Correlation between aggregated molecular cancer subtypes and selected clinical features
Esophageal Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1DR2THS
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DR2THS
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 10 clinical features across 174 patients, 48 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 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE', and 'RACE'.

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

  • CNMF clustering analysis on RPPA data identified 7 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_M_STAGE', and 'RACE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE', and 'RACE'.

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

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'RACE', and 'ETHNICITY'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE', and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE', and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'NUMBER_PACK_YEARS_SMOKED', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'NEOPLASM_DISEASESTAGE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'NUMBER_PACK_YEARS_SMOKED',  'RACE', and 'ETHNICITY'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 48 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		NEOPLASM
DISEASESTAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER NUMBER
PACK
YEARS
SMOKED 		RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test
Copy Number Ratio CNMF subtypes 0.177
(0.268) 		0.00015
(0.000652) 		0.0841
(0.15) 		0.00543
(0.016) 		0.336
(0.442) 		0.552
(0.65) 		0.128
(0.203) 		0.348
(0.447) 		1e-05
(7.14e-05) 		0.0882
(0.155)
METHLYATION CNMF 0.563
(0.655) 		0.000112
(0.00051) 		0.00039
(0.00139) 		0.0001
(5e-04) 		0.203
(0.299) 		0.908
(0.946) 		0.705
(0.792) 		0.106
(0.174) 		1e-05
(7.14e-05) 		0.0275
(0.0659)
RPPA CNMF subtypes 0.335
(0.442) 		0.0126
(0.034) 		0.118
(0.19) 		0.0329
(0.0749) 		0.427
(0.527) 		0.0408
(0.0868) 		0.835
(0.898) 		0.398
(0.497) 		0.00026
(0.00108)
RPPA cHierClus subtypes 0.319
(0.431) 		0.00118
(0.00393) 		0.0259
(0.0648) 		0.0112
(0.0312) 		0.0828
(0.15) 		0.0563
(0.112) 		0.347
(0.447) 		0.264
(0.369) 		1e-05
(7.14e-05)
RNAseq CNMF subtypes 0.55
(0.65) 		3.66e-06
(7.14e-05) 		0.00032
(0.00119) 		7e-05
(0.000368) 		0.09
(0.155) 		0.655
(0.744) 		0.491
(0.599) 		0.141
(0.221) 		1e-05
(7.14e-05) 		0.0402
(0.0868)
RNAseq cHierClus subtypes 0.869
(0.925) 		2.83e-05
(0.000187) 		3e-05
(0.000187) 		0.00325
(0.00985) 		0.00908
(0.0259) 		0.354
(0.448) 		0.952
(0.972) 		0.183
(0.273) 		1e-05
(7.14e-05) 		0.0277
(0.0659)
MIRSEQ CNMF 0.896
(0.944) 		7.74e-06
(7.14e-05) 		7e-05
(0.000368) 		0.00011
(0.00051) 		0.0917
(0.155) 		0.735
(0.816) 		0.938
(0.967) 		0.0508
(0.104) 		1e-05
(7.14e-05) 		0.0658
(0.124)
MIRSEQ CHIERARCHICAL 0.0142
(0.0372) 		5.98e-06
(7.14e-05) 		0.00029
(0.00112) 		0.00146
(0.00471) 		0.0763
(0.141) 		0.266
(0.369) 		0.507
(0.611) 		0.22
(0.319) 		1e-05
(7.14e-05) 		0.057
(0.112)
MIRseq Mature CNMF subtypes 0.28
(0.383) 		3.58e-05
(0.000211) 		0.00029
(0.00112) 		0.00315
(0.00984) 		0.164
(0.253) 		0.236
(0.337) 		0.766
(0.841) 		0.0493
(0.103) 		1e-05
(7.14e-05) 		0.0612
(0.118)
MIRseq Mature cHierClus subtypes 0.102
(0.171) 		1.1e-06
(7.14e-05) 		1e-05
(7.14e-05) 		0.00054
(0.00186) 		0.0157
(0.0403) 		0.625
(0.718) 		0.813
(0.883) 		0.039
(0.0866) 		1e-05
(7.14e-05) 		0.0324
(0.0749)
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 64 43 66
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.177 (logrank test), Q value = 0.27

