Correlation between aggregated molecular cancer subtypes and selected clinical features
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 11 clinical features across 202 patients, 18 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 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.T.STAGE', and 'GENDER'.

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

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

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

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

  • 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.

  • 7 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 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 18 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 0.504
(1.00) 		0.00198
(0.144) 		0.0474
(1.00) 		0.206
(1.00) 		0.0409
(1.00) 		0.362
(1.00) 		0.0303
(1.00) 		0.0176
(1.00)
AGE Kruskal-Wallis (anova) 0.509
(1.00) 		0.092
(1.00) 		0.832
(1.00) 		0.0129
(0.814) 		0.487
(1.00) 		0.0606
(1.00) 		0.223
(1.00) 		0.281
(1.00)
NEOPLASM DISEASESTAGE Fisher's exact test 0.00028
(0.0224) 		1e-05
(0.00088) 		1e-05
(0.00088) 		0.00012
(0.00972) 		0.011
(0.714) 		0.0113
(0.726) 		0.00558
(0.385) 		1e-05
(0.00088)
PATHOLOGY T STAGE Fisher's exact test 0.00073
(0.0562) 		1e-05
(0.00088) 		1e-05
(0.00088) 		0.00195
(0.144) 		0.0455
(1.00) 		0.0101
(0.67) 		0.018
(1.00) 		2e-05
(0.00166)
PATHOLOGY N STAGE Fisher's exact test 0.0303
(1.00) 		0.00342
(0.243) 		0.0016
(0.12) 		0.00083
(0.0631) 		0.0333
(1.00) 		0.0267
(1.00) 		0.056
(1.00) 		0.00426
(0.298)
PATHOLOGY M STAGE Fisher's exact test 0.0375
(1.00) 		0.0005
(0.0395) 		0.0153
(0.949) 		0.00665
(0.452) 		0.359
(1.00) 		0.417
(1.00) 		0.392
(1.00) 		0.0965
(1.00)
GENDER Fisher's exact test 0.00263
(0.189) 		0.0168
(1.00) 		0.00052
(0.0406) 		8e-05
(0.00656) 		0.0797
(1.00) 		0.0549
(1.00) 		0.0896
(1.00) 		0.02
(1.00)
KARNOFSKY PERFORMANCE SCORE Kruskal-Wallis (anova) 0.353
(1.00) 		0.0235
(1.00) 		0.00828
(0.555) 		0.473
(1.00) 		0.0367
(1.00) 		0.289
(1.00) 		0.0583
(1.00) 		0.0604
(1.00)
NUMBERPACKYEARSSMOKED Kruskal-Wallis (anova) 0.949
(1.00) 		0.332
(1.00) 		0.245
(1.00) 		0.906
(1.00) 		0.578
(1.00) 		0.625
(1.00) 		0.342
(1.00) 		0.402
(1.00)
RACE Fisher's exact test 0.353
(1.00) 		0.381
(1.00) 		0.425
(1.00) 		0.933
(1.00) 		0.159
(1.00) 		0.0331
(1.00) 		0.544
(1.00) 		0.0922
(1.00)
ETHNICITY Fisher's exact test 0.416
(1.00) 		0.817
(1.00) 		0.56
(1.00) 		0.171
(1.00) 		0.772
(1.00) 		0.2
(1.00) 		0.396
(1.00) 		0.0498
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 76 93 29
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.504 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 173 7 2.0 - 5925.0 (508.0)
subtype1 70 1 2.0 - 5925.0 (623.0)
subtype2 77 5 3.0 - 3760.0 (510.0)
subtype3 26 1 2.0 - 2948.0 (265.0)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.509 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 195 59.9 (12.3)
subtype1 75 59.0 (10.9)
subtype2 92 60.6 (13.2)
subtype3 28 59.7 (13.2)

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 = 0.00028 (Fisher's exact test), Q value = 0.022

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 122 11 42 12
subtype1 57 4 7 1
subtype2 45 5 31 10
subtype3 20 2 4 1

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 = 0.00073 (Fisher's exact test), Q value = 0.056

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

nPatients T1 T2 T3+T4
ALL 130 18 50
subtype1 60 8 8
subtype2 50 7 36
subtype3 20 3 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 = 0.0303 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2
ALL 33 20 4
subtype1 11 1 0
subtype2 21 18 3
subtype3 1 1 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 = 0.0375 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 81 8 96
subtype1 30 0 39
subtype2 42 8 41
subtype3 9 0 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 = 0.00263 (Fisher's exact test), Q value = 0.19

