Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Chromophobe (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1WW7H1D
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 12 clinical features across 66 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

  • 2 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that do not correlate to any clinical features.

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

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

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.

  • 9 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding 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 0.353
(0.887)
0.675
(1.00)
0.518
(0.974)
0.52
(0.974)
0.568
(1.00)
0.115
(0.565)
0.276
(0.789)
0.123
(0.566)
0.821
(1.00)
0.167
(0.608)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.891
(1.00)
0.229
(0.756)
0.824
(1.00)
0.943
(1.00)
0.698
(1.00)
0.566
(1.00)
0.00899
(0.565)
0.355
(0.887)
0.101
(0.565)
0.796
(1.00)
PATHOLOGIC STAGE Fisher's exact test 0.712
(1.00)
0.103
(0.565)
0.478
(0.974)
0.0393
(0.565)
0.0942
(0.565)
0.117
(0.565)
0.296
(0.807)
0.619
(1.00)
0.144
(0.584)
0.0258
(0.565)
PATHOLOGY T STAGE Fisher's exact test 0.949
(1.00)
0.0622
(0.565)
0.265
(0.789)
0.0297
(0.565)
0.0586
(0.565)
0.0783
(0.565)
0.161
(0.608)
0.646
(1.00)
0.0625
(0.565)
0.0215
(0.565)
PATHOLOGY N STAGE Fisher's exact test 1
(1.00)
0.346
(0.887)
1
(1.00)
0.577
(1.00)
0.398
(0.918)
0.162
(0.608)
0.912
(1.00)
0.26
(0.789)
0.424
(0.96)
0.736
(1.00)
PATHOLOGY M STAGE Fisher's exact test 0.511
(0.974)
0.503
(0.974)
0.731
(1.00)
0.714
(1.00)
0.626
(1.00)
0.442
(0.974)
1
(1.00)
0.146
(0.584)
0.68
(1.00)
0.828
(1.00)
GENDER Fisher's exact test 0.605
(1.00)
0.176
(0.62)
0.897
(1.00)
0.795
(1.00)
0.232
(0.756)
0.104
(0.565)
0.698
(1.00)
0.583
(1.00)
0.272
(0.789)
0.146
(0.584)
KARNOFSKY PERFORMANCE SCORE Fisher's exact test 0.491
(0.974)
0.739
(1.00)
0.62
(1.00)
0.62
(1.00)
0.0976
(0.565)
0.0968
(0.565)
0.385
(0.905)
0.136
(0.584)
1
(1.00)
1
(1.00)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.0582
(0.565)
0.52
(0.974)
1
(1.00)
0.831
(1.00)
0.118
(0.565)
0.118
(0.565)
0.289
(0.805)
1
(1.00)
YEAR OF TOBACCO SMOKING ONSET Kruskal-Wallis (anova) 0.377
(0.905)
RACE Fisher's exact test 0.492
(0.974)
0.381
(0.905)
0.506
(0.974)
0.747
(1.00)
0.817
(1.00)
0.506
(0.974)
0.0417
(0.565)
0.233
(0.756)
1
(1.00)
0.762
(1.00)
ETHNICITY Fisher's exact test 1
(1.00)
0.527
(0.974)
0.0551
(0.565)
0.054
(0.565)
0.0107
(0.565)
0.0916
(0.565)
0.647
(1.00)
0.275
(0.789)
0.335
(0.887)
0.861
(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
Number of samples 38 26 2
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.353 (logrank test), Q value = 0.89

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

nPatients nDeath Duration Range (Median), Month
ALL 63 9 1.0 - 153.7 (84.9)
subtype1 37 4 1.0 - 153.7 (86.6)
subtype2 26 5 2.5 - 137.1 (72.7)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.891 (Wilcoxon-test), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 64 51.6 (14.4)
subtype1 38 51.8 (14.8)
subtype2 26 51.3 (14.0)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.712 (Fisher's exact test), Q value = 1

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 20 25 13 6
subtype1 12 14 7 5
subtype2 8 11 6 1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.949 (Fisher's exact test), Q value = 1

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

nPatients T1 T2 T3+T4
ALL 20 25 19
subtype1 12 14 12
subtype2 8 11 7

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 = 1 (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 40 5
subtype1 24 3
subtype2 16 2

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.511 (Fisher's exact test), Q value = 0.97

