Correlation between aggregated molecular cancer subtypes and selected clinical features
Testicular Germ Cell Tumors (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1ZP45DM
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1ZP45DM
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 10 clinical features across 134 patients, 40 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGIC_STAGE' and 'RADIATION_THERAPY'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'RADIATION_THERAPY'.

  • CNMF clustering analysis on RPPA data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE', and 'RADIATION_THERAPY'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_M_STAGE', and 'RADIATION_THERAPY'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'RADIATION_THERAPY'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_M_STAGE', and 'RADIATION_THERAPY'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGY_T_STAGE',  'PATHOLOGY_M_STAGE', and 'RADIATION_THERAPY'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'RADIATION_THERAPY'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE', and 'RADIATION_THERAPY'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 40 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		PATHOLOGIC
STAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		RADIATION
THERAPY 		KARNOFSKY
PERFORMANCE
SCORE 		RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test
Copy Number Ratio CNMF subtypes 0.337
(0.552) 		0.158
(0.336) 		0.00414
(0.0183) 		0.777
(0.926) 		0.785
(0.926) 		0.325
(0.545) 		0.00701
(0.027) 		0.903
(0.951) 		0.856
(0.926) 		0.479
(0.665)
METHLYATION CNMF 0.327
(0.545) 		0.00188
(0.0122) 		0.0023
(0.0122) 		0.0118
(0.0408) 		0.102
(0.222) 		0.168
(0.349) 		0.00023
(0.0025) 		0.67
(0.859) 		0.364
(0.578) 		0.685
(0.867)
RPPA CNMF subtypes 0.847
(0.926) 		0.025
(0.0646) 		9e-05
(0.0015) 		0.00135
(0.00964) 		0.0158
(0.0493) 		0.0826
(0.184) 		0.002
(0.0122) 		0.751
(0.926) 		0.976
(0.988) 		0.802
(0.926)
RPPA cHierClus subtypes 0.407
(0.605) 		0.031
(0.0774) 		0.00232
(0.0122) 		0.298
(0.513) 		0.208
(0.392) 		0.00736
(0.027) 		0.00016
(0.00229) 		0.667
(0.859) 		0.838
(0.926) 		1
(1.00)
RNAseq CNMF subtypes 0.201
(0.387) 		0.00426
(0.0183) 		0.00043
(0.00391) 		0.00414
(0.0183) 		0.349
(0.563) 		0.0697
(0.162) 		1e-05
(0.000667) 		0.611
(0.815) 		0.86
(0.926) 		0.392
(0.593)
RNAseq cHierClus subtypes 0.0235
(0.0646) 		0.0551
(0.134) 		0.00401
(0.0183) 		0.00756
(0.027) 		0.22
(0.408) 		0.0252
(0.0646) 		4e-05
(0.001) 		0.493
(0.667) 		0.961
(0.988) 		0.418
(0.605)
MIRSEQ CNMF 0.945
(0.984) 		0.177
(0.361) 		0.0589
(0.14) 		0.0235
(0.0646) 		0.297
(0.513) 		0.0252
(0.0646) 		0.00019
(0.00237) 		0.63
(0.829) 		0.978
(0.988) 		0.447
(0.639)
MIRSEQ CHIERARCHICAL 0.743
(0.926) 		0.00522
(0.0209) 		0.00055
(0.00423) 		0.00439
(0.0183) 		0.252
(0.457) 		0.016
(0.0493) 		2e-05
(0.000667) 		0.813
(0.926) 		0.862
(0.926) 		0.389
(0.593)
MIRseq Mature CNMF subtypes 0.489
(0.667) 		0.0147
(0.049) 		0.00025
(0.0025) 		0.00055
(0.00423) 		0.295
(0.513) 		0.0757
(0.172) 		6e-05
(0.0012) 		0.37
(0.578) 		0.821
(0.926) 		0.479
(0.665)
MIRseq Mature cHierClus subtypes 0.788
(0.926) 		0.0176
(0.0517) 		0.00223
(0.0122) 		0.0163
(0.0493) 		0.2
(0.387) 		0.181
(0.362) 		2e-05
(0.000667) 		0.846
(0.926) 		0.895
(0.951) 		0.415
(0.605)
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 44 43 47
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.337 (logrank test), Q value = 0.55

