Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1JW8BWT
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1JW8BWT
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 8 clinical features across 502 patients, 33 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'GENDER',  'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'AGE',  'GENDER',  'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.

  • CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death',  'DISTANT.METASTASIS',  'LYMPH.NODE.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death',  'DISTANT.METASTASIS',  'LYMPH.NODE.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'GENDER',  'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'GENDER',  'DISTANT.METASTASIS',  'LYMPH.NODE.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'GENDER',  'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death' and 'NEOPLASM.DISEASESTAGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death'.

  • 3 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 12 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 33 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		DISTANT
METASTASIS 		LYMPH
NODE
METASTASIS 		TUMOR
STAGECODE 		NEOPLASM
DISEASESTAGE
Statistical Tests logrank test ANOVA Fisher's exact test ANOVA Fisher's exact test Chi-square test ANOVA Chi-square test
mRNA CNMF subtypes 0.231
(1.00) 		0.795
(1.00) 		0.634
(1.00) 		0.12
(1.00) 		0.171
(1.00) 		0.00527
(0.258)
mRNA cHierClus subtypes 0.588
(1.00) 		0.651
(1.00) 		0.705
(1.00) 		0.104
(1.00) 		0.172
(1.00) 		0.00728
(0.342)
Copy Number Ratio CNMF subtypes 0.000614
(0.0369) 		0.015
(0.632) 		0.00221
(0.117) 		0.29
(1.00) 		0.00062
(0.0369) 		0.1
(1.00) 		0.00027
(0.017)
METHLYATION CNMF 4.7e-06
(0.000329) 		0.00373
(0.19) 		0.00031
(0.0192) 		0.703
(1.00) 		0.000191
(0.0122) 		0.426
(1.00) 		4.14e-11
(3.35e-09)
RPPA CNMF subtypes 1.79e-09
(1.43e-07) 		0.129
(1.00) 		0.0611
(1.00) 		0.322
(1.00) 		3.89e-07
(2.91e-05) 		0.00083
(0.0465) 		7.63e-09
(6.03e-07)
RPPA cHierClus subtypes 8.92e-08
(6.87e-06) 		0.00927
(0.408) 		0.578
(1.00) 		0.0463
(1.00) 		9.01e-05
(0.00586) 		0.00271
(0.141) 		1.42e-06
(0.000105)
RNAseq CNMF subtypes 1.63e-06
(0.000119) 		0.09
(1.00) 		6.19e-05
(0.00414) 		0.66
(1.00) 		0.000645
(0.0374) 		0.0564
(1.00) 		3.71e-07
(2.82e-05)
RNAseq cHierClus subtypes 3.34e-08
(2.6e-06) 		0.358
(1.00) 		0.00158
(0.0869) 		0.357
(1.00) 		2e-06
(0.000144) 		0.00482
(0.241) 		7.38e-13
(6.05e-11)
MIRSEQ CNMF 2.31e-06
(0.000164) 		0.0424
(1.00) 		0.000437
(0.0267) 		0.172
(1.00) 		5.25e-05
(0.00357) 		0.0155
(0.636) 		2.95e-05
(0.00204)
MIRSEQ CHIERARCHICAL 0.00211
(0.114) 		0.298
(1.00) 		0.124
(1.00) 		0.943
(1.00) 		0.351
(1.00) 		0.0166
(0.665) 		0.000659
(0.0376)
MIRseq Mature CNMF subtypes 7.54e-05
(0.00498) 		0.0809
(1.00) 		0.01
(0.43) 		0.465
(1.00) 		0.00738
(0.342) 		0.275
(1.00) 		0.00781
(0.352)
MIRseq Mature cHierClus subtypes 0.00711
(0.341) 		0.1
(1.00) 		0.0406
(1.00) 		0.601
(1.00) 		0.0392
(1.00) 		0.0474
(1.00) 		0.169
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

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

P value = 0.231 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 71 13 0.5 - 101.1 (32.6)
subtype1 33 4 0.5 - 101.1 (31.0)
subtype2 24 8 0.5 - 93.3 (36.7)
subtype3 14 1 1.3 - 84.4 (25.0)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.795 (ANOVA), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.634 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'DISTANT.METASTASIS'

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

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 67 5
subtype1 33 1
subtype2 20 4
subtype3 14 0

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'mRNA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.171 (Chi-square test), Q value = 1

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 35 3 34
subtype1 18 0 16
subtype2 10 3 11
subtype3 7 0 7

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00527 (Chi-square test), Q value = 0.26