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

nPatients FEMALE MALE
ALL 58 140
subtype1 12 64
subtype2 34 59
subtype3 12 17

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 = 0.353 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 52 91.2 (16.5)
subtype1 23 94.8 (7.9)
subtype2 19 86.3 (25.0)
subtype3 10 92.0 (7.9)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.949 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 24 30.2 (35.9)
subtype1 7 24.4 (16.8)
subtype2 13 35.2 (46.6)
subtype3 4 23.8 (19.3)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.353 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 49 129
subtype1 2 1 22 45
subtype2 0 2 23 63
subtype3 0 1 4 21

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.416 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 153
subtype1 2 63
subtype2 6 67
subtype3 1 23

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 44 54 88
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00198 (logrank test), Q value = 0.14

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

nPatients nDeath Duration Range (Median), Year
ALL 162 6 2.0 - 5925.0 (445.5)
subtype1 36 0 4.0 - 2816.0 (477.5)
subtype2 44 6 5.0 - 5925.0 (317.0)
subtype3 82 0 2.0 - 3950.0 (476.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.092 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 183 60.3 (12.5)
subtype1 43 63.0 (12.1)
subtype2 53 61.0 (14.7)
subtype3 87 58.6 (11.0)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00088

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 119 9 42 11
subtype1 33 2 6 2
subtype2 14 3 28 9
subtype3 72 4 8 0

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.00088

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

nPatients T1 T2 T3+T4
ALL 125 12 49
subtype1 34 1 9
subtype2 16 5 33
subtype3 75 6 7

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

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

P value = 0.00342 (Fisher's exact test), Q value = 0.24

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

nPatients N0 N1 N2
ALL 32 19 4
subtype1 9 2 1
subtype2 13 17 3
subtype3 10 0 0

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

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

P value = 5e-04 (Fisher's exact test), Q value = 0.039

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

nPatients M0 M1 MX
ALL 74 7 97
subtype1 24 1 16
subtype2 22 6 25
subtype3 28 0 56

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0168 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 55 131
subtype1 12 32
subtype2 24 30
subtype3 19 69

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

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

P value = 0.0235 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 53 93.2 (10.3)
subtype1 11 85.5 (16.3)
subtype2 9 95.6 (5.3)
subtype3 33 95.2 (7.6)

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

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.332 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 28 28.9 (33.9)
subtype1 4 67.5 (79.8)
subtype2 7 20.1 (11.1)
subtype3 17 23.4 (16.2)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.381 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 42 123
subtype1 1 3 12 25
subtype2 0 1 12 38
subtype3 1 1 18 60

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

'METHLYATION CNMF' versus 'ETHNICITY'

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

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 141
subtype1 1 33
subtype2 3 37
subtype3 5 71

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 42 65 62 28
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0474 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 172 7 2.0 - 5925.0 (510.5)
subtype1 37 0 2.0 - 3035.0 (595.0)
subtype2 52 6 4.0 - 5925.0 (397.0)
subtype3 59 1 2.0 - 1726.0 (436.0)
subtype4 24 0 2.0 - 3950.0 (750.5)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.832 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 194 59.9 (12.3)
subtype1 42 61.5 (11.7)
subtype2 64 59.5 (13.8)
subtype3 61 59.4 (11.2)
subtype4 27 59.3 (12.2)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00088

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 120 12 42 12
subtype1 25 5 9 0
subtype2 20 4 28 11
subtype3 50 3 4 0
subtype4 25 0 1 1

Figure S25.  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 = 1e-05 (Fisher's exact test), Q value = 0.00088

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

nPatients T1 T2 T3+T4
ALL 128 19 50
subtype1 28 5 9
subtype2 23 6 36
subtype3 52 7 3
subtype4 25 1 2

Figure S26.  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 = 0.0016 (Fisher's exact test), Q value = 0.12

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

nPatients N0 N1 N2
ALL 32 20 4
subtype1 11 2 0
subtype2 12 18 3
subtype3 7 0 0
subtype4 2 0 1

Figure S27.  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 = 0.0153 (Fisher's exact test), Q value = 0.95

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

nPatients M0 M1 MX
ALL 83 8 93
subtype1 17 0 22
subtype2 30 8 24
subtype3 24 0 32
subtype4 12 0 15

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00052 (Fisher's exact test), Q value = 0.041

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

nPatients FEMALE MALE
ALL 58 139
subtype1 7 35
subtype2 32 33
subtype3 13 49
subtype4 6 22