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

nPatients nDeath Duration Range (Median), Month
ALL 171 71 0.1 - 122.1 (12.6)
subtype1 64 34 0.1 - 122.1 (12.8)
subtype2 42 12 0.1 - 83.2 (13.4)
subtype3 65 25 0.1 - 52.6 (12.5)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00015 (Kruskal-Wallis (anova)), Q value = 0.00065

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

nPatients Mean (Std.Dev)
ALL 173 62.6 (12.1)
subtype1 64 63.8 (12.4)
subtype2 43 67.7 (11.6)
subtype3 66 58.1 (10.6)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0841 (Fisher's exact test), Q value = 0.15

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 5 7 1 39 29 25 12 10 7 5 4
subtype1 4 2 3 0 5 11 12 3 3 2 2 2
subtype2 4 2 1 0 11 9 3 2 2 3 0 1
subtype3 0 1 3 1 23 9 10 7 5 2 3 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 = 0.00543 (Fisher's exact test), Q value = 0.016

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

nPatients T0+T1 T2 T3 T4
ALL 31 36 85 5
subtype1 16 7 30 0
subtype2 10 8 20 1
subtype3 5 21 35 4

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 = 0.336 (Fisher's exact test), Q value = 0.44

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

nPatients N0 N1 N2 N3
ALL 71 64 12 8
subtype1 18 28 5 2
subtype2 21 12 2 3
subtype3 32 24 5 3

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 = 0.552 (Fisher's exact test), Q value = 0.65

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

nPatients 0 1
ALL 126 9
subtype1 37 4
subtype2 32 1
subtype3 57 4

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 = 0.128 (Fisher's exact test), Q value = 0.2

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

nPatients FEMALE MALE
ALL 24 149
subtype1 6 58
subtype2 10 33
subtype3 8 58

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 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.348 (Kruskal-Wallis (anova)), Q value = 0.45

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

nPatients Mean (Std.Dev)
ALL 92 35.2 (21.9)
subtype1 32 35.9 (23.2)
subtype2 22 40.0 (24.1)
subtype3 38 31.7 (19.2)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 41 2 111
subtype1 4 0 45
subtype2 3 1 38
subtype3 34 1 28

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.0882 (Fisher's exact test), Q value = 0.15

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 81
subtype1 2 21
subtype2 1 14
subtype3 0 46

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 76 31 67
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.563 (logrank test), Q value = 0.65

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

nPatients nDeath Duration Range (Median), Month
ALL 172 72 0.1 - 122.1 (12.6)
subtype1 76 37 0.4 - 122.1 (13.1)
subtype2 30 10 0.1 - 83.2 (13.0)
subtype3 66 25 0.1 - 68.0 (12.4)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000112 (Kruskal-Wallis (anova)), Q value = 0.00051

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

nPatients Mean (Std.Dev)
ALL 174 62.6 (12.1)
subtype1 76 65.7 (11.7)
subtype2 31 64.9 (13.8)
subtype3 67 58.0 (10.3)

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

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00039 (Fisher's exact test), Q value = 0.0014

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 5 7 1 39 29 26 12 10 7 5 4
subtype1 8 2 3 0 4 14 14 2 4 4 2 2
subtype2 0 1 2 0 9 6 3 5 0 1 0 1
subtype3 0 2 2 1 26 9 9 5 6 2 3 1

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-04 (Fisher's exact test), Q value = 5e-04

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

nPatients T0+T1 T2 T3 T4
ALL 31 36 86 5
subtype1 23 7 33 0
subtype2 2 8 17 2
subtype3 6 21 36 3

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.203 (Fisher's exact test), Q value = 0.3

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

nPatients N0 N1 N2 N3
ALL 71 65 12 8
subtype1 21 34 4 4
subtype2 14 10 2 1
subtype3 36 21 6 3

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

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.908 (Fisher's exact test), Q value = 0.95

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

nPatients 0 1
ALL 127 9
subtype1 46 4
subtype2 24 1
subtype3 57 4

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.705 (Fisher's exact test), Q value = 0.79

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

nPatients FEMALE MALE
ALL 24 150
subtype1 9 67
subtype2 4 27
subtype3 11 56

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

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.106 (Kruskal-Wallis (anova)), Q value = 0.17

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

nPatients Mean (Std.Dev)
ALL 92 35.2 (21.9)
subtype1 37 41.3 (24.5)
subtype2 17 33.4 (20.9)
subtype3 38 29.9 (18.3)

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

'METHLYATION CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 41 2 111
subtype1 1 0 61
subtype2 7 0 22
subtype3 33 2 28

