Correlation between aggregated molecular cancer subtypes and selected clinical features
Colorectal Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18K78GN
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 13 clinical features across 626 patients, 61 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'TUMOR_TISSUE_SITE',  'PATHOLOGIC_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'HISTOLOGICAL_TYPE',  'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'PATHOLOGIC_STAGE', and 'HISTOLOGICAL_TYPE'.

  • 4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'TUMOR_TISSUE_SITE',  'PATHOLOGIC_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'HISTOLOGICAL_TYPE',  'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE', and 'HISTOLOGICAL_TYPE'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY_M_STAGE' and 'RESIDUAL_TUMOR'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGIC_STAGE' and 'RESIDUAL_TUMOR'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_T_STAGE',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'PATHOLOGIC_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE',  'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'PATHOLOGIC_STAGE',  'PATHOLOGY_N_STAGE',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

  • 9 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH',  'TUMOR_TISSUE_SITE',  'PATHOLOGIC_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'HISTOLOGICAL_TYPE',  'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'TUMOR_TISSUE_SITE',  'PATHOLOGY_T_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'TUMOR_TISSUE_SITE',  'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 61 significant findings detected.

Clinical
Features
Statistical
Tests
mRNA
CNMF
subtypes
mRNA
cHierClus
subtypes
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 0.358
(0.513)
0.427
(0.555)
0.96
(0.985)
0.945
(0.976)
0.316
(0.465)
0.838
(0.902)
0.0628
(0.146)
0.392
(0.537)
0.018
(0.0585)
0.254
(0.407)
0.565
(0.686)
0.379
(0.528)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.414
(0.546)
0.03
(0.085)
0.141
(0.261)
0.000852
(0.00453)
0.371
(0.521)
0.128
(0.254)
0.000821
(0.00453)
0.000871
(0.00453)
0.00463
(0.0206)
0.0129
(0.0457)
0.122
(0.245)
0.706
(0.787)
TUMOR TISSUE SITE Fisher's exact test 0.0147
(0.0501)
0.00132
(0.00664)
0.0001
(0.000709)
0.00871
(0.034)
0.914
(0.973)
0.171
(0.293)
0.06
(0.144)
1e-05
(9.18e-05)
1e-05
(9.18e-05)
5e-05
(0.00039)
1e-05
(9.18e-05)
7e-05
(0.00052)
PATHOLOGIC STAGE Fisher's exact test 0.00564
(0.0238)
0.0182
(0.0585)
1e-05
(9.18e-05)
0.167
(0.292)
0.139
(0.261)
0.00032
(0.00192)
0.00176
(0.00856)
0.00013
(0.000845)
0.00181
(0.00856)
0.00799
(0.032)
0.093
(0.196)
0.923
(0.973)
PATHOLOGY T STAGE Fisher's exact test 0.0726
(0.164)
0.0724
(0.164)
0.671
(0.77)
0.567
(0.686)
0.681
(0.77)
0.572
(0.686)
0.0239
(0.0731)
0.608
(0.719)
0.283
(0.433)
0.241
(0.391)
0.0481
(0.123)
0.516
(0.642)
PATHOLOGY N STAGE Fisher's exact test 0.0148
(0.0501)
0.139
(0.261)
1e-05
(9.18e-05)
0.122
(0.245)
0.196
(0.326)
0.579
(0.69)
0.137
(0.261)
0.0129
(0.0457)
0.00287
(0.0132)
0.0404
(0.107)
0.0297
(0.085)
0.658
(0.766)
PATHOLOGY M STAGE Fisher's exact test 0.0296
(0.085)
0.148
(0.27)
3e-05
(0.000246)
0.169
(0.293)
0.0375
(0.101)
0.402
(0.54)
0.101
(0.206)
0.0882
(0.188)
0.704
(0.787)
0.037
(0.101)
0.0112
(0.0425)
0.518
(0.642)
GENDER Fisher's exact test 0.0546
(0.137)
0.133
(0.258)
0.417
(0.546)
0.391
(0.537)
0.919
(0.973)
0.677
(0.77)
0.416
(0.546)
0.365
(0.517)
0.621
(0.729)
0.759
(0.834)
0.491
(0.629)
0.339
(0.489)
RADIATION THERAPY Fisher's exact test 0.94
(0.976)
0.257
(0.407)
0.268
(0.418)
0.149
(0.27)
0.558
(0.685)
0.0978
(0.203)
0.184
(0.309)
0.496
(0.629)
0.0627
(0.146)
0.514
(0.642)
0.0575
(0.142)
0.0248
(0.0743)
HISTOLOGICAL TYPE Fisher's exact test 1e-05
(9.18e-05)
1e-05
(9.18e-05)
1e-05
(9.18e-05)
1e-05
(9.18e-05)
0.0821
(0.18)
0.258
(0.407)
2e-05
(0.000173)
1e-05
(9.18e-05)
1e-05
(9.18e-05)
1e-05
(9.18e-05)
1e-05
(9.18e-05)
0.00012
(0.000814)
RESIDUAL TUMOR Fisher's exact test 0.0184
(0.0585)
0.0834
(0.181)
0.0423
(0.11)
0.494
(0.629)
0.00038
(0.0022)
0.00015
(0.000936)
1e-05
(9.18e-05)
1e-05
(9.18e-05)
1e-05
(9.18e-05)
0.00723
(0.0297)
0.281
(0.433)
0.158
(0.283)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.0116
(0.0432)
0.0585
(0.143)
1.33e-06
(9.18e-05)
0.398
(0.54)
0.173
(0.293)
0.666
(0.77)
0.209
(0.344)
0.023
(0.0717)
0.163
(0.288)
0.0365
(0.101)
0.0051
(0.0221)
0.328
(0.478)
ETHNICITY Fisher's exact test 0.777
(0.842)
0.743
(0.822)
0.291
(0.437)
0.077
(0.172)
0.291
(0.437)
0.936
(0.976)
0.768
(0.837)
0.298
(0.443)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 47 63 68 44
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.358 (logrank test), Q value = 0.51

