Correlation between aggregated molecular cancer subtypes and selected clinical features
Stomach Adenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1N29VF7
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1N29VF7
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 11 clinical features across 308 patients, 12 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 6 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.T.STAGE'.

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY.T.STAGE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'PATHOLOGY.T.STAGE' and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE' and 'COMPLETENESS.OF.RESECTION'.

  • 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 correlate to 'HISTOLOGICAL.TYPE'.

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 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, 12 significant findings detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		RPPA
CNMF
subtypes 		RPPA
cHierClus
subtypes 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.811
(1.00) 		0.532
(1.00) 		0.636
(1.00) 		0.274
(1.00) 		0.116
(1.00) 		0.166
(1.00) 		0.0558
(1.00) 		0.819
(1.00) 		0.0117
(0.998) 		0.77
(1.00)
AGE ANOVA 0.0644
(1.00) 		0.0037
(0.35) 		0.0614
(1.00) 		0.0266
(1.00) 		0.0134
(1.00) 		0.00157
(0.159) 		0.00717
(0.64) 		0.0236
(1.00) 		0.00307
(0.301) 		0.00772
(0.68)
NEOPLASM DISEASESTAGE Chi-square test 0.245
(1.00) 		0.272
(1.00) 		0.19
(1.00) 		0.0612
(1.00) 		0.0278
(1.00) 		0.031
(1.00) 		0.357
(1.00) 		0.507
(1.00) 		0.0166
(1.00) 		0.038
(1.00)
PATHOLOGY T STAGE Chi-square test 0.000811
(0.0851) 		0.00711
(0.64) 		0.00968
(0.833) 		0.00112
(0.114) 		0.000243
(0.026) 		3.42e-05
(0.00376) 		0.00401
(0.373) 		0.0258
(1.00) 		0.00923
(0.803) 		0.0025
(0.248)
PATHOLOGY N STAGE Chi-square test 0.0241
(1.00) 		0.809
(1.00) 		0.886
(1.00) 		0.377
(1.00) 		0.109
(1.00) 		0.197
(1.00) 		0.17
(1.00) 		0.573
(1.00) 		0.542
(1.00) 		0.236
(1.00)
PATHOLOGY M STAGE Chi-square test 0.649
(1.00) 		0.152
(1.00) 		0.354
(1.00) 		0.0625
(1.00) 		0.102
(1.00) 		0.203
(1.00) 		0.118
(1.00) 		0.987
(1.00) 		0.174
(1.00) 		0.731
(1.00)
GENDER Fisher's exact test 0.0693
(1.00) 		0.964
(1.00) 		0.219
(1.00) 		0.491
(1.00) 		0.827
(1.00) 		0.459
(1.00) 		0.378
(1.00) 		0.861
(1.00) 		0.903
(1.00) 		0.86
(1.00)
HISTOLOGICAL TYPE Chi-square test 0.416
(1.00) 		0.0036
(0.345) 		0.0187
(1.00) 		0.122
(1.00) 		0.00589
(0.542) 		0.000149
(0.0161) 		0.000243
(0.026) 		0.00598
(0.544) 		8.06e-05
(0.00878) 		0.001
(0.104)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.00321
(0.311) 		0.0168
(1.00) 		0.0517
(1.00) 		0.0712
(1.00) 		0.00174
(0.174) 		0.622
(1.00) 		0.458
(1.00) 		0.0218
(1.00) 		0.224
(1.00) 		0.162
(1.00)
COMPLETENESS OF RESECTION Chi-square test 0.931
(1.00) 		0.338
(1.00) 		0.361
(1.00) 		0.259
(1.00) 		0.54
(1.00) 		0.998
(1.00) 		0.00106
(0.109) 		0.5
(1.00) 		0.00368
(0.35) 		0.532
(1.00)
NUMBER OF LYMPH NODES ANOVA 0.24
(1.00) 		0.638
(1.00) 		0.703
(1.00) 		0.368
(1.00) 		0.538
(1.00) 		0.756
(1.00) 		0.762
(1.00) 		0.979
(1.00) 		0.661
(1.00) 		0.373
(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 4 5 6
Number of samples 141 25 49 12 76 3
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.811 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 274 60 0.1 - 105.1 (8.8)
subtype1 129 29 0.1 - 105.1 (8.4)
subtype2 23 6 0.1 - 79.1 (12.7)
subtype3 41 8 0.1 - 21.2 (3.0)
subtype4 9 1 0.2 - 24.2 (14.0)
subtype5 70 16 0.1 - 72.2 (10.9)
subtype6 2 0 0.1 - 0.7 (0.4)