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

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

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 15 33 24
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.588 (logrank test), Q value = 1

Table S9.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 71 13 0.5 - 101.1 (32.6)
subtype1 15 2 1.3 - 84.4 (24.2)
subtype2 32 4 0.5 - 101.1 (30.5)
subtype3 24 7 0.5 - 93.3 (37.0)

Figure S7.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.651 (ANOVA), Q value = 1

Table S10.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 71 60.5 (12.4)
subtype1 15 63.2 (11.2)
subtype2 32 59.9 (14.0)
subtype3 24 59.7 (10.9)

Figure S8.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.705 (Fisher's exact test), Q value = 1

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 29 43
subtype1 5 10
subtype2 15 18
subtype3 9 15

Figure S9.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'DISTANT.METASTASIS'

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

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 67 5
subtype1 15 0
subtype2 32 1
subtype3 20 4

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'mRNA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.172 (Chi-square test), Q value = 1

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 35 3 34
subtype1 7 0 8
subtype2 17 0 16
subtype3 11 3 10

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00728 (Chi-square test), Q value = 0.34

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 40 13 14 5
subtype1 9 6 0 0
subtype2 22 4 6 1
subtype3 9 3 8 4

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

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

Table S15.  Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 161 210 122
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.000614 (logrank test), Q value = 0.037

Table S16.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 490 158 0.1 - 111.0 (35.2)
subtype1 161 69 0.1 - 109.9 (31.3)
subtype2 209 48 0.1 - 111.0 (37.0)
subtype3 120 41 0.1 - 91.4 (35.7)

Figure S13.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.015 (ANOVA), Q value = 0.63

Table S17.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 492 60.6 (12.2)
subtype1 160 62.8 (11.7)
subtype2 210 59.4 (12.5)
subtype3 122 59.6 (11.9)

Figure S14.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00221 (Fisher's exact test), Q value = 0.12

Table S18.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 171 322
subtype1 39 122
subtype2 86 124
subtype3 46 76

Figure S15.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.29 (ANOVA), Q value = 1

Table S19.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 15 81.3 (33.6)
subtype2 12 91.7 (9.4)
subtype3 9 95.6 (7.3)

Figure S16.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.00062 (Fisher's exact test), Q value = 0.037

Table S20.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 417 76
subtype1 123 38
subtype2 191 19
subtype3 103 19

Figure S17.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.1 (Chi-square test), Q value = 1

Table S21.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 228 18 247
subtype1 72 11 78
subtype2 101 3 106
subtype3 55 4 63

Figure S18.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.00027 (Chi-square test), Q value = 0.017

Table S22.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 237 53 126 77
subtype1 57 17 49 38
subtype2 122 22 48 18
subtype3 58 14 29 21

Figure S19.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S23.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 96 118 69
'METHLYATION CNMF' versus 'Time to Death'

P value = 4.7e-06 (logrank test), Q value = 0.00033

Table S24.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 281 95 0.1 - 109.9 (28.5)
subtype1 95 48 0.1 - 84.7 (28.8)
subtype2 117 20 0.2 - 109.6 (31.5)
subtype3 69 27 0.1 - 109.9 (20.4)

Figure S20.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00373 (ANOVA), Q value = 0.19

Table S25.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 283 61.5 (12.0)
subtype1 96 63.9 (10.6)
subtype2 118 58.7 (12.7)
subtype3 69 62.8 (11.7)

Figure S21.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00031 (Fisher's exact test), Q value = 0.019

Table S26.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 96 187
subtype1 20 76
subtype2 55 63
subtype3 21 48

Figure S22.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.703 (ANOVA), Q value = 1

Table S27.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 28 92.5 (8.0)
subtype1 6 91.7 (7.5)
subtype2 16 91.9 (8.3)
subtype3 6 95.0 (8.4)

Figure S23.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.000191 (Fisher's exact test), Q value = 0.012

Table S28.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 232 51
subtype1 67 29
subtype2 108 10
subtype3 57 12

Figure S24.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.426 (Chi-square test), Q value = 1

Table S29.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 127 9 147
subtype1 41 5 50
subtype2 54 1 63
subtype3 32 3 34

Figure S25.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 4.14e-11 (Chi-square test), Q value = 3.3e-09

Table S30.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 129 27 74 53
subtype1 19 9 38 30
subtype2 79 15 15 9
subtype3 31 3 21 14

Figure S26.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S31.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 101 90 86 76 44 57
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 1.79e-09 (logrank test), Q value = 1.4e-07

