subtype1 47 6 13 1
subtype2 33 3 17 9
subtype3 1 3 6 3
subtype4 23 0 0 0

Figure S102.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0011

Table S113.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 110 14 45
subtype1 50 8 13
subtype2 34 2 24
subtype3 2 3 8
subtype4 24 1 0

Figure S103.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00376 (Fisher's exact test), Q value = 0.3

Table S114.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 33 20 4
subtype1 14 3 0
subtype2 16 10 4
subtype3 1 7 0
subtype4 2 0 0

Figure S104.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0137 (Fisher's exact test), Q value = 1

Table S115.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 71 8 85
subtype1 28 0 38
subtype2 29 5 26
subtype3 5 3 5
subtype4 9 0 16

Figure S105.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0191 (Fisher's exact test), Q value = 1

Table S116.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 52 119
subtype1 17 54
subtype2 19 43
subtype3 9 4
subtype4 7 18

Figure S106.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0538 (Kruskal-Wallis (anova)), Q value = 1

Table S117.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 48 91.2 (16.8)
subtype1 22 95.0 (6.7)
subtype2 17 86.5 (23.2)
subtype3 1 40.0 (NA)
subtype4 8 97.5 (4.6)

Figure S107.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.297 (Kruskal-Wallis (anova)), Q value = 1

Table S118.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 29 34.6 (37.4)
subtype1 11 35.0 (33.3)
subtype2 9 48.1 (54.3)
subtype4 9 20.6 (11.3)

Figure S108.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.685 (Fisher's exact test), Q value = 1

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 33 122
subtype1 2 1 13 52
subtype2 0 3 12 45
subtype3 0 1 3 7
subtype4 0 0 5 18

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

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.136 (Fisher's exact test), Q value = 1

Table S120.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 7 134
subtype1 1 57
subtype2 2 47
subtype3 1 9
subtype4 3 21

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Methods & Data
Input

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

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

  • Number of patients = 223

  • Number of clustering approaches = 10

  • 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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