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

nPatients 0 1
ALL 34 2
subtype1 20 2
subtype2 14 0

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.605 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 26 38
subtype1 14 24
subtype2 12 14

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 = 0.491 (Fisher's exact test), Q value = 0.97

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

nPatients 100 90
ALL 9 2
subtype1 5 2
subtype2 4 0

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0582 (Wilcoxon-test), Q value = 0.56

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

nPatients Mean (Std.Dev)
ALL 11 25.1 (21.9)
subtype1 7 14.6 (13.0)
subtype2 4 43.5 (23.6)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.492 (Fisher's exact test), Q value = 0.97

Table S11.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 3 57
subtype1 2 1 33
subtype2 0 2 24

Figure S10.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

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

Table S12.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 30
subtype1 2 16
subtype2 2 14

Figure S11.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 19 29 18
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.675 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 153.7 (73.9)
subtype1 19 2 23.8 - 137.1 (92.7)
subtype2 28 4 2.5 - 153.7 (65.9)
subtype3 18 3 1.0 - 109.2 (51.7)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.229 (Kruskal-Wallis (anova)), Q value = 0.76

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

nPatients Mean (Std.Dev)
ALL 66 51.5 (14.3)
subtype1 19 49.4 (13.5)
subtype2 29 49.9 (14.2)
subtype3 18 56.3 (14.9)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.103 (Fisher's exact test), Q value = 0.56

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
subtype1 8 8 3 0
subtype2 5 14 6 4
subtype3 8 3 5 2

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0622 (Fisher's exact test), Q value = 0.56

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 21 25 20
subtype1 8 8 3
subtype2 5 14 10
subtype3 8 3 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 = 0.346 (Fisher's exact test), Q value = 0.89

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 40 5
subtype1 12 0
subtype2 18 4
subtype3 10 1

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 = 0.503 (Fisher's exact test), Q value = 0.97

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 34 2
subtype1 13 0
subtype2 13 2
subtype3 8 0

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.176 (Fisher's exact test), Q value = 0.62

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

nPatients FEMALE MALE
ALL 27 39
subtype1 9 10
subtype2 14 15
subtype3 4 14

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.739 (Fisher's exact test), Q value = 1

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients 100 90
ALL 10 3
subtype1 4 1
subtype2 3 0
subtype3 3 2

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

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.52 (Kruskal-Wallis (anova)), Q value = 0.97

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

nPatients Mean (Std.Dev)
ALL 11 25.1 (21.9)
subtype1 3 12.0 (13.1)
subtype2 5 29.6 (28.7)
subtype3 3 30.7 (15.1)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.381 (Fisher's exact test), Q value = 0.9

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 58
subtype1 0 0 18
subtype2 2 3 23
subtype3 0 1 17

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.527 (Fisher's exact test), Q value = 0.97

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 32
subtype1 2 11
subtype2 0 10
subtype3 2 11

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: '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 26 17 20
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.518 (logrank test), Q value = 0.97

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

nPatients nDeath Duration Range (Median), Month
ALL 62 8 1.0 - 153.7 (85.7)
subtype1 26 4 3.5 - 153.7 (79.4)
subtype2 17 3 1.0 - 129.4 (71.4)
subtype3 19 1 19.3 - 137.1 (90.5)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.824 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 63 51.7 (14.3)
subtype1 26 51.2 (12.5)
subtype2 17 53.6 (18.4)
subtype3 20 50.8 (13.2)

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

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.478 (Fisher's exact test), Q value = 0.97

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 19 25 13 6
subtype1 4 12 7 3
subtype2 6 6 3 2
subtype3 9 7 3 1

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.265 (Fisher's exact test), Q value = 0.79

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 19 25 19
subtype1 4 12 10
subtype2 6 6 5
subtype3 9 7 4

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 = 1 (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 39 5
subtype1 18 2
subtype2 11 2
subtype3 10 1

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 = 0.731 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 33 2
subtype1 15 1
subtype2 10 0
subtype3 8 1

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 = 0.897 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 25 38
subtype1 11 15
subtype2 7 10
subtype3 7 13

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 = 0.62 (Fisher's exact test), Q value = 1

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

nPatients 100 90
ALL 10 2
subtype1 5 0
subtype2 1 1
subtype3 4 1

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

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

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

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

nPatients Mean (Std.Dev)
ALL 11 25.1 (21.9)
subtype1 6 30.8 (26.9)
subtype2 2 4.5 (2.1)
subtype3 3 27.3 (7.5)