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.0275 (Fisher's exact test), Q value = 0.066

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 81
subtype1 3 20
subtype2 0 18
subtype3 0 43

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

Clustering Approach #3: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3 4 5 6 7
Number of samples 24 14 15 21 15 22 5
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.335 (logrank test), Q value = 0.44

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

nPatients nDeath Duration Range (Median), Month
ALL 114 40 0.1 - 122.1 (12.7)
subtype1 24 12 1.4 - 122.1 (17.4)
subtype2 14 3 0.1 - 60.4 (14.4)
subtype3 15 6 0.1 - 24.0 (10.0)
subtype4 20 6 0.1 - 44.8 (11.2)
subtype5 15 3 3.7 - 52.6 (13.2)
subtype6 21 8 0.8 - 68.0 (12.6)
subtype7 5 2 4.5 - 29.0 (20.0)

Figure S21.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0126 (Kruskal-Wallis (anova)), Q value = 0.034

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

nPatients Mean (Std.Dev)
ALL 116 62.9 (11.8)
subtype1 24 69.6 (10.3)
subtype2 14 66.4 (13.1)
subtype3 15 59.7 (9.4)
subtype4 21 59.5 (12.6)
subtype5 15 63.7 (10.4)
subtype6 22 58.1 (11.2)
subtype7 5 63.0 (11.6)

Figure S22.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.118 (Fisher's exact test), Q value = 0.19

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

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 6 3 5 1 34 22 17 10 8 2 3 1
subtype1 3 2 2 0 1 5 3 2 1 2 0 1
subtype2 2 0 0 1 6 3 2 0 0 0 0 0
subtype3 0 0 1 0 3 2 3 2 2 0 0 0
subtype4 1 0 1 0 10 2 3 2 2 0 0 0
subtype5 0 1 0 0 1 5 3 2 1 0 2 0
subtype6 0 0 1 0 10 5 3 2 1 0 0 0
subtype7 0 0 0 0 3 0 0 0 1 0 1 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0329 (Fisher's exact test), Q value = 0.075

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

nPatients T0+T1 T2 T3 T4
ALL 18 28 67 2
subtype1 10 4 9 0
subtype2 2 5 7 0
subtype3 2 3 10 0
subtype4 1 7 13 0
subtype5 2 1 11 1
subtype6 1 8 12 1
subtype7 0 0 5 0

Figure S24.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.427 (Fisher's exact test), Q value = 0.53

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

nPatients N0 N1 N2 N3
ALL 56 46 9 3
subtype1 7 11 3 2
subtype2 9 4 0 0
subtype3 5 9 1 0
subtype4 13 6 2 0
subtype5 6 7 1 1
subtype6 13 8 1 0
subtype7 3 1 1 0

Figure S25.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0408 (Fisher's exact test), Q value = 0.087

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

nPatients 0 1
ALL 97 4
subtype1 13 1
subtype2 14 0
subtype3 14 0
subtype4 20 0
subtype5 12 2
subtype6 21 0
subtype7 3 1

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.835 (Fisher's exact test), Q value = 0.9

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

nPatients FEMALE MALE
ALL 15 101
subtype1 4 20
subtype2 2 12
subtype3 2 13
subtype4 1 20
subtype5 3 12
subtype6 3 19
subtype7 0 5

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

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.398 (Kruskal-Wallis (anova)), Q value = 0.5

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

nPatients Mean (Std.Dev)
ALL 61 37.3 (20.9)
subtype1 12 40.2 (19.3)
subtype2 8 34.9 (22.8)
subtype3 8 47.8 (30.1)
subtype4 11 25.7 (9.5)
subtype5 8 42.6 (23.0)
subtype6 11 35.0 (21.1)
subtype7 3 41.3 (12.1)

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.00026 (Fisher's exact test), Q value = 0.0011

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

nPatients ASIAN WHITE
ALL 38 70
subtype1 0 20
subtype2 4 8
subtype3 5 9
subtype4 12 8
subtype5 3 12
subtype6 12 10
subtype7 2 3

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

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S33.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 23 19 20 23 31
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.319 (logrank test), Q value = 0.43

Table S34.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 114 40 0.1 - 122.1 (12.7)
subtype1 23 11 1.4 - 46.2 (18.4)
subtype2 19 5 0.5 - 122.1 (18.8)
subtype3 20 8 0.1 - 52.6 (11.6)
subtype4 21 7 0.1 - 42.9 (12.4)
subtype5 31 9 0.1 - 68.0 (12.6)