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

nPatients nDeath Duration Range (Median), Month
ALL 134 4 0.1 - 244.5 (41.5)
subtype1 44 2 0.1 - 225.6 (34.3)
subtype2 43 0 0.2 - 232.8 (40.0)
subtype3 47 2 0.6 - 244.5 (68.0)

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 'YEARS_TO_BIRTH'

P value = 0.158 (Kruskal-Wallis (anova)), Q value = 0.34

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

nPatients Mean (Std.Dev)
ALL 134 32.0 (9.3)
subtype1 44 32.8 (8.3)
subtype2 43 32.6 (8.4)
subtype3 47 30.6 (10.9)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00414 (Fisher's exact test), Q value = 0.018

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 19 26 11 5 6 1 1 2 1 6 5 46
subtype1 4 15 5 1 4 0 1 0 1 1 1 10
subtype2 10 6 4 0 2 1 0 1 0 0 1 15
subtype3 5 5 2 4 0 0 0 1 0 5 3 21

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 = 0.777 (Fisher's exact test), Q value = 0.93

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

nPatients T1 T2 T3
ALL 76 51 6
subtype1 27 14 2
subtype2 24 18 1
subtype3 25 19 3

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 = 0.785 (Fisher's exact test), Q value = 0.93

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

nPatients N0 N1+N2
ALL 46 13
subtype1 13 5
subtype2 13 3
subtype3 20 5

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 = 0.325 (Fisher's exact test), Q value = 0.55

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

nPatients 0 1
ALL 115 4
subtype1 37 1
subtype2 38 0
subtype3 40 3

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 'RADIATION_THERAPY'

P value = 0.00701 (Fisher's exact test), Q value = 0.027

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

nPatients NO YES
ALL 110 19
subtype1 37 6
subtype2 28 11
subtype3 45 2

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.903 (Kruskal-Wallis (anova)), Q value = 0.95

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

nPatients Mean (Std.Dev)
ALL 98 94.9 (6.0)
subtype1 28 94.6 (5.8)
subtype2 34 95.3 (5.6)
subtype3 36 94.7 (6.5)

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 'RACE'

P value = 0.856 (Fisher's exact test), Q value = 0.93

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 6 119
subtype1 2 3 38
subtype2 1 1 38
subtype3 1 2 43

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.479 (Fisher's exact test), Q value = 0.67

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 111
subtype1 2 37
subtype2 4 37
subtype3 6 37

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S12.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 54 45 35
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.327 (logrank test), Q value = 0.55

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 134 4 0.1 - 244.5 (41.5)
subtype1 54 2 0.1 - 211.9 (27.7)
subtype2 45 2 0.5 - 230.9 (75.2)
subtype3 35 0 0.4 - 244.5 (51.0)

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00188 (Kruskal-Wallis (anova)), Q value = 0.012

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 134 32.0 (9.3)
subtype1 54 34.3 (8.2)
subtype2 45 28.6 (9.4)
subtype3 35 32.7 (9.8)

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.0023 (Fisher's exact test), Q value = 0.012

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 19 26 11 5 6 1 1 2 1 6 5 46
subtype1 9 19 5 0 1 1 1 0 0 1 0 15
subtype2 4 3 4 3 5 0 0 1 1 4 2 16
subtype3 6 4 2 2 0 0 0 1 0 1 3 15

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0118 (Fisher's exact test), Q value = 0.041

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 76 51 6
subtype1 35 16 2
subtype2 18 26 1
subtype3 23 9 3

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.102 (Fisher's exact test), Q value = 0.22

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 46 13
subtype1 15 3
subtype2 17 9
subtype3 14 1

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.168 (Fisher's exact test), Q value = 0.35

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 4
subtype1 49 0
subtype2 38 2
subtype3 28 2

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.00023 (Fisher's exact test), Q value = 0.0025

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 110 19
subtype1 37 14
subtype2 44 0
subtype3 29 5

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.67 (Kruskal-Wallis (anova)), Q value = 0.86

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 98 94.9 (6.0)
subtype1 37 94.9 (5.6)
subtype2 34 94.1 (7.0)
subtype3 27 95.9 (5.0)

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'RACE'

P value = 0.364 (Fisher's exact test), Q value = 0.58

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 6 119
subtype1 3 1 49
subtype2 0 3 42
subtype3 1 2 28

