Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1W37TZ9
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1W37TZ9
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 8 different clustering approaches and 10 clinical features across 163 patients, 14 significant findings detected with P value < 0.05 and Q value < 0.25.

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

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

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

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

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

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

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

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 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, 14 significant findings detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.0966
(1.00) 		1.48e-07
(9.77e-06) 		0.0356
(1.00) 		0.0902
(1.00) 		0.167
(1.00) 		0.0993
(1.00) 		0.242
(1.00) 		0.00727
(0.372)
AGE ANOVA 0.503
(1.00) 		0.00104
(0.0593) 		0.752
(1.00) 		0.0299
(1.00) 		0.142
(1.00) 		0.557
(1.00) 		0.217
(1.00) 		0.802
(1.00)
NEOPLASM DISEASESTAGE Chi-square test 0.0127
(0.624) 		1.8e-07
(1.17e-05) 		1.2e-07
(8.01e-06) 		1.69e-05
(0.00104) 		0.00715
(0.372) 		0.146
(1.00) 		0.0168
(0.757) 		1.08e-05
(0.000681)
PATHOLOGY T STAGE Chi-square test 0.0347
(1.00) 		5.63e-08
(3.83e-06) 		3.36e-07
(2.15e-05) 		0.000832
(0.0482) 		0.0441
(1.00) 		0.0409
(1.00) 		0.0375
(1.00) 		9.65e-05
(0.00589)
PATHOLOGY N STAGE Chi-square test 0.697
(1.00) 		0.00626
(0.332) 		0.024
(1.00) 		0.0179
(0.786) 		0.0573
(1.00) 		0.128
(1.00) 		0.148
(1.00) 		0.0758
(1.00)
PATHOLOGY M STAGE Chi-square test 0.034
(1.00) 		0.000455
(0.0273) 		0.0049
(0.265) 		0.0145
(0.68) 		0.311
(1.00) 		0.0347
(1.00) 		0.187
(1.00) 		0.107
(1.00)
GENDER Fisher's exact test 0.0576
(1.00) 		0.00226
(0.124) 		0.00124
(0.0695) 		0.000504
(0.0297) 		0.0128
(0.624) 		0.0156
(0.72) 		0.0701
(1.00) 		0.0095
(0.475)
KARNOFSKY PERFORMANCE SCORE ANOVA 0.531
(1.00) 		0.206
(1.00) 		0.0815
(1.00) 		0.5
(1.00) 		0.196
(1.00) 		0.0852
(1.00) 		0.305
(1.00) 		0.0419
(1.00)
NUMBERPACKYEARSSMOKED ANOVA 0.402
(1.00) 		0.302
(1.00) 		0.437
(1.00) 		0.0568
(1.00)
YEAROFTOBACCOSMOKINGONSET ANOVA
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3 4 5
Number of samples 57 31 33 18 24
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.0966 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 150 17 0.0 - 194.8 (14.0)
subtype1 52 6 0.1 - 129.9 (24.7)
subtype2 29 1 0.0 - 87.2 (9.6)
subtype3 31 6 0.1 - 123.6 (9.6)
subtype4 17 1 0.0 - 194.8 (22.1)
subtype5 21 3 0.1 - 96.9 (7.3)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.503 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 157 59.7 (12.5)
subtype1 56 60.7 (12.9)
subtype2 29 56.7 (11.3)
subtype3 32 58.7 (11.5)
subtype4 17 62.8 (11.1)
subtype5 23 60.2 (15.4)

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

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

P value = 0.0127 (Chi-square test), Q value = 0.62

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 95 11 37 10
subtype1 33 4 16 1
subtype2 23 1 3 0
subtype3 11 2 13 5
subtype4 12 2 2 1
subtype5 16 2 3 3

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

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

P value = 0.0347 (Chi-square test), Q value = 1

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

nPatients T1 T2 T3+T4
ALL 102 17 44
subtype1 37 4 16
subtype2 24 4 3
subtype3 12 5 16
subtype4 13 2 3
subtype5 16 2 6

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 = 0.697 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2
ALL 27 16 4
subtype1 8 6 1
subtype2 4 0 0
subtype3 10 6 2
subtype4 3 1 0
subtype5 2 3 1

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 = 0.034 (Chi-square test), Q value = 1

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

nPatients M0 M1 MX
ALL 62 6 82
subtype1 26 1 26
subtype2 8 0 20
subtype3 16 3 12
subtype4 8 0 8
subtype5 4 2 16

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0576 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 49 114
subtype1 20 37
subtype2 5 26
subtype3 14 19
subtype4 2 16
subtype5 8 16