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 = 0.0644 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 299 66.1 (10.7)
subtype1 139 64.9 (11.5)
subtype2 25 68.9 (7.9)
subtype3 47 64.3 (10.7)
subtype4 11 70.0 (8.9)
subtype5 75 68.1 (9.7)
subtype6 2 57.5 (0.7)

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 = 0.245 (Chi-square test), Q value = 1

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
ALL 2 11 27 28 31 45 3 47 38 29 29
subtype1 1 3 13 12 16 21 2 17 21 15 13
subtype2 0 2 3 1 3 3 0 3 4 2 3
subtype3 0 0 1 7 3 9 1 11 5 5 5
subtype4 1 2 1 0 0 0 0 2 3 1 0
subtype5 0 4 9 8 8 12 0 12 5 6 8
subtype6 0 0 0 0 1 0 0 2 0 0 0

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 = 0.000811 (Chi-square test), Q value = 0.085

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

nPatients T1 T2 T3 T4
ALL 13 75 124 84
subtype1 4 33 53 46
subtype2 2 6 13 4
subtype3 0 11 21 15
subtype4 4 3 2 2
subtype5 3 22 33 16
subtype6 0 0 2 1

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 = 0.0241 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 97 87 55 55
subtype1 44 42 24 26
subtype2 11 0 6 7
subtype3 9 21 12 7
subtype4 4 0 2 4
subtype5 28 22 11 11
subtype6 1 2 0 0

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 = 0.649 (Chi-square test), Q value = 1

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

nPatients M0 M1 MX
ALL 271 20 15
subtype1 123 12 6
subtype2 24 1 0
subtype3 44 3 2
subtype4 12 0 0
subtype5 65 4 7
subtype6 3 0 0

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 = 0.0693 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 120 186
subtype1 63 78
subtype2 5 20
subtype3 19 30
subtype4 7 5
subtype5 26 50
subtype6 0 3

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 'HISTOLOGICAL.TYPE'

P value = 0.416 (Chi-square test), Q value = 1

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

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 52 143 45 40 15 6 3
subtype1 30 70 18 12 8 1 2
subtype2 4 10 5 4 1 1 0
subtype3 12 22 5 7 2 0 0
subtype4 2 4 3 3 0 0 0
subtype5 3 36 14 13 4 4 1
subtype6 1 1 0 1 0 0 0

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00321 (Chi-square test), Q value = 0.31

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

nPatients NO YES
ALL 7 299
subtype1 1 140
subtype2 0 25
subtype3 2 47
subtype4 1 11
subtype5 2 74
subtype6 1 2

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.931 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 241 10 12 25
subtype1 113 3 6 12
subtype2 20 2 1 1
subtype3 37 1 2 5
subtype4 10 0 0 2
subtype5 60 3 3 5
subtype6 1 1 0 0

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.24 (ANOVA), Q value = 1

Table S12.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 267 5.0 (7.5)
subtype1 128 4.1 (5.2)
subtype2 21 7.4 (9.7)
subtype3 40 5.9 (7.3)
subtype4 10 7.0 (7.6)
subtype5 66 5.3 (10.2)
subtype6 2 2.0 (0.0)

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 37 81 65 77
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.532 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 250 47 0.1 - 105.1 (8.0)
subtype1 37 6 0.1 - 24.7 (11.7)
subtype2 77 10 0.1 - 65.1 (7.0)
subtype3 63 13 0.1 - 105.1 (3.7)
subtype4 73 18 0.1 - 79.1 (10.2)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0037 (ANOVA), Q value = 0.35

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

nPatients Mean (Std.Dev)
ALL 253 65.3 (10.6)
subtype1 36 63.6 (10.2)
subtype2 79 67.3 (10.4)
subtype3 65 61.7 (11.0)
subtype4 73 67.0 (9.8)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.272 (Chi-square test), Q value = 1

Table S16.  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
ALL 2 10 21 25 32 44 2 41 37 28 18
subtype1 0 1 1 0 5 8 0 5 8 7 2
subtype2 2 5 10 7 12 12 0 10 12 8 3
subtype3 0 1 5 7 8 8 2 11 9 7 7
subtype4 0 3 5 11 7 16 0 15 8 6 6