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.506 (Fisher's exact test), Q value = 0.97

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 55
subtype1 2 2 22
subtype2 0 0 16
subtype3 0 2 17

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.0551 (Fisher's exact test), Q value = 0.56

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 31
subtype1 1 9
subtype2 3 7
subtype3 0 15

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 29 20 14
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.52 (logrank test), Q value = 0.97

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

nPatients nDeath Duration Range (Median), Month
ALL 62 8 1.0 - 153.7 (85.7)
subtype1 29 5 1.0 - 153.7 (73.9)
subtype2 19 1 19.3 - 137.1 (90.5)
subtype3 14 2 2.5 - 105.5 (99.1)

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.943 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 63 51.7 (14.3)
subtype1 29 51.3 (12.3)
subtype2 20 51.6 (13.8)
subtype3 14 52.8 (19.2)

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

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0393 (Fisher's exact test), Q value = 0.56

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 19 25 13 6
subtype1 3 14 7 5
subtype2 9 7 4 0
subtype3 7 4 2 1

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0297 (Fisher's exact test), Q value = 0.56

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 19 25 19
subtype1 3 14 12
subtype2 9 7 4
subtype3 7 4 3

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 = 0.577 (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 39 5
subtype1 19 4
subtype2 10 0
subtype3 10 1

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 = 0.714 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 33 2
subtype1 15 2
subtype2 10 0
subtype3 8 0

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 = 0.795 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 25 38
subtype1 13 16
subtype2 7 13
subtype3 5 9

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 = 0.62 (Fisher's exact test), Q value = 1

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

nPatients 100 90
ALL 10 2
subtype1 5 0
subtype2 4 1
subtype3 1 1

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

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.831 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 11 25.1 (21.9)
subtype1 6 30.8 (26.9)
subtype2 4 22.0 (12.3)
subtype3 1 3.0 (NA)

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

'RPPA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.377 (Kruskal-Wallis (anova)), Q value = 0.9

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

nPatients Mean (Std.Dev)
ALL 8 1973.8 (15.4)
subtype1 4 1975.5 (11.7)
subtype2 3 1963.7 (14.0)
subtype3 1 1997.0 (NA)

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

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.747 (Fisher's exact test), Q value = 1

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 55
subtype1 2 2 25
subtype2 0 2 17
subtype3 0 0 13

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.054 (Fisher's exact test), Q value = 0.56

Table S49.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 31
subtype1 1 10
subtype2 0 14
subtype3 3 7

Figure S45.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S50.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 17 15 5 6 14 4 5
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.568 (logrank test), Q value = 1

Table S51.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 153.7 (73.9)
subtype1 17 2 24.8 - 137.1 (92.7)
subtype2 14 0 2.5 - 153.7 (101.5)
subtype3 5 1 23.8 - 86.6 (62.4)
subtype4 6 1 23.5 - 109.2 (61.2)
subtype5 14 4 16.7 - 129.4 (59.4)
subtype6 4 0 23.3 - 100.9 (30.1)
subtype7 5 1 1.0 - 100.5 (65.2)

Figure S46.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.698 (Kruskal-Wallis (anova)), Q value = 1

Table S52.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 66 51.5 (14.3)
subtype1 17 47.9 (13.6)
subtype2 15 54.1 (16.3)
subtype3 5 52.4 (9.0)
subtype4 6 60.0 (11.8)
subtype5 14 50.4 (14.0)
subtype6 4 50.0 (17.1)
subtype7 5 49.4 (18.3)

Figure S47.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0942 (Fisher's exact test), Q value = 0.56

Table S53.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
subtype1 6 6 4 1
subtype2 6 7 1 1
subtype3 1 3 1 0
subtype4 1 2 3 0
subtype5 1 7 4 2
subtype6 4 0 0 0
subtype7 2 0 1 2

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0586 (Fisher's exact test), Q value = 0.56

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 21 25 20
subtype1 6 6 5
subtype2 6 7 2
subtype3 1 3 1
subtype4 1 2 3
subtype5 1 7 6
subtype6 4 0 0
subtype7 2 0 3

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.398 (Fisher's exact test), Q value = 0.92