Figure S30.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00118 (Kruskal-Wallis (anova)), Q value = 0.0039

Table S35.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 116 62.9 (11.8)
subtype1 23 71.4 (9.4)
subtype2 19 63.7 (11.2)
subtype3 20 62.6 (9.7)
subtype4 23 58.3 (12.3)
subtype5 31 59.6 (11.7)

Figure S31.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0259 (Fisher's exact test), Q value = 0.065

Table S36.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 6 3 5 1 34 22 17 10 8 2 3 1
subtype1 3 2 2 0 1 4 3 2 2 2 0 0
subtype2 3 0 1 0 4 5 2 2 1 0 0 0
subtype3 0 0 0 0 3 5 4 2 2 0 2 1
subtype4 0 0 1 1 10 3 6 1 1 0 0 0
subtype5 0 1 1 0 16 5 2 3 2 0 1 0

Figure S32.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0112 (Fisher's exact test), Q value = 0.031

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

nPatients T0+T1 T2 T3 T4
ALL 18 28 67 2
subtype1 9 3 10 0
subtype2 4 6 9 0
subtype3 3 2 14 1
subtype4 0 7 16 0
subtype5 2 10 18 1

Figure S33.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0828 (Fisher's exact test), Q value = 0.15

Table S38.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 56 46 9 3
subtype1 6 10 4 2
subtype2 10 9 0 0
subtype3 7 10 2 1
subtype4 12 9 1 0
subtype5 21 8 2 0

Figure S34.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0563 (Fisher's exact test), Q value = 0.11

Table S39.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 97 4
subtype1 13 0
subtype2 19 0
subtype3 15 3
subtype4 23 0
subtype5 27 1

Figure S35.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.347 (Fisher's exact test), Q value = 0.45

Table S40.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 15 101
subtype1 4 19
subtype2 4 15
subtype3 1 19
subtype4 1 22
subtype5 5 26

Figure S36.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.264 (Kruskal-Wallis (anova)), Q value = 0.37

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 61 37.3 (20.9)
subtype1 11 37.2 (20.2)
subtype2 9 34.3 (20.9)
subtype3 12 48.1 (20.9)
subtype4 15 33.0 (25.7)
subtype5 14 34.7 (14.5)

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN WHITE
ALL 38 70
subtype1 0 19
subtype2 2 15
subtype3 5 14
subtype4 16 6
subtype5 15 16

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RACE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S43.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 84 65 25
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.55 (logrank test), Q value = 0.65

Table S44.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 172 72 0.1 - 122.1 (12.6)
subtype1 84 40 0.4 - 122.1 (13.1)
subtype2 63 22 0.1 - 47.9 (12.3)
subtype3 25 10 0.8 - 68.0 (13.6)

Figure S39.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 3.66e-06 (Kruskal-Wallis (anova)), Q value = 7.1e-05

Table S45.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 174 62.6 (12.1)
subtype1 84 66.6 (11.9)
subtype2 65 57.3 (10.8)
subtype3 25 63.0 (10.7)

Figure S40.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00032 (Fisher's exact test), Q value = 0.0012

Table S46.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 5 7 1 39 29 26 12 10 7 5 4
subtype1 8 2 2 0 5 16 14 4 4 5 2 3
subtype2 0 1 2 1 24 10 10 7 4 2 3 0
subtype3 0 2 3 0 10 3 2 1 2 0 0 1

Figure S41.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 7e-05 (Fisher's exact test), Q value = 0.00037

Table S47.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 31 36 86 5
subtype1 24 9 36 1
subtype2 3 17 40 4
subtype3 4 10 10 0

Figure S42.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.09 (Fisher's exact test), Q value = 0.16

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

nPatients N0 N1 N2 N3
ALL 71 65 12 8
subtype1 22 36 6 5
subtype2 36 20 4 3
subtype3 13 9 2 0

Figure S43.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.655 (Fisher's exact test), Q value = 0.74

Table S49.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 127 9
subtype1 49 5
subtype2 56 3
subtype3 22 1

Figure S44.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.491 (Fisher's exact test), Q value = 0.6

Table S50.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 24 150
subtype1 12 72
subtype2 7 58
subtype3 5 20