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 = 0.00711 (Chi-square test), Q value = 0.64

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

nPatients T1 T2 T3 T4
ALL 11 60 114 75
subtype1 1 2 17 17
subtype2 7 21 35 18
subtype3 1 14 25 25
subtype4 2 23 37 15

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 = 0.809 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 91 69 49 50
subtype1 13 9 5 10
subtype2 32 19 14 16
subtype3 21 17 16 10
subtype4 25 24 14 14

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 = 0.152 (Chi-square test), Q value = 1

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

nPatients M0 M1 MX
ALL 235 13 12
subtype1 33 2 2
subtype2 77 2 2
subtype3 58 6 1
subtype4 67 3 7

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.964 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 98 162
subtype1 13 24
subtype2 30 51
subtype3 26 39
subtype4 29 48

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0036 (Chi-square test), Q value = 0.35

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 47 115 36 39 14 6 3
subtype1 10 17 3 6 0 1 0
subtype2 10 38 11 15 5 1 1
subtype3 21 26 8 2 6 0 2
subtype4 6 34 14 16 3 4 0

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

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 253
subtype1 0 37
subtype2 1 80
subtype3 0 65
subtype4 6 71

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

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.338 (Chi-square test), Q value = 1

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 227 9 5
subtype1 35 0 1
subtype2 74 1 1
subtype3 54 5 1
subtype4 64 3 2

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

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.638 (ANOVA), Q value = 1

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 233 4.8 (7.3)
subtype1 35 5.6 (9.3)
subtype2 72 4.3 (6.2)
subtype3 59 4.2 (5.2)
subtype4 67 5.5 (8.8)

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

Clustering Approach #3: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 82 91 91
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.636 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 233 51 0.1 - 105.1 (7.8)
subtype1 71 17 0.1 - 105.1 (7.5)
subtype2 84 17 0.1 - 65.1 (5.7)
subtype3 78 17 0.1 - 79.1 (11.1)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0614 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 257 65.9 (10.9)
subtype1 80 66.7 (10.6)
subtype2 91 63.7 (11.7)
subtype3 86 67.3 (10.0)

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

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.19 (Chi-square test), Q value = 1

Table S28.  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
ALL 2 6 24 29 25 35 3 39 35 25 25
subtype1 0 0 8 4 10 10 1 15 13 9 6
subtype2 0 2 3 13 10 14 1 13 11 11 10
subtype3 2 4 13 12 5 11 1 11 11 5 9

Figure S25.  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 = 0.00968 (Chi-square test), Q value = 0.83

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

nPatients T1 T2 T3 T4
ALL 8 66 104 76
subtype1 0 19 38 22
subtype2 2 16 37 33
subtype3 6 31 29 21

Figure S26.  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 = 0.886 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 86 75 43 48
subtype1 26 23 14 15
subtype2 26 29 16 18
subtype3 34 23 13 15

Figure S27.  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 = 0.354 (Chi-square test), Q value = 1

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

nPatients M0 M1 MX
ALL 237 16 11
subtype1 72 4 6
subtype2 84 6 1
subtype3 81 6 4

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.219 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 100 164
subtype1 34 48
subtype2 38 53
subtype3 28 63

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0187 (Chi-square test), Q value = 1

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 42 135 38 26 15 4 2
subtype1 9 45 13 11 3 0 1
subtype2 19 44 9 4 11 1 1
subtype3 14 46 16 11 1 3 0

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0517 (Fisher's exact test), Q value = 1

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 257
subtype1 2 80
subtype2 0 91
subtype3 5 86

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.361 (Chi-square test), Q value = 1

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 206 8 8 25
subtype1 64 3 1 11
subtype2 72 4 2 7
subtype3 70 1 5 7

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

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

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 224 4.7 (6.3)
subtype1 71 4.4 (6.4)
subtype2 81 4.6 (4.8)
subtype3 72 5.2 (7.6)

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RPPA cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 106 46 112
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.274 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 233 51 0.1 - 105.1 (7.8)
subtype1 87 19 0.1 - 72.2 (9.0)
subtype2 43 8 0.1 - 105.1 (11.7)
subtype3 103 24 0.1 - 79.1 (3.7)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0266 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 257 65.9 (10.9)
subtype1 99 67.6 (10.4)
subtype2 46 67.2 (11.5)
subtype3 112 63.8 (10.8)