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

nPatients N0 N1+N2
ALL 40 5
subtype1 10 1
subtype2 10 0
subtype3 3 0
subtype4 5 0
subtype5 9 3
subtype6 0 0
subtype7 3 1

Figure S50.  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 = 0.626 (Fisher's exact test), Q value = 1

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 34 2
subtype1 12 0
subtype2 6 1
subtype3 4 0
subtype4 4 0
subtype5 6 1
subtype6 0 0
subtype7 2 0

Figure S51.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.232 (Fisher's exact test), Q value = 0.76

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 27 39
subtype1 8 9
subtype2 9 6
subtype3 1 4
subtype4 0 6
subtype5 5 9
subtype6 2 2
subtype7 2 3

Figure S52.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0976 (Fisher's exact test), Q value = 0.56

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients 100 90
ALL 10 3
subtype1 4 0
subtype2 2 0
subtype3 0 1
subtype4 0 1
subtype5 2 0
subtype6 2 1
subtype7 0 0

Figure S53.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.118 (Kruskal-Wallis (anova)), Q value = 0.56

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 11 25.1 (21.9)
subtype1 3 14.3 (11.2)
subtype2 4 15.8 (16.5)
subtype5 3 50.0 (24.1)
subtype6 1 20.0 (NA)

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.817 (Fisher's exact test), Q value = 1

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 58
subtype1 1 0 16
subtype2 0 1 13
subtype3 0 0 4
subtype4 0 1 5
subtype5 1 2 11
subtype6 0 0 4
subtype7 0 0 5

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0107 (Fisher's exact test), Q value = 0.56

Table S61.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 32
subtype1 0 10
subtype2 1 9
subtype3 1 0
subtype4 0 3
subtype5 0 5
subtype6 0 4
subtype7 2 1

Figure S56.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S62.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 19 16 7 6 10 4 4
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.115 (logrank test), Q value = 0.56

Table S63.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 153.7 (73.9)
subtype1 19 2 24.8 - 137.1 (92.5)
subtype2 15 0 2.5 - 153.7 (99.9)
subtype3 7 2 1.0 - 100.5 (47.2)
subtype4 6 1 23.5 - 109.2 (61.2)
subtype5 10 4 16.7 - 129.4 (69.7)
subtype6 4 0 42.4 - 87.9 (65.7)
subtype7 4 0 23.3 - 100.9 (30.1)

Figure S57.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.566 (Kruskal-Wallis (anova)), Q value = 1

Table S64.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 66 51.5 (14.3)
subtype1 19 46.8 (13.3)
subtype2 16 53.5 (16.0)
subtype3 7 53.1 (15.9)
subtype4 6 60.0 (11.8)
subtype5 10 53.6 (12.7)
subtype6 4 46.5 (15.1)
subtype7 4 50.0 (17.1)

Figure S58.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.117 (Fisher's exact test), Q value = 0.56

Table S65.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
subtype1 7 7 4 1
subtype2 7 7 1 1
subtype3 1 3 1 2
subtype4 1 2 3 0
subtype5 1 3 4 2
subtype6 0 3 1 0
subtype7 4 0 0 0

Figure S59.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0783 (Fisher's exact test), Q value = 0.56

Table S66.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 21 25 20
subtype1 7 7 5
subtype2 7 7 2
subtype3 1 3 3
subtype4 1 2 3
subtype5 1 3 6
subtype6 0 3 1
subtype7 4 0 0

Figure S60.  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 = 0.162 (Fisher's exact test), Q value = 0.61

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 40 5
subtype1 12 1
subtype2 11 0
subtype3 3 1
subtype4 5 0
subtype5 6 3
subtype6 3 0
subtype7 0 0

Figure S61.  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 = 0.442 (Fisher's exact test), Q value = 0.97

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

nPatients 0 1
ALL 34 2
subtype1 13 0
subtype2 7 1
subtype3 4 0
subtype4 4 0
subtype5 4 1
subtype6 2 0
subtype7 0 0

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.104 (Fisher's exact test), Q value = 0.56

Table S69.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 27 39
subtype1 9 10
subtype2 10 6
subtype3 1 6
subtype4 0 6
subtype5 4 6
subtype6 1 3
subtype7 2 2

Figure S63.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0968 (Fisher's exact test), Q value = 0.56

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients 100 90
ALL 10 3
subtype1 4 0
subtype2 2 0
subtype3 0 1
subtype4 0 1
subtype5 2 0
subtype6 0 0
subtype7 2 1