Table S32.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 452 151 0.1 - 111.0 (34.3)
subtype1 101 26 0.2 - 111.0 (43.7)
subtype2 90 35 0.1 - 90.4 (29.4)
subtype3 85 22 0.2 - 93.0 (35.3)
subtype4 75 23 0.1 - 96.8 (27.9)
subtype5 44 8 0.2 - 83.8 (36.5)
subtype6 57 37 0.6 - 84.0 (19.7)

Figure S27.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.129 (ANOVA), Q value = 1

Table S33.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 100 61.8 (11.1)
subtype2 90 58.0 (12.2)
subtype3 86 62.3 (12.6)
subtype4 76 60.0 (11.8)
subtype5 44 58.3 (15.7)
subtype6 57 61.2 (11.4)

Figure S28.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0611 (Chi-square test), Q value = 1

Table S34.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 44 57
subtype2 29 61
subtype3 27 59
subtype4 18 58
subtype5 18 26
subtype6 15 42

Figure S29.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.322 (ANOVA), Q value = 1

Table S35.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 7 92.9 (7.6)
subtype2 8 93.8 (5.2)
subtype3 10 90.0 (9.4)
subtype4 2 100.0 (0.0)
subtype5 4 100.0 (0.0)
subtype6 3 93.3 (11.5)

Figure S30.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 3.89e-07 (Chi-square test), Q value = 2.9e-05

Table S36.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 380 74
subtype1 87 14
subtype2 71 19
subtype3 76 10
subtype4 68 8
subtype5 44 0
subtype6 34 23

Figure S31.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RPPA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00083 (Chi-square test), Q value = 0.046

Table S37.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 208 16 230
subtype1 48 1 52
subtype2 46 2 42
subtype3 43 2 41
subtype4 35 3 38
subtype5 13 0 31
subtype6 23 8 26

Figure S32.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 7.63e-09 (Chi-square test), Q value = 6e-07

Table S38.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 219 44 116 75
subtype1 54 11 22 14
subtype2 42 9 19 20
subtype3 39 12 25 10
subtype4 44 7 17 8
subtype5 33 2 9 0
subtype6 7 3 24 23

Figure S33.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S39.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 189 153 112
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 8.92e-08 (logrank test), Q value = 6.9e-06

Table S40.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 452 151 0.1 - 111.0 (34.3)
subtype1 189 42 0.1 - 96.8 (37.0)
subtype2 153 50 0.2 - 111.0 (36.8)
subtype3 110 59 0.1 - 91.4 (21.5)

Figure S34.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00927 (ANOVA), Q value = 0.41

Table S41.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 453 60.4 (12.3)
subtype1 189 58.4 (12.7)
subtype2 152 62.3 (12.3)
subtype3 112 61.3 (11.1)

Figure S35.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.578 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 151 303
subtype1 68 121
subtype2 47 106
subtype3 36 76

Figure S36.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0463 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 34 93.5 (7.7)
subtype1 17 96.5 (4.9)
subtype2 10 89.0 (8.8)
subtype3 7 92.9 (9.5)

Figure S37.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 9.01e-05 (Fisher's exact test), Q value = 0.0059

Table S44.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 380 74
subtype1 170 19
subtype2 131 22
subtype3 79 33

Figure S38.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RPPA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00271 (Chi-square test), Q value = 0.14

Table S45.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 208 16 230
subtype1 72 4 113
subtype2 76 4 73
subtype3 60 8 44

Figure S39.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.42e-06 (Chi-square test), Q value = 1e-04

Table S46.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 219 44 116 75
subtype1 118 16 36 19
subtype2 65 17 48 23
subtype3 36 11 32 33

Figure S40.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S47.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 199 180 101
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.63e-06 (logrank test), Q value = 0.00012

Table S48.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 478 155 0.1 - 111.0 (34.3)
subtype1 199 44 0.1 - 111.0 (37.0)
subtype2 179 83 0.1 - 90.3 (30.6)
subtype3 100 28 0.1 - 93.3 (35.2)

Figure S41.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.09 (ANOVA), Q value = 1

Table S49.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 479 60.6 (12.2)
subtype1 198 61.7 (12.2)
subtype2 180 60.6 (11.8)
subtype3 101 58.4 (12.7)

Figure S42.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 6.19e-05 (Fisher's exact test), Q value = 0.0041

Table S50.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 167 313
subtype1 91 108
subtype2 44 136
subtype3 32 69

Figure S43.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.66 (ANOVA), Q value = 1