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

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0612 (Chi-square test), Q value = 1

Table S40.  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
ALL 2 6 24 29 25 35 3 39 35 25 25
subtype1 2 3 12 10 10 8 2 18 15 6 10
subtype2 0 1 9 4 4 8 0 6 7 5 0
subtype3 0 2 3 15 11 19 1 15 13 14 15

Figure S36.  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 = 0.00112 (Chi-square test), Q value = 0.11

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

nPatients T1 T2 T3 T4
ALL 8 66 104 76
subtype1 5 31 45 20
subtype2 1 19 13 12
subtype3 2 16 46 44

Figure S37.  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 = 0.377 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 86 75 43 48
subtype1 35 27 16 20
subtype2 20 9 6 9
subtype3 31 39 21 19

Figure S38.  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 = 0.0625 (Chi-square test), Q value = 1

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

nPatients M0 M1 MX
ALL 237 16 11
subtype1 91 7 8
subtype2 45 0 1
subtype3 101 9 2

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.491 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 100 164
subtype1 38 68
subtype2 21 25
subtype3 41 71

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.122 (Chi-square test), Q value = 1

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 42 135 38 26 15 4 2
subtype1 15 58 13 14 3 2 1
subtype2 4 21 11 7 2 1 0
subtype3 23 56 14 5 10 1 1

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0712 (Fisher's exact test), Q value = 1

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 257
subtype1 6 100
subtype2 0 46
subtype3 1 111

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.259 (Chi-square test), Q value = 1

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 206 8 8 25
subtype1 77 4 5 14
subtype2 41 0 0 3
subtype3 88 4 3 8

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.368 (ANOVA), Q value = 1

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 224 4.7 (6.3)
subtype1 88 5.4 (7.9)
subtype2 40 3.9 (5.9)
subtype3 96 4.4 (4.5)

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4 5
Number of samples 66 63 46 37 62
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.116 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 243 50 0.1 - 105.1 (9.2)
subtype1 60 6 0.1 - 105.1 (12.1)
subtype2 58 15 0.1 - 59.0 (4.9)
subtype3 36 8 0.1 - 55.0 (8.0)
subtype4 33 8 0.2 - 47.0 (9.0)
subtype5 56 13 0.1 - 72.2 (6.6)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0134 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 267 65.8 (10.8)
subtype1 65 65.6 (11.1)
subtype2 63 62.3 (11.6)
subtype3 43 65.8 (10.6)
subtype4 36 69.9 (10.6)
subtype5 60 67.0 (9.0)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0278 (Chi-square test), Q value = 1

Table S52.  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
ALL 2 11 25 24 29 42 3 40 31 25 27
subtype1 0 3 6 6 12 11 0 8 6 7 3
subtype2 0 0 1 6 6 9 1 9 12 10 6
subtype3 0 2 3 4 2 7 0 7 5 3 10
subtype4 2 3 8 1 4 6 1 5 3 0 3
subtype5 0 3 7 7 5 9 1 11 5 5 5

Figure S47.  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 = 0.000243 (Chi-square test), Q value = 0.026

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

nPatients T1 T2 T3 T4
ALL 13 69 107 76
subtype1 2 18 24 19
subtype2 0 5 30 25
subtype3 3 13 16 13
subtype4 6 15 8 8
subtype5 2 18 29 11

Figure S48.  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 = 0.109 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 90 78 47 47
subtype1 29 18 7 10
subtype2 14 19 10 17
subtype3 10 15 11 8
subtype4 17 8 8 3
subtype5 20 18 11 9

Figure S49.  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 = 0.102 (Chi-square test), Q value = 1

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

nPatients M0 M1 MX
ALL 243 18 13
subtype1 63 2 1
subtype2 58 3 2
subtype3 35 7 4
subtype4 34 2 1
subtype5 53 4 5

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.827 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 105 169
subtype1 28 38
subtype2 25 38
subtype3 18 28
subtype4 14 23
subtype5 20 42

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00589 (Chi-square test), Q value = 0.54

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 51 131 35 36 13 5 1
subtype1 12 41 4 8 0 1 0
subtype2 19 28 4 3 7 0 1
subtype3 10 19 6 7 3 1 0
subtype4 5 14 7 8 2 1 0
subtype5 5 29 14 10 1 2 0

Figure S52.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00174 (Chi-square test), Q value = 0.17