Figure S64.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.118 (Kruskal-Wallis (anova)), Q value = 0.56

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 11 25.1 (21.9)
subtype1 3 14.3 (11.2)
subtype2 4 15.8 (16.5)
subtype5 3 50.0 (24.1)
subtype7 1 20.0 (NA)

Figure S65.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.506 (Fisher's exact test), Q value = 0.97

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 58
subtype1 1 0 18
subtype2 0 1 14
subtype3 0 0 6
subtype4 0 1 5
subtype5 1 1 8
subtype6 0 1 3
subtype7 0 0 4

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0916 (Fisher's exact test), Q value = 0.56

Table S73.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 32
subtype1 0 10
subtype2 2 9
subtype3 2 1
subtype4 0 3
subtype5 0 3
subtype6 0 2
subtype7 0 4

Figure S67.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S74.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4
Number of samples 18 26 10 12
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.276 (logrank test), Q value = 0.79

Table S75.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 153.7 (73.9)
subtype1 17 4 1.0 - 152.0 (51.1)
subtype2 26 3 3.5 - 153.7 (91.5)
subtype3 10 2 16.7 - 129.4 (68.1)
subtype4 12 0 11.2 - 108.6 (56.9)

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00899 (Kruskal-Wallis (anova)), Q value = 0.56

Table S76.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 66 51.5 (14.3)
subtype1 18 59.1 (15.0)
subtype2 26 51.7 (14.4)
subtype3 10 49.5 (7.3)
subtype4 12 41.5 (11.9)

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.296 (Fisher's exact test), Q value = 0.81

Table S77.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
subtype1 5 4 6 3
subtype2 11 8 4 3
subtype3 3 6 1 0
subtype4 2 7 3 0

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.161 (Fisher's exact test), Q value = 0.61

Table S78.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 21 25 20
subtype1 5 4 9
subtype2 11 8 7
subtype3 3 6 1
subtype4 2 7 3

Figure S71.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.912 (Fisher's exact test), Q value = 1

Table S79.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 40 5
subtype1 9 2
subtype2 15 2
subtype3 8 1
subtype4 8 0

Figure S72.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

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

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 34 2
subtype1 7 1
subtype2 16 1
subtype3 6 0
subtype4 5 0

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.698 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 27 39
subtype1 6 12
subtype2 12 14
subtype3 3 7
subtype4 6 6

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

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.385 (Fisher's exact test), Q value = 0.9

Table S82.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients 100 90
ALL 10 3
subtype1 2 2
subtype2 4 0
subtype3 2 0
subtype4 2 1

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.289 (Kruskal-Wallis (anova)), Q value = 0.81

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 11 25.1 (21.9)
subtype1 3 31.7 (14.6)
subtype2 4 11.0 (11.3)
subtype3 1 75.0 (NA)
subtype4 3 20.7 (16.3)

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.0417 (Fisher's exact test), Q value = 0.56

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 58
subtype1 0 0 18
subtype2 2 1 23
subtype3 0 0 10
subtype4 0 3 7

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.647 (Fisher's exact test), Q value = 1

Table S85.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 32
subtype1 1 9
subtype2 1 14
subtype3 1 4
subtype4 1 5

Figure S78.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S86.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 18 11 12 6 5 5 9
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.123 (logrank test), Q value = 0.57

Table S87.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 65 9 1.0 - 153.7 (73.9)
subtype1 18 2 30.2 - 130.1 (91.6)
subtype2 11 1 2.5 - 153.7 (87.9)
subtype3 12 1 23.8 - 108.6 (75.7)
subtype4 6 0 19.3 - 109.2 (38.3)
subtype5 4 0 11.2 - 100.5 (72.8)
subtype6 5 2 16.7 - 152.0 (92.5)
subtype7 9 3 1.0 - 100.9 (28.8)

Figure S79.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.355 (Kruskal-Wallis (anova)), Q value = 0.89

Table S88.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 66 51.5 (14.3)
subtype1 18 49.2 (12.4)
subtype2 11 58.5 (14.3)
subtype3 12 47.8 (12.6)
subtype4 6 47.3 (15.0)
subtype5 5 51.4 (19.9)
subtype6 5 46.0 (13.5)
subtype7 9 58.4 (15.6)