Table S51.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 90.9 (17.8)
subtype1 14 91.4 (8.6)
subtype2 12 87.5 (28.3)
subtype3 8 95.0 (7.6)

Figure S44.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.000645 (Fisher's exact test), Q value = 0.037

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

nPatients M0 M1
ALL 403 77
subtype1 176 23
subtype2 136 44
subtype3 91 10

Figure S45.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0564 (Chi-square test), Q value = 1

Table S53.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 228 17 235
subtype1 96 2 101
subtype2 85 12 83
subtype3 47 3 51

Figure S46.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 3.71e-07 (Chi-square test), Q value = 2.8e-05

Table S54.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 233 49 120 78
subtype1 118 19 39 23
subtype2 55 20 61 44
subtype3 60 10 20 11

Figure S47.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S55.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 73 215 192
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 3.34e-08 (logrank test), Q value = 2.6e-06

Table S56.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 478 155 0.1 - 111.0 (34.3)
subtype1 72 14 0.2 - 92.0 (27.8)
subtype2 214 48 0.1 - 111.0 (37.2)
subtype3 192 93 0.1 - 93.3 (30.5)

Figure S48.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.358 (ANOVA), Q value = 1

Table S57.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 479 60.6 (12.2)
subtype1 73 58.7 (13.2)
subtype2 214 61.0 (12.4)
subtype3 192 60.8 (11.5)

Figure S49.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00158 (Fisher's exact test), Q value = 0.087

Table S58.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 167 313
subtype1 23 50
subtype2 93 122
subtype3 51 141

Figure S50.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.357 (ANOVA), Q value = 1

Table S59.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 34 90.9 (17.8)
subtype1 10 95.0 (9.7)
subtype2 13 93.1 (6.3)
subtype3 11 84.5 (29.1)

Figure S51.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 2e-06 (Fisher's exact test), Q value = 0.00014

Table S60.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 403 77
subtype1 70 3
subtype2 191 24
subtype3 142 50

Figure S52.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00482 (Chi-square test), Q value = 0.24

Table S61.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 228 17 235
subtype1 32 2 39
subtype2 104 1 110
subtype3 92 14 86

Figure S53.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 7.38e-13 (Chi-square test), Q value = 6.1e-11

Table S62.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 233 49 120 78
subtype1 54 6 10 3
subtype2 126 22 44 23
subtype3 53 21 66 52

Figure S54.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S63.  Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 117 192 172
'MIRSEQ CNMF' versus 'Time to Death'

P value = 2.31e-06 (logrank test), Q value = 0.00016

Table S64.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 479 156 0.1 - 111.0 (35.2)
subtype1 117 32 0.1 - 109.9 (37.0)
subtype2 192 43 0.1 - 111.0 (35.8)
subtype3 170 81 0.2 - 93.3 (30.6)

Figure S55.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0424 (ANOVA), Q value = 1

Table S65.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 481 60.6 (12.2)
subtype1 117 58.6 (12.3)
subtype2 192 62.1 (12.2)
subtype3 172 60.2 (11.9)

Figure S56.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.000437 (Fisher's exact test), Q value = 0.027

Table S66.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 163 318
subtype1 37 80
subtype2 84 108
subtype3 42 130

Figure S57.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.172 (ANOVA), Q value = 1

Table S67.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 9 95.6 (7.3)
subtype2 15 92.0 (8.6)
subtype3 12 78.3 (37.1)

Figure S58.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 5.25e-05 (Fisher's exact test), Q value = 0.0036

Table S68.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 405 76
subtype1 108 9
subtype2 169 23
subtype3 128 44

Figure S59.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0155 (Chi-square test), Q value = 0.64

Table S69.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 222 18 241
subtype1 56 3 58
subtype2 86 2 104
subtype3 80 13 79

Figure S60.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 2.95e-05 (Chi-square test), Q value = 0.002

Table S70.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 228 51 125 77
subtype1 66 11 30 10
subtype2 106 18 45 23
subtype3 56 22 50 44

Figure S61.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S71.  Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 37 162 282
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00211 (logrank test), Q value = 0.11

Table S72.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 479 156 0.1 - 111.0 (35.2)
subtype1 36 13 0.5 - 85.2 (29.0)
subtype2 162 70 0.1 - 109.9 (35.4)
subtype3 281 73 0.1 - 111.0 (35.2)

Figure S62.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.298 (ANOVA), Q value = 1

Table S73.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 481 60.6 (12.2)
subtype1 37 57.8 (11.0)
subtype2 162 60.3 (12.1)
subtype3 282 61.1 (12.4)