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

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.141 (Kruskal-Wallis (anova)), Q value = 0.22

Table S51.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 92 35.2 (21.9)
subtype1 38 40.8 (24.6)
subtype2 37 30.2 (18.7)
subtype3 17 33.2 (19.9)

Figure S46.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

Table S52.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 41 2 111
subtype1 1 0 67
subtype2 32 2 28
subtype3 8 0 16

Figure S47.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0402 (Fisher's exact test), Q value = 0.087

Table S53.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 81
subtype1 3 22
subtype2 0 48
subtype3 0 11

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S54.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 67 20 87
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.869 (logrank test), Q value = 0.92

Table S55.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 172 72 0.1 - 122.1 (12.6)
subtype1 67 32 1.4 - 122.1 (13.5)
subtype2 20 9 0.4 - 54.0 (8.3)
subtype3 85 31 0.1 - 68.0 (12.5)

Figure S49.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 2.83e-05 (Kruskal-Wallis (anova)), Q value = 0.00019

Table S56.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 174 62.6 (12.1)
subtype1 67 66.9 (11.7)
subtype2 20 65.5 (13.3)
subtype3 87 58.7 (10.8)

Figure S50.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00019

Table S57.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 5 7 1 39 29 26 12 10 7 5 4
subtype1 6 2 2 0 3 15 13 3 1 5 2 3
subtype2 2 0 1 0 2 2 1 1 3 0 0 0
subtype3 0 3 4 1 34 12 12 8 6 2 3 1

Figure S51.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00325 (Fisher's exact test), Q value = 0.0098

Table S58.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 31 36 86 5
subtype1 18 9 29 1
subtype2 6 2 7 0
subtype3 7 25 50 4

Figure S52.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00908 (Fisher's exact test), Q value = 0.026

Table S59.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 71 65 12 8
subtype1 17 31 3 5
subtype2 5 7 3 0
subtype3 49 27 6 3

Figure S53.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.354 (Fisher's exact test), Q value = 0.45

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

nPatients 0 1
ALL 127 9
subtype1 42 5
subtype2 9 0
subtype3 76 4

Figure S54.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.952 (Fisher's exact test), Q value = 0.97

Table S61.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 24 150
subtype1 10 57
subtype2 2 18
subtype3 12 75

Figure S55.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.183 (Kruskal-Wallis (anova)), Q value = 0.27

Table S62.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 92 35.2 (21.9)
subtype1 26 37.4 (26.0)
subtype2 14 43.8 (21.9)
subtype3 52 31.7 (19.1)

Figure S56.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

Table S63.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 41 2 111
subtype1 1 0 53
subtype2 0 0 17
subtype3 40 2 41

Figure S57.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0277 (Fisher's exact test), Q value = 0.066

Table S64.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 81
subtype1 2 14
subtype2 1 9
subtype3 0 58

Figure S58.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S65.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 84 78 11
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.896 (logrank test), Q value = 0.94

Table S66.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 171 72 0.1 - 122.1 (12.6)
subtype1 84 41 0.4 - 122.1 (13.1)
subtype2 77 29 0.1 - 68.0 (12.6)
subtype3 10 2 0.3 - 25.2 (9.0)

Figure S59.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 7.74e-06 (Kruskal-Wallis (anova)), Q value = 7.1e-05

Table S67.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 173 62.7 (12.1)
subtype1 84 66.6 (11.9)
subtype2 78 58.0 (10.3)
subtype3 11 66.3 (13.8)

Figure S60.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 7e-05 (Fisher's exact test), Q value = 0.00037

Table S68.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 5 7 1 38 29 26 12 10 7 5 4
subtype1 8 2 3 0 5 16 14 2 4 5 2 3
subtype2 0 2 3 1 31 11 11 6 6 2 3 1
subtype3 0 1 1 0 2 2 1 4 0 0 0 0

Figure S61.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00011 (Fisher's exact test), Q value = 0.00051

Table S69.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 31 36 85 5
subtype1 24 10 35 0
subtype2 6 24 44 3
subtype3 1 2 6 2

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0917 (Fisher's exact test), Q value = 0.16

Table S70.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 70 65 12 8
subtype1 22 36 6 5
subtype2 43 24 6 3
subtype3 5 5 0 0

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.735 (Fisher's exact test), Q value = 0.82

Table S71.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 126 9
subtype1 49 5
subtype2 68 4
subtype3 9 0