Figure S80.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.619 (Fisher's exact test), Q value = 1

Table S89.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 21 25 14 6
subtype1 7 5 5 1
subtype2 3 5 1 2
subtype3 2 8 2 0
subtype4 3 2 1 0
subtype5 1 2 2 0
subtype6 1 2 1 1
subtype7 4 1 2 2

Figure S81.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.646 (Fisher's exact test), Q value = 1

Table S90.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 21 25 20
subtype1 7 5 6
subtype2 3 5 3
subtype3 2 8 2
subtype4 3 2 1
subtype5 1 2 2
subtype6 1 2 2
subtype7 4 1 4

Figure S82.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.26 (Fisher's exact test), Q value = 0.79

Table S91.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 40 5
subtype1 11 1
subtype2 6 1
subtype3 10 0
subtype4 4 0
subtype5 3 0
subtype6 3 2
subtype7 3 1

Figure S83.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.146 (Fisher's exact test), Q value = 0.58

Table S92.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 34 2
subtype1 13 0
subtype2 6 1
subtype3 9 0
subtype4 1 0
subtype5 2 0
subtype6 1 1
subtype7 2 0

Figure S84.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.583 (Fisher's exact test), Q value = 1

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 27 39
subtype1 10 8
subtype2 5 6
subtype3 4 8
subtype4 2 4
subtype5 1 4
subtype6 3 2
subtype7 2 7

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.136 (Fisher's exact test), Q value = 0.58

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

nPatients 100 90
ALL 10 3
subtype1 3 0
subtype2 0 0
subtype3 4 0
subtype4 0 1
subtype5 0 1
subtype6 1 0
subtype7 2 1

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.233 (Fisher's exact test), Q value = 0.76

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 58
subtype1 1 0 17
subtype2 1 0 10
subtype3 0 3 9
subtype4 0 1 4
subtype5 0 0 4
subtype6 0 0 5
subtype7 0 0 9

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.275 (Fisher's exact test), Q value = 0.79

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 32
subtype1 1 8
subtype2 0 5
subtype3 1 5
subtype4 0 2
subtype5 2 2
subtype6 0 3
subtype7 0 7

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 9 13 14 12
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.821 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 47 8 1.0 - 153.7 (65.2)
subtype1 9 2 30.2 - 97.0 (73.9)
subtype2 13 1 3.5 - 153.7 (71.4)
subtype3 14 3 2.5 - 152.0 (62.8)
subtype4 11 2 1.0 - 100.5 (31.3)

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 'YEARS_TO_BIRTH'

P value = 0.101 (Kruskal-Wallis (anova)), Q value = 0.56

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

nPatients Mean (Std.Dev)
ALL 48 52.4 (14.9)
subtype1 9 50.8 (11.1)
subtype2 13 51.8 (15.5)
subtype3 14 47.7 (15.9)
subtype4 12 59.7 (14.4)

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.144 (Fisher's exact test), Q value = 0.58

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 14 16 12 6
subtype1 3 1 4 1
subtype2 7 4 1 1
subtype3 1 8 3 2
subtype4 3 3 4 2

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0625 (Fisher's exact test), Q value = 0.56

Table S101.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 14 16 18
subtype1 3 1 5
subtype2 7 4 2
subtype3 1 8 5
subtype4 3 3 6

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 = 0.424 (Fisher's exact test), Q value = 0.96

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

nPatients N0 N1+N2
ALL 29 5
subtype1 7 1
subtype2 8 0
subtype3 7 3
subtype4 7 1

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 = 0.68 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 24 2
subtype1 8 0
subtype2 6 1
subtype3 5 1
subtype4 5 0

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 = 0.272 (Fisher's exact test), Q value = 0.79

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

nPatients FEMALE MALE
ALL 21 27
subtype1 6 3
subtype2 5 8
subtype3 7 7
subtype4 3 9

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

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

nPatients 100 90
ALL 6 2
subtype1 0 0
subtype2 2 0
subtype3 3 1
subtype4 1 1

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 'NUMBER_PACK_YEARS_SMOKED'

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

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

nPatients Mean (Std.Dev)
ALL 10 24.1 (22.9)
subtype1 1 6.0 (NA)
subtype2 2 2.0 (1.4)
subtype3 3 37.3 (33.7)
subtype4 4 29.8 (12.5)