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.685 (Fisher's exact test), Q value = 0.87

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 111
subtype1 5 48
subtype2 3 37
subtype3 4 26

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S23.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 44 7 8 29 16
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.847 (logrank test), Q value = 0.93

Table S24.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 104 4 0.1 - 244.5 (45.3)
subtype1 44 1 0.1 - 232.8 (32.5)
subtype2 7 0 15.5 - 203.3 (37.0)
subtype3 8 0 22.2 - 172.0 (75.4)
subtype4 29 2 0.6 - 244.5 (73.3)
subtype5 16 1 6.9 - 230.9 (89.0)

Figure S21.  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 = 0.025 (Kruskal-Wallis (anova)), Q value = 0.065

Table S25.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 104 32.0 (9.0)
subtype1 44 34.3 (8.5)
subtype2 7 29.9 (4.9)
subtype3 8 34.1 (7.0)
subtype4 29 31.2 (10.8)
subtype5 16 26.9 (7.1)

Figure S22.  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 = 9e-05 (Fisher's exact test), Q value = 0.0015

Table S26.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 14 21 8 5 3 1 1 1 5 4 38
subtype1 11 12 4 0 0 0 0 0 0 2 14
subtype2 0 2 0 0 1 1 0 0 1 0 2
subtype3 0 0 4 0 1 0 0 1 0 0 2
subtype4 3 4 0 2 0 0 1 0 2 2 14
subtype5 0 3 0 3 1 0 0 0 2 0 6

Figure S23.  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 = 0.00135 (Fisher's exact test), Q value = 0.0096

Table S27.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 61 40 3
subtype1 29 15 0
subtype2 4 3 0
subtype3 1 7 0
subtype4 21 5 3
subtype5 6 10 0

Figure S24.  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 = 0.0158 (Fisher's exact test), Q value = 0.049

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 40 9
subtype1 16 0
subtype2 1 2
subtype3 5 2
subtype4 12 2
subtype5 6 3

Figure S25.  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 = 0.0826 (Fisher's exact test), Q value = 0.18

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 90 4
subtype1 40 0
subtype2 6 0
subtype3 7 1
subtype4 22 3
subtype5 15 0

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.002 (Fisher's exact test), Q value = 0.012

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 86 16
subtype1 29 13
subtype2 5 2
subtype3 8 0
subtype4 28 1
subtype5 16 0

Figure S27.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.751 (Kruskal-Wallis (anova)), Q value = 0.93

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 79 94.7 (5.7)
subtype1 32 95.3 (5.7)
subtype2 5 96.0 (5.5)
subtype3 8 93.8 (5.2)
subtype4 21 94.8 (6.0)
subtype5 13 93.1 (6.3)

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.976 (Fisher's exact test), Q value = 0.99

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 4 92
subtype1 3 2 36
subtype2 0 0 7
subtype3 0 0 8
subtype4 1 1 26
subtype5 0 1 15

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.802 (Fisher's exact test), Q value = 0.93

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 85
subtype1 5 36
subtype2 0 6
subtype3 0 8
subtype4 4 21
subtype5 1 14

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S34.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 50 19 35
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.407 (logrank test), Q value = 0.61

Table S35.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 104 4 0.1 - 244.5 (45.3)
subtype1 50 1 0.1 - 232.8 (36.7)
subtype2 19 0 4.9 - 181.3 (75.2)
subtype3 35 3 0.6 - 244.5 (81.4)

Figure S31.  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 = 0.031 (Kruskal-Wallis (anova)), Q value = 0.077

Table S36.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 104 32.0 (9.0)
subtype1 50 33.9 (8.2)
subtype2 19 32.3 (10.9)
subtype3 35 29.1 (8.5)

Figure S32.  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 = 0.00232 (Fisher's exact test), Q value = 0.012

Table S37.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 14 21 8 5 3 1 1 1 5 4 38
subtype1 11 14 4 0 0 1 0 0 1 2 16
subtype2 0 3 4 1 1 0 0 1 1 1 6
subtype3 3 4 0 4 2 0 1 0 3 1 16

Figure S33.  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 = 0.298 (Fisher's exact test), Q value = 0.51

Table S38.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 61 40 3
subtype1 33 17 0
subtype2 10 8 1
subtype3 18 15 2