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 267
subtype1 0 66
subtype2 0 63
subtype3 5 41
subtype4 0 37
subtype5 2 60

Figure S53.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.54 (Chi-square test), Q value = 1

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 217 8 10 22
subtype1 55 1 1 5
subtype2 47 2 0 6
subtype3 35 2 5 4
subtype4 30 1 2 3
subtype5 50 2 2 4

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.538 (ANOVA), Q value = 1

Table S60.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 238 4.8 (7.1)
subtype1 57 4.2 (8.5)
subtype2 54 5.4 (5.4)
subtype3 40 6.3 (7.9)
subtype4 35 4.1 (8.2)
subtype5 52 4.4 (5.2)

Figure S55.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S61.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 69 114 91
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.166 (logrank test), Q value = 1

Table S62.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 243 50 0.1 - 105.1 (9.2)
subtype1 62 13 0.1 - 105.1 (7.4)
subtype2 99 17 0.1 - 55.0 (11.3)
subtype3 82 20 0.1 - 59.0 (4.2)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00157 (ANOVA), Q value = 0.16

Table S63.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 267 65.8 (10.8)
subtype1 67 66.2 (10.1)
subtype2 110 68.1 (10.6)
subtype3 90 62.6 (10.9)

Figure S57.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.031 (Chi-square test), Q value = 1

Table S64.  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
ALL 2 11 25 24 29 42 3 40 31 25 27
subtype1 0 2 6 8 4 13 0 15 5 4 7
subtype2 2 9 15 7 15 17 1 14 11 8 9
subtype3 0 0 4 9 10 12 2 11 15 13 11

Figure S58.  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 = 3.42e-05 (Chi-square test), Q value = 0.0038

Table S65.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 13 69 107 76
subtype1 1 20 33 13
subtype2 12 35 37 26
subtype3 0 14 37 37

Figure S59.  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 = 0.197 (Chi-square test), Q value = 1

Table S66.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 90 78 47 47
subtype1 19 24 12 9
subtype2 46 29 20 16
subtype3 25 25 15 22

Figure S60.  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 = 0.203 (Chi-square test), Q value = 1

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 243 18 13
subtype1 60 3 6
subtype2 103 6 5
subtype3 80 9 2

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.459 (Fisher's exact test), Q value = 1

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 105 169
subtype1 22 47
subtype2 46 68
subtype3 37 54

Figure S62.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000149 (Chi-square test), Q value = 0.016

Table S69.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 51 131 35 36 13 5 1
subtype1 9 35 14 7 1 2 0
subtype2 16 52 15 25 3 3 0
subtype3 26 44 6 4 9 0 1

Figure S63.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.622 (Fisher's exact test), Q value = 1

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 267
subtype1 2 67
subtype2 4 110
subtype3 1 90

Figure S64.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.998 (Chi-square test), Q value = 1

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 217 8 10 22
subtype1 55 2 2 5
subtype2 92 3 5 10
subtype3 70 3 3 7

Figure S65.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.756 (ANOVA), Q value = 1

Table S72.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 238 4.8 (7.1)
subtype1 56 4.5 (6.0)
subtype2 103 4.7 (8.2)
subtype3 79 5.3 (6.0)

Figure S66.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S73.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 104 95 109
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0558 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 276 60 0.1 - 105.1 (8.8)
subtype1 89 24 0.1 - 105.1 (10.9)
subtype2 87 24 0.1 - 65.1 (5.0)
subtype3 100 12 0.1 - 72.2 (8.8)

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00717 (ANOVA), Q value = 0.64

Table S75.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 301 66.0 (10.7)
subtype1 103 67.5 (10.1)
subtype2 94 63.1 (12.1)
subtype3 104 67.1 (9.4)

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.357 (Chi-square test), Q value = 1

Table S76.  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
ALL 2 11 27 29 32 45 3 47 38 29 29
subtype1 1 5 9 10 11 14 2 17 7 6 13
subtype2 0 0 6 9 9 15 1 12 16 14 10
subtype3 1 6 12 10 12 16 0 18 15 9 6

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00401 (Chi-square test), Q value = 0.37

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

nPatients T1 T2 T3 T4
ALL 13 75 126 84
subtype1 6 34 40 19
subtype2 0 17 38 37
subtype3 7 24 48 28

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.17 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2 N3
ALL 99 87 55 55
subtype1 32 34 18 14
subtype2 25 25 18 25
subtype3 42 28 19 16