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

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

P value = 0.00828 (Kruskal-Wallis (anova)), Q value = 0.55

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

nPatients Mean (Std.Dev)
ALL 54 91.5 (16.3)
subtype1 11 91.8 (8.7)
subtype2 12 80.8 (30.3)
subtype3 25 96.8 (6.9)
subtype4 6 90.0 (0.0)

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

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.245 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 25 30.7 (35.5)
subtype1 4 13.2 (7.0)
subtype2 6 55.5 (64.9)
subtype3 12 25.8 (17.7)
subtype4 3 24.3 (9.5)

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

'RNAseq CNMF subtypes' versus 'RACE'

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

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 47 130
subtype1 1 0 8 31
subtype2 0 3 14 45
subtype3 1 0 18 37
subtype4 0 1 7 17

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.56 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 152
subtype1 1 34
subtype2 4 46
subtype3 2 53
subtype4 2 19

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 67 64 66
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.206 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 172 7 2.0 - 5925.0 (510.5)
subtype1 59 2 4.0 - 3760.0 (595.0)
subtype2 52 4 2.0 - 2816.0 (405.5)
subtype3 61 1 2.0 - 5925.0 (508.0)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0129 (Kruskal-Wallis (anova)), Q value = 0.81

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

nPatients Mean (Std.Dev)
ALL 194 59.9 (12.3)
subtype1 66 62.4 (12.4)
subtype2 63 55.9 (12.6)
subtype3 65 61.1 (11.0)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00012 (Fisher's exact test), Q value = 0.0097

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 120 12 42 12
subtype1 34 3 25 2
subtype2 38 3 11 9
subtype3 48 6 6 1

Figure S36.  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 = 0.00195 (Fisher's exact test), Q value = 0.14

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

nPatients T1 T2 T3+T4
ALL 128 19 50
subtype1 37 5 25
subtype2 40 5 19
subtype3 51 9 6

Figure S37.  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 = 0.00083 (Fisher's exact test), Q value = 0.063

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

nPatients N0 N1 N2
ALL 32 20 4
subtype1 19 7 1
subtype2 4 12 3
subtype3 9 1 0

Figure S38.  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 = 0.00665 (Fisher's exact test), Q value = 0.45

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

nPatients M0 M1 MX
ALL 83 8 93
subtype1 35 2 24
subtype2 26 6 30
subtype3 22 0 39

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 8e-05 (Fisher's exact test), Q value = 0.0066

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

nPatients FEMALE MALE
ALL 58 139
subtype1 16 51
subtype2 32 32
subtype3 10 56

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

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

P value = 0.473 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 54 91.5 (16.3)
subtype1 14 92.9 (7.3)
subtype2 16 85.6 (27.1)
subtype3 24 94.6 (8.3)

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

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.906 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 25 30.7 (35.5)
subtype1 7 43.3 (63.2)
subtype2 9 22.7 (13.2)
subtype3 9 29.0 (19.6)

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.933 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 4 47 130
subtype1 1 1 15 46
subtype2 0 2 14 42
subtype3 1 1 18 42

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.171 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 152
subtype1 3 49
subtype2 5 46
subtype3 1 57

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

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3
Number of samples 67 65 70
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0409 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 177 7 2.0 - 5925.0 (494.0)
subtype1 58 0 2.0 - 3760.0 (526.5)
subtype2 60 1 2.0 - 2816.0 (429.5)
subtype3 59 6 2.0 - 5925.0 (649.0)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.487 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 199 60.1 (12.4)
subtype1 66 61.7 (11.7)
subtype2 63 60.0 (10.8)
subtype3 70 58.7 (14.2)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.011 (Fisher's exact test), Q value = 0.71

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 124 12 43 12
subtype1 37 6 19 1
subtype2 48 2 8 2
subtype3 39 4 16 9

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

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

P value = 0.0455 (Fisher's exact test), Q value = 1

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

nPatients T1 T2 T3+T4
ALL 132 19 51
subtype1 39 9 19
subtype2 51 5 9
subtype3 42 5 23

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

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

P value = 0.0333 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2
ALL 34 20 4
subtype1 17 4 1
subtype2 8 3 0
subtype3 9 13 3

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

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

P value = 0.359 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 84 8 97
subtype1 30 1 28
subtype2 23 2 36
subtype3 31 5 33

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0797 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 59 143
subtype1 13 54
subtype2 21 44
subtype3 25 45

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

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

P value = 0.0367 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 54 91.5 (16.3)
subtype1 12 92.5 (7.5)
subtype2 20 96.5 (7.5)
subtype3 22 86.4 (23.2)