Figure S34.  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 = 0.208 (Fisher's exact test), Q value = 0.39

Table S39.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 40 9
subtype1 17 1
subtype2 8 3
subtype3 15 5

Figure S35.  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 = 0.00736 (Fisher's exact test), Q value = 0.027

Table S40.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 90 4
subtype1 45 0
subtype2 13 3
subtype3 32 1

Figure S36.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00016 (Fisher's exact test), Q value = 0.0023

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 86 16
subtype1 33 15
subtype2 19 0
subtype3 34 1

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.667 (Kruskal-Wallis (anova)), Q value = 0.86

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 79 94.7 (5.7)
subtype1 36 95.3 (5.6)
subtype2 16 93.8 (6.2)
subtype3 27 94.4 (5.8)

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.838 (Fisher's exact test), Q value = 0.93

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 4 92
subtype1 3 2 42
subtype2 0 0 18
subtype3 1 2 32

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

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

Table S44.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 85
subtype1 5 41
subtype2 2 14
subtype3 3 30

Figure S40.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S45.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 69 36 29
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.201 (logrank test), Q value = 0.39

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

nPatients nDeath Duration Range (Median), Month
ALL 134 4 0.1 - 244.5 (41.5)
subtype1 69 1 0.1 - 232.8 (28.5)
subtype2 36 3 0.6 - 230.9 (77.7)
subtype3 29 0 4.9 - 244.5 (71.2)

Figure S41.  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 = 0.00426 (Kruskal-Wallis (anova)), Q value = 0.018

Table S47.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 134 32.0 (9.3)
subtype1 69 33.8 (8.1)
subtype2 36 28.8 (10.1)
subtype3 29 31.6 (10.3)

Figure S42.  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 = 0.00043 (Fisher's exact test), Q value = 0.0039

Table S48.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 19 26 11 5 6 1 1 2 1 6 5 46
subtype1 15 20 7 0 1 1 1 0 0 1 2 18
subtype2 2 3 4 3 4 0 0 1 1 3 1 13
subtype3 2 3 0 2 1 0 0 1 0 2 2 15

Figure S43.  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 = 0.00414 (Fisher's exact test), Q value = 0.018

Table S49.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 76 51 6
subtype1 43 23 2
subtype2 13 22 1
subtype3 20 6 3

Figure S44.  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 = 0.349 (Fisher's exact test), Q value = 0.56

Table S50.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 46 13
subtype1 20 3
subtype2 15 7
subtype3 11 3

Figure S45.  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 = 0.0697 (Fisher's exact test), Q value = 0.16

Table S51.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 4
subtype1 61 0
subtype2 32 2
subtype3 22 2

Figure S46.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 0.00067

Table S52.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 110 19
subtype1 45 19
subtype2 36 0
subtype3 29 0

Figure S47.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.611 (Kruskal-Wallis (anova)), Q value = 0.81

Table S53.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 98 94.9 (6.0)
subtype1 50 95.4 (5.4)
subtype2 28 93.6 (7.3)
subtype3 20 95.5 (5.1)

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.86 (Fisher's exact test), Q value = 0.93

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 6 119
subtype1 3 3 60
subtype2 0 2 34
subtype3 1 1 25

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.392 (Fisher's exact test), Q value = 0.59

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 111
subtype1 6 59
subtype2 2 32
subtype3 4 20

Figure S50.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S56.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 42 27 10 17 8 20 10
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0235 (logrank test), Q value = 0.065

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

nPatients nDeath Duration Range (Median), Month
ALL 134 4 0.1 - 244.5 (41.5)
subtype1 42 1 0.1 - 232.8 (28.2)
subtype2 27 0 0.4 - 203.3 (36.6)
subtype3 10 2 0.6 - 216.9 (56.6)
subtype4 17 0 8.9 - 244.5 (75.6)
subtype5 8 0 6.9 - 225.6 (50.0)
subtype6 20 1 14.9 - 230.9 (81.4)
subtype7 10 0 4.9 - 204.2 (48.3)

Figure S51.  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 = 0.0551 (Kruskal-Wallis (anova)), Q value = 0.13

Table S58.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 134 32.0 (9.3)
subtype1 42 34.7 (8.7)
subtype2 27 32.6 (6.9)
subtype3 10 30.0 (8.4)
subtype4 17 29.0 (8.3)
subtype5 8 28.5 (6.8)
subtype6 20 29.0 (11.9)
subtype7 10 35.0 (13.2)