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

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

P value = 0.531 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 39 88.7 (18.4)
subtype1 12 86.7 (16.1)
subtype2 9 91.1 (10.5)
subtype3 8 81.2 (33.1)
subtype4 4 100.0 (0.0)
subtype5 6 91.7 (7.5)

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S10.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 31 21 28 37 30
'METHLYATION CNMF' versus 'Time to Death'

P value = 1.48e-07 (logrank test), Q value = 9.8e-06

Table S11.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 134 15 0.0 - 194.8 (13.2)
subtype1 28 5 0.0 - 92.6 (22.0)
subtype2 19 7 0.1 - 63.7 (4.0)
subtype3 27 2 0.0 - 194.8 (20.1)
subtype4 33 1 0.0 - 99.8 (5.7)
subtype5 27 0 0.1 - 129.9 (13.6)

Figure S9.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00104 (ANOVA), Q value = 0.059

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 141 59.9 (12.6)
subtype1 29 63.9 (12.5)
subtype2 20 54.5 (15.3)
subtype3 28 65.6 (12.2)
subtype4 35 59.6 (10.7)
subtype5 29 54.6 (10.2)

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.8e-07 (Chi-square test), Q value = 1.2e-05

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 90 8 36 9
subtype1 22 2 4 2
subtype2 3 1 12 5
subtype3 10 2 13 2
subtype4 28 3 5 0
subtype5 27 0 2 0

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 5.63e-08 (Chi-square test), Q value = 3.8e-06

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 95 10 42
subtype1 23 1 7
subtype2 4 1 16
subtype3 11 3 14
subtype4 30 4 3
subtype5 27 1 2

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.00626 (Chi-square test), Q value = 0.33

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 25 15 4
subtype1 6 2 1
subtype2 2 11 2
subtype3 10 1 1
subtype4 5 1 0
subtype5 2 0 0

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.000455 (Chi-square test), Q value = 0.027

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 52 5 82
subtype1 16 1 12
subtype2 6 3 12
subtype3 16 1 9
subtype4 9 0 26
subtype5 5 0 23

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00226 (Chi-square test), Q value = 0.12

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 45 102
subtype1 8 23
subtype2 14 7
subtype3 9 19
subtype4 9 28
subtype5 5 25

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.206 (ANOVA), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 38 91.1 (11.3)
subtype1 6 81.7 (21.4)
subtype2 6 91.7 (7.5)
subtype3 2 95.0 (7.1)
subtype4 11 92.7 (7.9)
subtype5 13 93.1 (8.5)

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.402 (ANOVA), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 9 36.8 (56.2)
subtype2 1 15.0 (NA)
subtype3 3 72.7 (97.3)
subtype4 3 13.3 (10.4)
subtype5 2 29.0 (7.1)

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S20.  Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 32 59 47 25
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0356 (logrank test), Q value = 1

Table S21.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 17 0.0 - 194.8 (14.0)
subtype1 30 1 0.0 - 99.8 (16.5)
subtype2 55 12 0.1 - 194.8 (12.0)
subtype3 42 2 0.1 - 53.8 (12.2)
subtype4 23 2 0.1 - 129.9 (34.2)

Figure S18.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.752 (ANOVA), Q value = 1

Table S22.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 157 59.7 (12.5)
subtype1 30 61.0 (11.7)
subtype2 58 60.5 (14.0)
subtype3 45 58.4 (11.1)
subtype4 24 58.6 (12.9)

Figure S19.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.2e-07 (Chi-square test), Q value = 8e-06

Table S23.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 95 11 37 10
subtype1 18 5 7 0
subtype2 19 4 25 9
subtype3 37 2 4 0
subtype4 21 0 1 1

Figure S20.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 3.36e-07 (Chi-square test), Q value = 2.2e-05

Table S24.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 102 17 44
subtype1 20 5 7
subtype2 22 5 32
subtype3 38 6 3
subtype4 22 1 2

Figure S21.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.024 (Chi-square test), Q value = 1

Table S25.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 27 16 4
subtype1 8 1 0
subtype2 12 15 3
subtype3 5 0 0
subtype4 2 0 1

Figure S22.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0049 (Chi-square test), Q value = 0.26

Table S26.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 62 6 82
subtype1 12 0 18
subtype2 28 6 22
subtype3 11 0 30
subtype4 11 0 12

Figure S23.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00124 (Fisher's exact test), Q value = 0.07

Table S27.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 114
subtype1 5 27
subtype2 29 30
subtype3 9 38
subtype4 6 19

Figure S24.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0815 (ANOVA), Q value = 1

Table S28.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 39 88.7 (18.4)
subtype1 9 90.0 (8.7)
subtype2 9 75.6 (33.6)
subtype3 16 95.0 (8.2)
subtype4 5 90.0 (0.0)

Figure S25.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.302 (ANOVA), Q value = 1