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

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.578 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 28 28.9 (33.9)
subtype1 6 15.5 (5.6)
subtype2 13 28.6 (18.3)
subtype3 9 38.1 (56.0)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.159 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 49 132
subtype1 0 1 23 38
subtype2 1 2 10 47
subtype3 1 2 16 47

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.772 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 157
subtype1 2 52
subtype2 4 51
subtype3 3 54

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

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S61.  Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 95 43 64
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.362 (logrank test), Q value = 1

Table S62.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 177 7 2.0 - 5925.0 (494.0)
subtype1 83 3 2.0 - 3760.0 (494.0)
subtype2 42 0 2.0 - 1636.0 (476.5)
subtype3 52 4 4.0 - 5925.0 (576.5)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0606 (Kruskal-Wallis (anova)), Q value = 1

Table S63.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 199 60.1 (12.4)
subtype1 94 60.6 (11.5)
subtype2 42 63.5 (11.3)
subtype3 63 57.2 (13.8)

Figure S57.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0113 (Fisher's exact test), Q value = 0.73

Table S64.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 124 12 43 12
subtype1 58 5 21 3
subtype2 34 3 5 0
subtype3 32 4 17 9

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

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

P value = 0.0101 (Fisher's exact test), Q value = 0.67

Table S65.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 132 19 51
subtype1 63 10 22
subtype2 35 4 4
subtype3 34 5 25

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

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

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

Table S66.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 34 20 4
subtype1 17 7 1
subtype2 7 0 0
subtype3 10 13 3

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

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

P value = 0.417 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 84 8 97
subtype1 39 3 42
subtype2 18 0 24
subtype3 27 5 31

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0549 (Fisher's exact test), Q value = 1

Table S68.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 59 143
subtype1 22 73
subtype2 11 32
subtype3 26 38

Figure S62.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.289 (Kruskal-Wallis (anova)), Q value = 1

Table S69.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 54 91.5 (16.3)
subtype1 26 90.0 (20.2)
subtype2 12 96.7 (4.9)
subtype3 16 90.0 (14.6)

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.625 (Kruskal-Wallis (anova)), Q value = 1

Table S70.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 28 28.9 (33.9)
subtype1 9 20.3 (13.8)
subtype2 9 30.3 (19.2)
subtype3 10 35.2 (53.3)

Figure S64.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0331 (Fisher's exact test), Q value = 1

Table S71.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 49 132
subtype1 1 1 31 55
subtype2 1 1 5 33
subtype3 0 3 13 44

Figure S65.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.2 (Fisher's exact test), Q value = 1

Table S72.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 157
subtype1 2 75
subtype2 2 36
subtype3 5 46

Figure S66.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S73.  Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 68 62 72
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0303 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Year
ALL 177 7 2.0 - 5925.0 (494.0)
subtype1 61 0 2.0 - 3760.0 (493.0)
subtype2 56 1 2.0 - 2816.0 (421.0)
subtype3 60 6 4.0 - 5925.0 (654.0)

Figure S67.  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 = 0.223 (Kruskal-Wallis (anova)), Q value = 1

Table S75.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 199 60.1 (12.4)
subtype1 67 62.0 (11.6)
subtype2 60 60.6 (10.6)
subtype3 72 58.0 (14.2)

Figure S68.  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 = 0.00558 (Fisher's exact test), Q value = 0.39

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

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 124 12 43 12
subtype1 36 5 21 1
subtype2 45 3 8 1
subtype3 43 4 14 10

Figure S69.  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 = 0.018 (Fisher's exact test), Q value = 1

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

nPatients T1 T2 T3+T4
ALL 132 19 51
subtype1 39 8 21
subtype2 48 7 7
subtype3 45 4 23

Figure S70.  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 = 0.056 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2
ALL 34 20 4
subtype1 18 4 1
subtype2 6 3 0
subtype3 10 13 3

Figure S71.  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 = 0.392 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 84 8 97
subtype1 27 1 32
subtype2 25 1 31
subtype3 32 6 34

Figure S72.  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 = 0.0896 (Fisher's exact test), Q value = 1

Table S80.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 59 143
subtype1 16 52
subtype2 15 47
subtype3 28 44

Figure S73.  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 = 0.0583 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 54 91.5 (16.3)
subtype1 13 93.1 (7.5)
subtype2 17 96.5 (7.9)
subtype3 24 87.1 (22.4)

Figure S74.  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 = 0.342 (Kruskal-Wallis (anova)), Q value = 1

Table S82.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 28 28.9 (33.9)
subtype1 9 32.6 (57.5)
subtype2 12 30.3 (17.9)
subtype3 7 21.6 (10.2)