Figure S71.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.118 (Chi-square test), Q value = 1

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

nPatients M0 M1 MX
ALL 273 20 15
subtype1 89 11 4
subtype2 86 6 3
subtype3 98 3 8

Figure S72.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.378 (Fisher's exact test), Q value = 1

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 120 188
subtype1 45 59
subtype2 32 63
subtype3 43 66

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.000243 (Chi-square test), Q value = 0.026

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 52 144 45 40 16 6 3
subtype1 15 51 23 10 3 1 0
subtype2 26 43 8 7 8 0 2
subtype3 11 50 14 23 5 5 1

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.458 (Fisher's exact test), Q value = 1

Table S82.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 301
subtype1 1 103
subtype2 2 93
subtype3 4 105

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.00106 (Chi-square test), Q value = 0.11

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 242 10 12 25
subtype1 70 4 9 16
subtype2 78 3 2 5
subtype3 94 3 1 4

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.762 (ANOVA), Q value = 1

Table S84.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 268 5.0 (7.5)
subtype1 91 5.1 (7.6)
subtype2 85 5.4 (5.5)
subtype3 92 4.6 (8.9)

Figure S77.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S85.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 98 76 134
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.819 (logrank test), Q value = 1

Table S86.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 276 60 0.1 - 105.1 (8.8)
subtype1 88 18 0.1 - 65.1 (8.0)
subtype2 69 16 0.1 - 79.1 (7.8)
subtype3 119 26 0.1 - 105.1 (9.0)

Figure S78.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0236 (ANOVA), Q value = 1

Table S87.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 301 66.0 (10.7)
subtype1 95 63.5 (12.3)
subtype2 74 67.0 (8.8)
subtype3 132 67.2 (10.2)

Figure S79.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.507 (Chi-square test), Q value = 1

Table S88.  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
ALL 2 11 27 29 32 45 3 47 38 29 29
subtype1 0 2 8 7 11 13 1 13 16 12 11
subtype2 0 1 6 11 5 15 1 13 5 8 7
subtype3 2 8 13 11 16 17 1 21 17 9 11

Figure S80.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0258 (Chi-square test), Q value = 1

Table S89.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 13 75 126 84
subtype1 2 16 41 35
subtype2 1 20 34 19
subtype3 10 39 51 30

Figure S81.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.573 (Chi-square test), Q value = 1

Table S90.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 99 87 55 55
subtype1 32 22 18 23
subtype2 23 22 14 13
subtype3 44 43 23 19

Figure S82.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.987 (Chi-square test), Q value = 1

Table S91.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 273 20 15
subtype1 86 7 5
subtype2 68 4 4
subtype3 119 9 6

Figure S83.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.861 (Fisher's exact test), Q value = 1

Table S92.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 120 188
subtype1 40 58
subtype2 28 48
subtype3 52 82

Figure S84.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.00598 (Chi-square test), Q value = 0.54

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 52 144 45 40 16 6 3
subtype1 23 49 8 5 8 1 2
subtype2 6 31 16 16 4 3 0
subtype3 23 64 21 19 4 2 1

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0218 (Fisher's exact test), Q value = 1

Table S94.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 301
subtype1 3 95
subtype2 4 72
subtype3 0 134

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.5 (Chi-square test), Q value = 1

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 242 10 12 25
subtype1 76 4 2 8
subtype2 64 1 2 4
subtype3 102 5 8 13

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.979 (ANOVA), Q value = 1

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 268 5.0 (7.5)
subtype1 89 4.9 (5.6)
subtype2 61 5.0 (6.2)
subtype3 118 5.1 (9.2)

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S97.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 65 121 75
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0117 (logrank test), Q value = 1

Table S98.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 251 47 0.1 - 105.1 (8.1)
subtype1 63 18 0.1 - 105.1 (11.6)
subtype2 115 11 0.1 - 72.2 (8.9)
subtype3 73 18 0.1 - 59.0 (3.7)

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.00307 (ANOVA), Q value = 0.3

Table S99.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 254 65.3 (10.6)
subtype1 63 66.8 (11.1)
subtype2 116 66.8 (9.1)
subtype3 75 61.9 (11.7)

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0166 (Chi-square test), Q value = 1

Table S100.  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
ALL 2 10 21 26 32 44 2 41 37 28 18
subtype1 1 1 5 11 9 4 0 13 8 4 9
subtype2 1 9 11 8 16 27 1 18 14 12 4
subtype3 0 0 5 7 7 13 1 10 15 12 5