Figure S52.  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 = 0.00401 (Fisher's exact test), Q value = 0.018

Table S59.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 19 26 11 5 6 1 1 2 1 6 5 46
subtype1 9 12 5 0 1 0 1 0 0 1 2 9
subtype2 6 8 2 0 0 1 0 0 0 0 0 9
subtype3 1 0 1 0 0 0 0 0 0 2 1 5
subtype4 2 2 0 1 1 0 0 0 0 1 1 9
subtype5 0 0 2 1 3 0 0 0 1 0 0 1
subtype6 1 3 1 2 1 0 0 1 0 2 0 8
subtype7 0 1 0 1 0 0 0 1 0 0 1 5

Figure S53.  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 = 0.00756 (Fisher's exact test), Q value = 0.027

Table S60.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 76 51 6
subtype1 21 18 2
subtype2 22 5 0
subtype3 6 3 1
subtype4 11 4 2
subtype5 3 5 0
subtype6 6 14 0
subtype7 7 2 1

Figure S54.  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 = 0.22 (Fisher's exact test), Q value = 0.41

Table S61.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 46 13
subtype1 11 2
subtype2 9 1
subtype3 5 1
subtype4 8 2
subtype5 2 4
subtype6 9 3
subtype7 2 0

Figure S55.  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 = 0.0252 (Fisher's exact test), Q value = 0.065

Table S62.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 4
subtype1 37 0
subtype2 24 0
subtype3 8 1
subtype4 15 1
subtype5 7 1
subtype6 19 0
subtype7 5 1

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

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 4e-05 (Fisher's exact test), Q value = 0.001

Table S63.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 110 19
subtype1 31 7
subtype2 14 12
subtype3 10 0
subtype4 17 0
subtype5 8 0
subtype6 20 0
subtype7 10 0

Figure S57.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.493 (Kruskal-Wallis (anova)), Q value = 0.67

Table S64.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 98 94.9 (6.0)
subtype1 30 95.7 (5.7)
subtype2 20 95.0 (5.1)
subtype3 6 95.0 (8.4)
subtype4 13 96.9 (4.8)
subtype5 8 93.8 (5.2)
subtype6 15 93.3 (8.2)
subtype7 6 91.7 (4.1)

Figure S58.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.961 (Fisher's exact test), Q value = 0.99

Table S65.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 6 119
subtype1 2 2 36
subtype2 1 1 24
subtype3 0 1 9
subtype4 1 0 15
subtype5 0 0 8
subtype6 0 1 19
subtype7 0 1 8

Figure S59.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.418 (Fisher's exact test), Q value = 0.61

Table S66.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 111
subtype1 2 37
subtype2 4 22
subtype3 0 9
subtype4 3 13
subtype5 1 7
subtype6 1 18
subtype7 1 5

Figure S60.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3
Number of samples 51 54 29
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.945 (logrank test), Q value = 0.98

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

nPatients nDeath Duration Range (Median), Month
ALL 134 4 0.1 - 244.5 (41.5)
subtype1 51 1 0.1 - 232.8 (31.9)
subtype2 54 2 0.4 - 230.9 (47.2)
subtype3 29 1 4.9 - 244.5 (71.2)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.177 (Kruskal-Wallis (anova)), Q value = 0.36

Table S69.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 134 32.0 (9.3)
subtype1 51 33.6 (8.3)
subtype2 54 30.7 (9.5)
subtype3 29 31.5 (10.4)

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.0589 (Fisher's exact test), Q value = 0.14

Table S70.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 19 26 11 5 6 1 1 2 1 6 5 46
subtype1 11 15 5 0 1 0 1 0 0 1 2 13
subtype2 6 8 6 3 4 1 0 1 1 3 1 18
subtype3 2 3 0 2 1 0 0 1 0 2 2 15

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0235 (Fisher's exact test), Q value = 0.065

Table S71.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 76 51 6
subtype1 32 17 1
subtype2 24 28 2
subtype3 20 6 3

Figure S64.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.297 (Fisher's exact test), Q value = 0.51

Table S72.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 46 13
subtype1 16 2
subtype2 20 9
subtype3 10 2