Table S29.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 9 36.8 (56.2)
subtype1 3 11.0 (6.6)
subtype2 4 62.2 (82.3)
subtype3 1 25.0 (NA)
subtype4 1 24.0 (NA)

Figure S26.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S30.  Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 58 59 46
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0902 (logrank test), Q value = 1

Table S31.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 17 0.0 - 194.8 (14.0)
subtype1 51 3 0.0 - 194.8 (13.1)
subtype2 56 5 0.0 - 123.6 (18.0)
subtype3 43 9 0.1 - 92.6 (13.2)

Figure S27.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0299 (ANOVA), Q value = 1

Table S32.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 157 59.7 (12.5)
subtype1 55 59.6 (10.8)
subtype2 57 62.7 (12.9)
subtype3 45 56.1 (13.2)

Figure S28.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.69e-05 (Chi-square test), Q value = 0.001

Table S33.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 95 11 37 10
subtype1 42 5 5 1
subtype2 26 3 26 2
subtype3 27 3 6 7

Figure S29.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.000832 (Chi-square test), Q value = 0.048

Table S34.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 102 17 44
subtype1 45 8 5
subtype2 28 5 26
subtype3 29 4 13

Figure S30.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0179 (Chi-square test), Q value = 0.79

Table S35.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 27 16 4
subtype1 8 1 0
subtype2 16 8 1
subtype3 3 7 3

Figure S31.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0145 (Chi-square test), Q value = 0.68

Table S36.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 62 6 82
subtype1 15 0 36
subtype2 29 2 23
subtype3 18 4 23

Figure S32.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.000504 (Fisher's exact test), Q value = 0.03

Table S37.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 114
subtype1 10 48
subtype2 15 44
subtype3 24 22

Figure S33.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.5 (ANOVA), Q value = 1

Table S38.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 39 88.7 (18.4)
subtype1 16 91.9 (9.1)
subtype2 13 83.8 (26.0)
subtype3 10 90.0 (18.3)

Figure S34.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S39.  Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 57 53 53
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.167 (logrank test), Q value = 1

Table S40.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 17 0.0 - 194.8 (14.0)
subtype1 53 4 0.0 - 123.6 (18.2)
subtype2 50 9 0.1 - 194.8 (13.0)
subtype3 47 4 0.0 - 96.9 (13.2)

Figure S35.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.142 (ANOVA), Q value = 1

Table S41.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 157 59.7 (12.5)
subtype1 55 60.5 (11.9)
subtype2 53 57.0 (14.0)
subtype3 49 61.7 (11.1)

Figure S36.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00715 (Chi-square test), Q value = 0.37

Table S42.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 95 11 37 10
subtype1 28 5 19 1
subtype2 30 3 11 8
subtype3 37 3 7 1

Figure S37.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0441 (Chi-square test), Q value = 1

Table S43.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 102 17 44
subtype1 30 8 19
subtype2 32 3 18
subtype3 40 6 7

Figure S38.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0573 (Chi-square test), Q value = 1

Table S44.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 27 16 4
subtype1 14 4 1
subtype2 6 10 3
subtype3 7 2 0

Figure S39.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.311 (Chi-square test), Q value = 1

Table S45.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 62 6 82
subtype1 24 1 25
subtype2 22 4 27
subtype3 16 1 30

Figure S40.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0128 (Fisher's exact test), Q value = 0.62

Table S46.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 114
subtype1 10 47
subtype2 23 30
subtype3 16 37

Figure S41.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.196 (ANOVA), Q value = 1

Table S47.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 39 88.7 (18.4)
subtype1 11 89.1 (9.4)
subtype2 16 83.1 (26.5)
subtype3 12 95.8 (5.1)

Figure S42.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S48.  Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 25 63 75
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0993 (logrank test), Q value = 1

Table S49.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 17 0.0 - 194.8 (14.0)
subtype1 24 5 0.9 - 96.9 (21.9)
subtype2 58 9 0.1 - 194.8 (13.8)
subtype3 68 3 0.0 - 123.6 (13.6)

Figure S43.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.557 (ANOVA), Q value = 1

Table S50.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 157 59.7 (12.5)
subtype1 24 61.9 (12.7)
subtype2 62 58.7 (13.9)
subtype3 71 59.9 (11.1)

Figure S44.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.146 (Chi-square test), Q value = 1

Table S51.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 95 11 37 10
subtype1 11 2 9 2
subtype2 36 4 15 7
subtype3 48 5 13 1

Figure S45.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0409 (Chi-square test), Q value = 1

Table S52.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 102 17 44
subtype1 13 2 10
subtype2 37 4 22
subtype3 52 11 12

Figure S46.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.128 (Chi-square test), Q value = 1