Figure S91.  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 = 0.00923 (Chi-square test), Q value = 0.8

Table S101.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 61 114 75
subtype1 2 22 29 12
subtype2 9 27 52 33
subtype3 0 12 33 30

Figure S92.  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 = 0.542 (Chi-square test), Q value = 1

Table S102.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 91 70 49 50
subtype1 23 18 12 12
subtype2 48 33 21 19
subtype3 20 19 16 19

Figure S93.  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 = 0.174 (Chi-square test), Q value = 1

Table S103.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 236 13 12
subtype1 58 6 1
subtype2 110 3 8
subtype3 68 4 3

Figure S94.  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 = 0.903 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 98 163
subtype1 23 42
subtype2 47 74
subtype3 28 47

Figure S95.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.06e-05 (Chi-square test), Q value = 0.0088

Table S105.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 47 115 36 39 15 6 3
subtype1 9 25 18 8 3 2 0
subtype2 14 57 12 27 5 4 2
subtype3 24 33 6 4 7 0 1

Figure S96.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.224 (Fisher's exact test), Q value = 1

Table S106.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 254
subtype1 3 62
subtype2 4 117
subtype3 0 75

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.00368 (Chi-square test), Q value = 0.35

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 228 9 5
subtype1 53 2 5
subtype2 108 4 0
subtype3 67 3 0

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.661 (ANOVA), Q value = 1

Table S108.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 234 4.8 (7.3)
subtype1 56 5.4 (7.0)
subtype2 110 4.4 (8.4)
subtype3 68 5.1 (5.4)

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S109.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 60 125 76
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.77 (logrank test), Q value = 1

Table S110.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 251 47 0.1 - 105.1 (8.1)
subtype1 59 11 0.1 - 79.1 (7.8)
subtype2 117 22 0.1 - 105.1 (10.2)
subtype3 75 14 0.1 - 65.1 (3.3)

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.00772 (ANOVA), Q value = 0.68

Table S111.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 254 65.3 (10.6)
subtype1 58 66.9 (8.6)
subtype2 120 66.6 (10.7)
subtype3 76 62.2 (11.3)

Figure S101.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.038 (Chi-square test), Q value = 1

Table S112.  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
ALL 2 10 21 26 32 44 2 41 37 28 18
subtype1 0 1 6 11 6 12 1 13 4 3 3
subtype2 2 9 11 11 14 21 1 19 17 12 8
subtype3 0 0 4 4 12 11 0 9 16 13 7

Figure S102.  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 = 0.0025 (Chi-square test), Q value = 0.25

Table S113.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 61 114 75
subtype1 1 16 31 12
subtype2 10 35 47 33
subtype3 0 10 36 30

Figure S103.  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 = 0.236 (Chi-square test), Q value = 1

Table S114.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 91 70 49 50
subtype1 24 19 11 6
subtype2 44 35 23 23
subtype3 23 16 15 21

Figure S104.  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 = 0.731 (Chi-square test), Q value = 1

Table S115.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 236 13 12
subtype1 55 2 3
subtype2 114 5 6
subtype3 67 6 3

Figure S105.  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 = 0.86 (Fisher's exact test), Q value = 1

Table S116.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 98 163
subtype1 23 37
subtype2 45 80
subtype3 30 46

Figure S106.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.001 (Chi-square test), Q value = 0.1

Table S117.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 47 115 36 39 15 6 3
subtype1 2 26 12 13 3 4 0
subtype2 21 58 17 20 5 2 2
subtype3 24 31 7 6 7 0 1

Figure S107.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.162 (Fisher's exact test), Q value = 1

Table S118.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 254
subtype1 3 57
subtype2 4 121
subtype3 0 76

Figure S108.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.532 (Chi-square test), Q value = 1

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 228 9 5
subtype1 55 1 0
subtype2 111 4 3
subtype3 62 4 2

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.373 (ANOVA), Q value = 1

Table S120.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 234 4.8 (7.3)
subtype1 51 3.6 (5.5)
subtype2 114 5.0 (8.7)
subtype3 69 5.4 (5.7)

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Methods & Data
Input

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

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

  • Number of patients = 308

  • Number of clustering approaches = 10

  • 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

ANOVA analysis

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

Chi-square test

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

Fisher's exact test

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

Q value calculation

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

Download Results

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

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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