Figure S65.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0252 (Fisher's exact test), Q value = 0.065

Table S73.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 4
subtype1 44 0
subtype2 50 1
subtype3 21 3

Figure S66.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.00019 (Fisher's exact test), Q value = 0.0024

Table S74.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 110 19
subtype1 33 15
subtype2 48 4
subtype3 29 0

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.63 (Kruskal-Wallis (anova)), Q value = 0.83

Table S75.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 98 94.9 (6.0)
subtype1 35 95.7 (5.6)
subtype2 43 94.4 (6.3)
subtype3 20 94.5 (6.0)

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.978 (Fisher's exact test), Q value = 0.99

Table S76.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 6 119
subtype1 2 2 44
subtype2 1 3 50
subtype3 1 1 25

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.447 (Fisher's exact test), Q value = 0.64

Table S77.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 111
subtype1 4 43
subtype2 4 48
subtype3 4 20

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S78.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 69 36 29
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.743 (logrank test), Q value = 0.93

Table S79.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 134 4 0.1 - 244.5 (41.5)
subtype1 69 1 0.1 - 232.8 (28.5)
subtype2 36 2 0.6 - 230.9 (77.7)
subtype3 29 1 4.9 - 244.5 (71.2)

Figure S71.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00522 (Kruskal-Wallis (anova)), Q value = 0.021

Table S80.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 134 32.0 (9.3)
subtype1 69 33.8 (8.1)
subtype2 36 28.8 (9.9)
subtype3 29 31.5 (10.4)

Figure S72.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.00055 (Fisher's exact test), Q value = 0.0042

Table S81.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 19 26 11 5 6 1 1 2 1 6 5 46
subtype1 15 20 7 0 1 1 1 0 0 1 2 18
subtype2 2 3 4 3 4 0 0 1 1 3 1 13
subtype3 2 3 0 2 1 0 0 1 0 2 2 15

Figure S73.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00439 (Fisher's exact test), Q value = 0.018

Table S82.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 76 51 6
subtype1 43 23 2
subtype2 13 22 1
subtype3 20 6 3

Figure S74.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.252 (Fisher's exact test), Q value = 0.46

Table S83.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 46 13
subtype1 20 3
subtype2 16 8
subtype3 10 2

Figure S75.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.016 (Fisher's exact test), Q value = 0.049

Table S84.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 4
subtype1 61 0
subtype2 33 1
subtype3 21 3

Figure S76.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 2e-05 (Fisher's exact test), Q value = 0.00067

Table S85.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 110 19
subtype1 45 19
subtype2 36 0
subtype3 29 0

Figure S77.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.813 (Kruskal-Wallis (anova)), Q value = 0.93

Table S86.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 98 94.9 (6.0)
subtype1 50 95.4 (5.4)
subtype2 28 94.3 (6.9)
subtype3 20 94.5 (6.0)

Figure S78.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.862 (Fisher's exact test), Q value = 0.93

Table S87.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 6 119
subtype1 3 3 60
subtype2 0 2 34
subtype3 1 1 25

Figure S79.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.389 (Fisher's exact test), Q value = 0.59

Table S88.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 111
subtype1 6 59
subtype2 2 32
subtype3 4 20

Figure S80.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S89.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 68 34 32
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.489 (logrank test), Q value = 0.67

Table S90.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 134 4 0.1 - 244.5 (41.5)
subtype1 68 1 0.1 - 232.8 (30.2)
subtype2 34 1 6.9 - 230.9 (77.7)
subtype3 32 2 0.6 - 244.5 (61.8)

Figure S81.  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 = 0.0147 (Kruskal-Wallis (anova)), Q value = 0.049

Table S91.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 134 32.0 (9.3)
subtype1 68 33.6 (8.0)
subtype2 34 29.6 (10.7)
subtype3 32 31.0 (9.9)

Figure S82.  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 = 0.00025 (Fisher's exact test), Q value = 0.0025

Table S92.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 19 26 11 5 6 1 1 2 1 6 5 46
subtype1 15 19 7 0 1 1 1 0 0 1 2 18
subtype2 2 4 4 3 4 0 0 1 1 3 0 11
subtype3 2 3 0 2 1 0 0 1 0 2 3 17

Figure S83.  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 = 0.00055 (Fisher's exact test), Q value = 0.0042