Figure S64.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.938 (Fisher's exact test), Q value = 0.97

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

nPatients FEMALE MALE
ALL 24 149
subtype1 11 73
subtype2 12 66
subtype3 1 10

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

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0508 (Kruskal-Wallis (anova)), Q value = 0.1

Table S73.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 91 35.4 (21.9)
subtype1 39 40.2 (24.6)
subtype2 45 29.2 (16.4)
subtype3 7 48.1 (27.4)

Figure S66.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

Table S74.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 40 2 111
subtype1 1 0 67
subtype2 37 2 35
subtype3 2 0 9

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0658 (Fisher's exact test), Q value = 0.12

Table S75.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 80
subtype1 3 22
subtype2 0 51
subtype3 0 7

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S76.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 62 26 54 31
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0142 (logrank test), Q value = 0.037

Table S77.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 171 72 0.1 - 122.1 (12.6)
subtype1 62 35 0.4 - 122.1 (11.5)
subtype2 26 7 3.0 - 83.2 (14.1)
subtype3 52 21 0.1 - 33.3 (12.2)
subtype4 31 9 0.8 - 68.0 (15.3)

Figure S69.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 5.98e-06 (Kruskal-Wallis (anova)), Q value = 7.1e-05

Table S78.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 173 62.7 (12.1)
subtype1 62 64.9 (11.9)
subtype2 26 71.2 (11.5)
subtype3 54 58.1 (11.6)
subtype4 31 59.3 (8.8)

Figure S70.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00029 (Fisher's exact test), Q value = 0.0011

Table S79.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 8 5 7 1 38 29 26 12 10 7 5 4
subtype1 7 2 3 0 1 10 12 1 3 3 1 3
subtype2 1 0 0 0 4 7 3 3 1 2 1 0
subtype3 0 2 2 1 18 9 9 5 4 2 1 0
subtype4 0 1 2 0 15 3 2 3 2 0 2 1

Figure S71.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00146 (Fisher's exact test), Q value = 0.0047

Table S80.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 31 36 85 5
subtype1 19 8 22 0
subtype2 5 3 15 1
subtype3 3 16 30 4
subtype4 4 9 18 0

Figure S72.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.0763 (Fisher's exact test), Q value = 0.14

Table S81.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 70 65 12 8
subtype1 14 28 4 3
subtype2 8 11 2 2
subtype3 28 17 5 2
subtype4 20 9 1 1

Figure S73.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.266 (Fisher's exact test), Q value = 0.37

Table S82.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 126 9
subtype1 33 4
subtype2 19 1
subtype3 48 1
subtype4 26 3

Figure S74.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.507 (Fisher's exact test), Q value = 0.61

Table S83.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 24 149
subtype1 7 55
subtype2 5 21
subtype3 6 48
subtype4 6 25

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.22 (Kruskal-Wallis (anova)), Q value = 0.32

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

nPatients Mean (Std.Dev)
ALL 91 35.4 (21.9)
subtype1 27 35.3 (20.5)
subtype2 13 48.5 (30.4)
subtype3 32 34.4 (19.5)
subtype4 19 28.1 (18.3)

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

Table S85.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 40 2 111
subtype1 1 0 48
subtype2 0 0 22
subtype3 25 2 25
subtype4 14 0 16

Figure S77.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.057 (Fisher's exact test), Q value = 0.11

Table S86.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 80
subtype1 3 17
subtype2 0 6
subtype3 0 39
subtype4 0 18

Figure S78.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S87.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 66 58 33 11
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.28 (logrank test), Q value = 0.38

Table S88.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 166 72 0.1 - 122.1 (12.5)
subtype1 66 34 0.4 - 122.1 (11.5)
subtype2 58 24 0.1 - 47.9 (12.5)
subtype3 32 9 0.1 - 83.2 (13.5)
subtype4 10 5 0.8 - 68.0 (8.2)

Figure S79.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 3.58e-05 (Kruskal-Wallis (anova)), Q value = 0.00021

Table S89.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 168 62.7 (12.2)
subtype1 66 66.1 (12.5)
subtype2 58 56.9 (10.3)
subtype3 33 66.5 (12.3)
subtype4 11 61.6 (8.2)

Figure S80.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00029 (Fisher's exact test), Q value = 0.0011