Figure S63.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.124 (Fisher's exact test), Q value = 1

Table S74.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 163 318
subtype1 13 24
subtype2 45 117
subtype3 105 177

Figure S64.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

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

P value = 0.943 (ANOVA), Q value = 1

Table S75.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 36 88.3 (23.0)
subtype1 2 95.0 (7.1)
subtype2 12 87.5 (28.3)
subtype3 22 88.2 (21.3)

Figure S65.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.351 (Fisher's exact test), Q value = 1

Table S76.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 405 76
subtype1 30 7
subtype2 132 30
subtype3 243 39

Figure S66.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0166 (Chi-square test), Q value = 0.67

Table S77.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 222 18 241
subtype1 19 3 15
subtype2 76 11 75
subtype3 127 4 151

Figure S67.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000659 (Chi-square test), Q value = 0.038

Table S78.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 228 51 125 77
subtype1 16 9 5 7
subtype2 60 15 56 31
subtype3 152 27 64 39

Figure S68.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S79.  Get Full Table Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 51 90 76
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 7.54e-05 (logrank test), Q value = 0.005

Table S80.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 216 66 0.2 - 109.6 (36.7)
subtype1 51 12 3.9 - 91.4 (36.4)
subtype2 90 17 0.2 - 109.6 (40.8)
subtype3 75 37 0.2 - 87.5 (31.3)

Figure S69.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0809 (ANOVA), Q value = 1

Table S81.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 217 59.4 (12.7)
subtype1 51 56.1 (12.0)
subtype2 90 61.1 (12.8)
subtype3 76 59.5 (12.9)

Figure S70.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.01 (Fisher's exact test), Q value = 0.43

Table S82.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 65 152
subtype1 15 36
subtype2 36 54
subtype3 14 62

Figure S71.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.465 (ANOVA), Q value = 1

Table S83.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 30 93.3 (8.0)
subtype1 8 95.0 (7.6)
subtype2 11 90.9 (9.4)
subtype3 11 94.5 (6.9)

Figure S72.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.00738 (Fisher's exact test), Q value = 0.34

Table S84.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 179 38
subtype1 46 5
subtype2 79 11
subtype3 54 22

Figure S73.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.275 (Chi-square test), Q value = 1

Table S85.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 96 8 113
subtype1 19 2 30
subtype2 41 1 48
subtype3 36 5 35

Figure S74.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.00781 (Chi-square test), Q value = 0.35

Table S86.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 105 23 48 41
subtype1 34 3 8 6
subtype2 45 11 22 12
subtype3 26 9 18 23

Figure S75.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S87.  Get Full Table Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 59 93 65
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.00711 (logrank test), Q value = 0.34

Table S88.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 216 66 0.2 - 109.6 (36.7)
subtype1 58 26 0.2 - 92.0 (38.9)
subtype2 93 19 0.2 - 109.6 (43.0)
subtype3 65 21 0.2 - 91.4 (29.9)

Figure S76.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.1 (ANOVA), Q value = 1

Table S89.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 217 59.4 (12.7)
subtype1 59 59.6 (12.5)
subtype2 93 61.1 (12.4)
subtype3 65 56.7 (13.2)

Figure S77.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0406 (Fisher's exact test), Q value = 1

Table S90.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 65 152
subtype1 12 47
subtype2 36 57
subtype3 17 48

Figure S78.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.601 (ANOVA), Q value = 1

Table S91.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 30 93.3 (8.0)
subtype1 8 91.2 (11.3)
subtype2 10 93.0 (6.7)
subtype3 12 95.0 (6.7)

Figure S79.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0392 (Fisher's exact test), Q value = 1

Table S92.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients M0 M1
ALL 179 38
subtype1 42 17
subtype2 81 12
subtype3 56 9

Figure S80.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0474 (Chi-square test), Q value = 1

Table S93.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 96 8 113
subtype1 24 1 34
subtype2 41 1 51
subtype3 31 6 28

Figure S81.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.169 (Chi-square test), Q value = 1

Table S94.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 105 23 48 41
subtype1 24 4 13 18
subtype2 47 13 21 12
subtype3 34 6 14 11

Figure S82.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NEOPLASM.DISEASESTAGE'

Methods & Data
Input

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

  • Clinical data file = KIRC-TP.clin.merged.picked.txt

  • Number of patients = 502

  • Number of clustering approaches = 12

  • Number of selected clinical features = 8

  • 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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' 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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] 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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] 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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