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.544 (Fisher's exact test), Q value = 1

Table S83.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 49 132
subtype1 0 1 21 42
subtype2 1 1 11 43
subtype3 1 3 17 47

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

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.396 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 157
subtype1 1 53
subtype2 4 49
subtype3 4 55

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

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S85.  Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 23 31 33 27 33 37 18
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0176 (logrank test), Q value = 1

Table S86.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Year
ALL 177 7 2.0 - 5925.0 (494.0)
subtype1 21 0 2.0 - 3035.0 (510.0)
subtype2 25 2 26.0 - 2639.0 (600.0)
subtype3 32 1 2.0 - 3760.0 (375.5)
subtype4 27 0 2.0 - 2816.0 (516.0)
subtype5 28 2 4.0 - 5925.0 (644.5)
subtype6 33 0 2.0 - 2072.0 (586.0)
subtype7 11 2 7.0 - 1967.0 (97.0)

Figure S78.  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 = 0.281 (Kruskal-Wallis (anova)), Q value = 1

Table S87.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 199 60.1 (12.4)
subtype1 22 63.0 (12.0)
subtype2 31 62.7 (12.7)
subtype3 32 59.8 (10.5)
subtype4 27 59.2 (11.3)
subtype5 33 62.6 (12.6)
subtype6 37 57.9 (11.1)
subtype7 17 53.9 (17.3)

Figure S79.  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 = 1e-05 (Fisher's exact test), Q value = 0.00088

Table S88.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 124 12 43 12
subtype1 16 1 4 0
subtype2 12 2 14 3
subtype3 24 2 4 0
subtype4 24 0 1 0
subtype5 18 1 8 6
subtype6 26 3 4 0
subtype7 4 3 8 3

Figure S80.  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 = 2e-05 (Fisher's exact test), Q value = 0.0017

Table S89.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 132 19 51
subtype1 18 2 3
subtype2 13 3 15
subtype3 24 5 4
subtype4 25 1 1
subtype5 18 1 14
subtype6 29 4 4
subtype7 5 3 10

Figure S81.  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 = 0.00426 (Fisher's exact test), Q value = 0.3

Table S90.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 34 20 4
subtype1 5 1 0
subtype2 11 6 1
subtype3 2 0 0
subtype4 2 0 0
subtype5 8 4 3
subtype6 5 0 0
subtype7 1 9 0

Figure S82.  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 = 0.0965 (Fisher's exact test), Q value = 1

Table S91.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 84 8 97
subtype1 9 0 10
subtype2 16 3 11
subtype3 15 0 15
subtype4 8 0 19
subtype5 16 2 14
subtype6 14 0 19
subtype7 6 3 9

Figure S83.  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 = 0.02 (Fisher's exact test), Q value = 1

Table S92.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 59 143
subtype1 6 17
subtype2 9 22
subtype3 7 26
subtype4 10 17
subtype5 11 22
subtype6 5 32
subtype7 11 7

Figure S84.  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 = 0.0604 (Kruskal-Wallis (anova)), Q value = 1

Table S93.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 54 91.5 (16.3)
subtype1 4 100.0 (0.0)
subtype2 6 76.7 (38.3)
subtype3 10 96.0 (9.7)
subtype4 6 95.0 (5.5)
subtype5 10 93.0 (6.7)
subtype6 13 93.8 (7.7)
subtype7 5 80.0 (22.4)

Figure S85.  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 = 0.402 (Kruskal-Wallis (anova)), Q value = 1

Table S94.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 28 28.9 (33.9)
subtype1 2 12.5 (10.6)
subtype2 4 23.2 (11.4)
subtype3 6 35.8 (20.4)
subtype4 9 17.6 (8.8)
subtype5 4 57.2 (85.2)
subtype6 2 30.0 (28.3)
subtype7 1 28.0 (NA)

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0922 (Fisher's exact test), Q value = 1

Table S95.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 5 49 132
subtype1 0 0 10 11
subtype2 0 0 6 24
subtype3 1 1 10 18
subtype4 0 0 4 21
subtype5 0 3 5 24
subtype6 1 0 8 25
subtype7 0 1 6 9

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0498 (Fisher's exact test), Q value = 1

Table S96.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 9 157
subtype1 0 19
subtype2 1 23
subtype3 0 28
subtype4 5 20
subtype5 1 25
subtype6 1 29
subtype7 1 13

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

Methods & Data
Input

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

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

  • Number of patients = 202

  • Number of clustering approaches = 8

  • Number of selected clinical features = 11

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

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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