Table S90.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 7 5 6 1 38 28 25 12 10 7 5 4
subtype1 7 2 3 0 2 11 12 2 3 2 2 3
subtype2 0 2 2 1 23 8 8 1 6 2 3 1
subtype3 0 1 1 0 11 7 4 6 1 2 0 0
subtype4 0 0 0 0 2 2 1 3 0 1 0 0

Figure S81.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00315 (Fisher's exact test), Q value = 0.0098

Table S91.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 30 35 83 5
subtype1 20 8 26 0
subtype2 6 17 33 1
subtype3 4 7 19 3
subtype4 0 3 5 1

Figure S82.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.164 (Fisher's exact test), Q value = 0.25

Table S92.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 67 64 12 8
subtype1 16 31 5 2
subtype2 30 18 5 3
subtype3 17 12 1 2
subtype4 4 3 1 1

Figure S83.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.236 (Fisher's exact test), Q value = 0.34

Table S93.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 123 9
subtype1 36 5
subtype2 49 4
subtype3 30 0
subtype4 8 0

Figure S84.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.766 (Fisher's exact test), Q value = 0.84

Table S94.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 22 146
subtype1 10 56
subtype2 7 51
subtype3 3 30
subtype4 2 9

Figure S85.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0493 (Kruskal-Wallis (anova)), Q value = 0.1

Table S95.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 88 35.8 (22.0)
subtype1 31 37.8 (20.5)
subtype2 35 27.8 (16.8)
subtype3 19 46.1 (27.3)
subtype4 3 43.3 (30.6)

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

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

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 39 1 108
subtype1 1 0 50
subtype2 30 1 23
subtype3 6 0 26
subtype4 2 0 9

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

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.0612 (Fisher's exact test), Q value = 0.12

Table S97.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 77
subtype1 2 17
subtype2 0 39
subtype3 0 15
subtype4 1 6

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S98.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 62 22 84
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.102 (logrank test), Q value = 0.17

Table S99.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 166 72 0.1 - 122.1 (12.5)
subtype1 62 36 0.4 - 122.1 (11.5)
subtype2 22 6 3.0 - 83.2 (16.7)
subtype3 82 30 0.1 - 68.0 (12.4)

Figure S89.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.1e-06 (Kruskal-Wallis (anova)), Q value = 7.1e-05

Table S100.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 168 62.7 (12.2)
subtype1 62 64.9 (12.3)
subtype2 22 72.2 (9.2)
subtype3 84 58.6 (11.1)

Figure S90.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

Table S101.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 7 5 6 1 38 28 25 12 10 7 5 4
subtype1 6 2 3 0 1 11 13 1 3 3 1 3
subtype2 1 0 0 0 4 5 2 2 1 2 1 0
subtype3 0 3 3 1 33 12 10 9 6 2 3 1

Figure S91.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00054 (Fisher's exact test), Q value = 0.0019

Table S102.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T0+T1 T2 T3 T4
ALL 30 35 83 5
subtype1 19 8 23 0
subtype2 4 2 14 0
subtype3 7 25 46 5

Figure S92.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0157 (Fisher's exact test), Q value = 0.04

Table S103.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2 N3
ALL 67 64 12 8
subtype1 13 30 4 3
subtype2 8 8 2 2
subtype3 46 26 6 3

Figure S93.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.625 (Fisher's exact test), Q value = 0.72

Table S104.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 123 9
subtype1 34 4
subtype2 16 1
subtype3 73 4

Figure S94.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.813 (Fisher's exact test), Q value = 0.88

Table S105.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 22 146
subtype1 7 55
subtype2 3 19
subtype3 12 72

Figure S95.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.039 (Kruskal-Wallis (anova)), Q value = 0.087

Table S106.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 88 35.8 (22.0)
subtype1 27 35.8 (19.9)
subtype2 11 54.4 (29.4)
subtype3 50 31.7 (19.4)

Figure S96.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 7.1e-05

Table S107.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 39 1 108
subtype1 1 0 46
subtype2 0 0 20
subtype3 38 1 42

Figure S97.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0324 (Fisher's exact test), Q value = 0.075

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 77
subtype1 3 16
subtype2 0 5
subtype3 0 56

Figure S98.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/ESCA-TP/15092862/ESCA-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/ESCA-TP/15078602/ESCA-TP.merged_data.txt

  • Number of patients = 174

  • Number of clustering approaches = 10

  • Number of selected clinical features = 10

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

Download Results

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

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