Table S53.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 27 16 4
subtype1 6 5 1
subtype2 9 9 3
subtype3 12 2 0

Figure S47.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0347 (Chi-square test), Q value = 1

Table S54.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 62 6 82
subtype1 15 2 6
subtype2 23 3 35
subtype3 24 1 41

Figure S48.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0156 (Fisher's exact test), Q value = 0.72

Table S55.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 114
subtype1 4 21
subtype2 27 36
subtype3 18 57

Figure S49.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0852 (ANOVA), Q value = 1

Table S56.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 39 88.7 (18.4)
subtype1 4 70.0 (46.9)
subtype2 19 89.5 (13.5)
subtype3 16 92.5 (9.3)

Figure S50.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S57.  Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 53 58 52
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.242 (logrank test), Q value = 1

Table S58.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 17 0.0 - 194.8 (14.0)
subtype1 50 4 0.0 - 123.6 (17.0)
subtype2 55 10 0.1 - 194.8 (20.1)
subtype3 45 3 0.1 - 99.8 (12.8)

Figure S51.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.217 (ANOVA), Q value = 1

Table S59.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 157 59.7 (12.5)
subtype1 52 62.0 (11.4)
subtype2 58 57.8 (14.6)
subtype3 47 59.5 (10.5)

Figure S52.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0168 (Chi-square test), Q value = 0.76

Table S60.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 95 11 37 10
subtype1 26 3 18 1
subtype2 33 4 12 8
subtype3 36 4 7 1

Figure S53.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0375 (Chi-square test), Q value = 1

Table S61.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 102 17 44
subtype1 28 6 19
subtype2 35 4 19
subtype3 39 7 6

Figure S54.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.148 (Chi-square test), Q value = 1

Table S62.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 27 16 4
subtype1 13 3 1
subtype2 8 10 3
subtype3 6 3 0

Figure S55.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.187 (Chi-square test), Q value = 1

Table S63.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 62 6 82
subtype1 23 1 22
subtype2 25 4 29
subtype3 14 1 31

Figure S56.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0701 (Fisher's exact test), Q value = 1

Table S64.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 114
subtype1 13 40
subtype2 24 34
subtype3 12 40

Figure S57.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.305 (ANOVA), Q value = 1

Table S65.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 39 88.7 (18.4)
subtype1 9 94.4 (7.3)
subtype2 18 83.9 (25.0)
subtype3 12 91.7 (9.4)

Figure S58.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.437 (ANOVA), Q value = 1

Table S66.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 9 36.8 (56.2)
subtype1 3 72.7 (97.3)
subtype2 2 19.5 (6.4)
subtype3 4 18.5 (13.4)

Figure S59.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S67.  Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 58 85 20
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.00727 (logrank test), Q value = 0.37

Table S68.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 150 17 0.0 - 194.8 (14.0)
subtype1 53 10 0.2 - 194.8 (14.4)
subtype2 77 2 0.0 - 123.6 (13.8)
subtype3 20 5 0.1 - 86.8 (13.3)

Figure S60.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.802 (ANOVA), Q value = 1

Table S69.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 157 59.7 (12.5)
subtype1 57 59.0 (14.0)
subtype2 80 60.4 (10.9)
subtype3 20 59.0 (14.6)

Figure S61.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1.08e-05 (Chi-square test), Q value = 0.00068

Table S70.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 95 11 37 10
subtype1 34 4 11 8
subtype2 57 6 13 1
subtype3 4 1 13 1

Figure S62.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 9.65e-05 (Chi-square test), Q value = 0.0059

Table S71.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 102 17 44
subtype1 35 4 19
subtype2 61 12 12
subtype3 6 1 13

Figure S63.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0758 (Chi-square test), Q value = 1

Table S72.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 27 16 4
subtype1 8 9 3
subtype2 13 2 0
subtype3 6 5 1

Figure S64.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.107 (Chi-square test), Q value = 1

Table S73.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 62 6 82
subtype1 21 4 33
subtype2 30 1 44
subtype3 11 1 5

Figure S65.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0095 (Fisher's exact test), Q value = 0.48

Table S74.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 114
subtype1 26 32
subtype2 18 67
subtype3 5 15

Figure S66.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0419 (ANOVA), Q value = 1

Table S75.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 39 88.7 (18.4)
subtype1 16 90.0 (14.1)
subtype2 18 92.8 (8.9)
subtype3 5 70.0 (40.0)

Figure S67.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0568 (ANOVA), Q value = 1

Table S76.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 9 36.8 (56.2)
subtype1 4 24.5 (7.8)
subtype2 3 11.0 (6.6)
subtype3 2 100.0 (120.2)

Figure S68.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Methods & Data
Input

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

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

  • Number of patients = 163

  • Number of clustering approaches = 8

  • 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

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

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

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