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

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

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 44
subtype1 0 0 9
subtype2 0 1 12
subtype3 1 0 11
subtype4 0 0 12

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.335 (Fisher's exact test), Q value = 0.89

Table S108.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 20
subtype1 1 0
subtype2 1 8
subtype3 1 6
subtype4 1 6

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: '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 5 6 7 8 9
Number of samples 5 6 5 8 5 4 6 5 4
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.167 (logrank test), Q value = 0.61

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

nPatients nDeath Duration Range (Median), Month
ALL 47 8 1.0 - 153.7 (65.2)
subtype1 5 2 3.5 - 103.1 (38.1)
subtype2 6 0 23.5 - 153.7 (62.9)
subtype3 5 1 24.8 - 137.1 (54.7)
subtype4 7 1 2.5 - 152.0 (92.5)
subtype5 5 0 65.2 - 99.0 (86.6)
subtype6 4 0 59.1 - 87.1 (75.7)
subtype7 6 2 1.0 - 100.5 (72.4)
subtype8 5 2 11.2 - 71.4 (19.3)
subtype9 4 0 23.3 - 100.9 (30.1)

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 'YEARS_TO_BIRTH'

P value = 0.796 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 48 52.4 (14.9)
subtype1 5 55.2 (11.5)
subtype2 6 49.5 (15.7)
subtype3 5 53.0 (15.0)
subtype4 8 55.2 (19.8)
subtype5 5 44.6 (5.4)
subtype6 4 48.0 (14.5)
subtype7 6 61.3 (17.7)
subtype8 5 50.2 (13.6)
subtype9 4 50.0 (17.1)

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

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0258 (Fisher's exact test), Q value = 0.56

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 14 16 12 6
subtype1 2 0 1 2
subtype2 3 3 0 0
subtype3 1 1 2 1
subtype4 2 5 0 1
subtype5 1 2 2 0
subtype6 1 2 1 0
subtype7 0 2 2 2
subtype8 0 1 4 0
subtype9 4 0 0 0

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

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0215 (Fisher's exact test), Q value = 0.56

Table S113.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3+T4
ALL 14 16 18
subtype1 2 0 3
subtype2 3 3 0
subtype3 1 1 3
subtype4 2 5 1
subtype5 1 2 2
subtype6 1 2 1
subtype7 0 2 4
subtype8 0 1 4
subtype9 4 0 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 = 0.736 (Fisher's exact test), Q value = 1

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

nPatients N0 N1+N2
ALL 29 5
subtype1 2 1
subtype2 5 0
subtype3 3 1
subtype4 5 1
subtype5 5 0
subtype6 3 0
subtype7 4 1
subtype8 2 1
subtype9 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 = 0.828 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 24 2
subtype1 4 1
subtype2 3 0
subtype3 4 0
subtype4 2 1
subtype5 4 0
subtype6 2 0
subtype7 3 0
subtype8 2 0
subtype9 0 0

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 = 0.146 (Fisher's exact test), Q value = 0.58

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

nPatients FEMALE MALE
ALL 21 27
subtype1 4 1
subtype2 2 4
subtype3 1 4
subtype4 4 4
subtype5 4 1
subtype6 2 2
subtype7 0 6
subtype8 2 3
subtype9 2 2

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

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

nPatients 100 90
ALL 6 2
subtype1 0 0
subtype2 1 0
subtype3 0 0
subtype4 1 1
subtype5 0 0
subtype6 2 0
subtype7 0 0
subtype8 0 0
subtype9 2 1

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 'RACE'

P value = 0.762 (Fisher's exact test), Q value = 1

Table S118.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 1 44
subtype1 0 0 5
subtype2 0 1 5
subtype3 1 0 4
subtype4 0 0 7
subtype5 0 0 5
subtype6 0 0 4
subtype7 0 0 6
subtype8 0 0 4
subtype9 0 0 4

Figure S108.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.861 (Fisher's exact test), Q value = 1

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 20
subtype1 1 1
subtype2 1 3
subtype3 0 2
subtype4 1 4
subtype5 0 0
subtype6 0 1
subtype7 1 2
subtype8 0 3
subtype9 0 4

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KICH-TP/22539844/KICH-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KICH-TP/22506450/KICH-TP.merged_data.txt

  • Number of patients = 66

  • Number of clustering approaches = 10

  • Number of selected clinical features = 12

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

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

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)