Table S93.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 76 51 6
subtype1 42 23 2
subtype2 12 22 0
subtype3 22 6 4

Figure S84.  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 = 0.295 (Fisher's exact test), Q value = 0.51

Table S94.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 46 13
subtype1 20 3
subtype2 14 7
subtype3 12 3

Figure S85.  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 = 0.0757 (Fisher's exact test), Q value = 0.17

Table S95.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 4
subtype1 60 0
subtype2 31 2
subtype3 24 2

Figure S86.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 6e-05 (Fisher's exact test), Q value = 0.0012

Table S96.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 110 19
subtype1 45 18
subtype2 33 1
subtype3 32 0

Figure S87.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.37 (Kruskal-Wallis (anova)), Q value = 0.58

Table S97.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 98 94.9 (6.0)
subtype1 49 95.5 (5.4)
subtype2 28 93.2 (7.2)
subtype3 21 95.7 (5.1)

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.821 (Fisher's exact test), Q value = 0.93

Table S98.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 6 119
subtype1 3 3 59
subtype2 0 1 33
subtype3 1 2 27

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

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.479 (Fisher's exact test), Q value = 0.67

Table S99.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 111
subtype1 6 58
subtype2 2 31
subtype3 4 22

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S100.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 36 33 36 29
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.788 (logrank test), Q value = 0.93

Table S101.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 134 4 0.1 - 244.5 (41.5)
subtype1 36 1 0.1 - 232.8 (29.9)
subtype2 33 0 0.4 - 186.3 (28.5)
subtype3 36 2 0.6 - 230.9 (80.8)
subtype4 29 1 4.9 - 244.5 (52.4)

Figure S91.  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 = 0.0176 (Kruskal-Wallis (anova)), Q value = 0.052

Table S102.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 134 32.0 (9.3)
subtype1 36 33.9 (8.4)
subtype2 33 33.8 (7.8)
subtype3 36 29.1 (10.2)
subtype4 29 31.1 (10.2)

Figure S92.  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 = 0.00223 (Fisher's exact test), Q value = 0.012

Table S103.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IS
ALL 19 26 11 5 6 1 1 2 1 6 5 46
subtype1 9 13 3 0 1 0 1 0 0 0 0 9
subtype2 6 7 4 0 0 1 0 0 0 1 2 9
subtype3 2 3 4 3 4 0 0 1 1 4 1 12
subtype4 2 3 0 2 1 0 0 1 0 1 2 16

Figure S93.  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 = 0.0163 (Fisher's exact test), Q value = 0.049

Table S104.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 76 51 6
subtype1 21 13 1
subtype2 22 10 1
subtype3 13 22 1
subtype4 20 6 3

Figure S94.  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 = 0.2 (Fisher's exact test), Q value = 0.39

Table S105.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1+N2
ALL 46 13
subtype1 14 1
subtype2 6 2
subtype3 15 8
subtype4 11 2

Figure S95.  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 = 0.181 (Fisher's exact test), Q value = 0.36

Table S106.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 4
subtype1 33 0
subtype2 28 0
subtype3 32 2
subtype4 22 2

Figure S96.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 2e-05 (Fisher's exact test), Q value = 0.00067

Table S107.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

nPatients NO YES
ALL 110 19
subtype1 23 10
subtype2 22 9
subtype3 36 0
subtype4 29 0

Figure S97.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.846 (Kruskal-Wallis (anova)), Q value = 0.93

Table S108.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 98 94.9 (6.0)
subtype1 26 95.0 (5.8)
subtype2 24 95.8 (5.0)
subtype3 29 93.8 (7.3)
subtype4 19 95.3 (5.1)

Figure S98.  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 = 0.895 (Fisher's exact test), Q value = 0.95

Table S109.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 4 6 119
subtype1 2 1 32
subtype2 1 2 28
subtype3 0 2 34
subtype4 1 1 25

Figure S99.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.415 (Fisher's exact test), Q value = 0.61

Table S110.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 111
subtype1 2 33
subtype2 4 26
subtype3 2 32
subtype4 4 20

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

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/TGCT-TP/20140863/TGCT-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/TGCT-TP/19775566/TGCT-TP.merged_data.txt

  • Number of patients = 134

  • Number of clustering approaches = 10

  • Number of selected clinical features = 10

  • 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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