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

nPatients nDeath Duration Range (Median), Month
ALL 211 40 0.0 - 54.0 (21.0)
subtype1 43 9 0.0 - 46.6 (24.0)
subtype2 58 15 1.0 - 52.0 (20.5)
subtype3 66 9 0.0 - 50.0 (21.5)
subtype4 44 7 0.0 - 54.0 (21.0)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.414 (Kruskal-Wallis (anova)), Q value = 0.55

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

nPatients Mean (Std.Dev)
ALL 222 69.5 (11.4)
subtype1 47 71.1 (11.8)
subtype2 63 68.7 (9.1)
subtype3 68 68.7 (12.3)
subtype4 44 70.1 (12.6)

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

'mRNA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.0147 (Fisher's exact test), Q value = 0.05

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

nPatients COLON RECTUM
ALL 152 68
subtype1 40 7
subtype2 38 25
subtype3 41 25
subtype4 33 11

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00564 (Fisher's exact test), Q value = 0.024

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 47 15 65 5 10 3 22 20 33 1
subtype1 8 3 16 3 3 1 4 2 6 0
subtype2 9 3 14 0 2 2 5 11 17 0
subtype3 19 3 20 0 4 0 7 7 8 0
subtype4 11 6 15 2 1 0 6 0 2 1

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0726 (Fisher's exact test), Q value = 0.16

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 46 148 19
subtype1 1 9 31 6
subtype2 1 12 42 8
subtype3 5 16 47 0
subtype4 2 9 28 5

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0148 (Fisher's exact test), Q value = 0.05

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 136 43 43
subtype1 31 6 10
subtype2 29 17 17
subtype3 42 12 14
subtype4 34 8 2

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0296 (Fisher's exact test), Q value = 0.085

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 186 34
subtype1 40 6
subtype2 46 17
subtype3 59 8
subtype4 41 3

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0546 (Fisher's exact test), Q value = 0.14

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 106 116
subtype1 29 18
subtype2 33 30
subtype3 28 40
subtype4 16 28

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.94 (Fisher's exact test), Q value = 0.98

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 167 13
subtype1 34 2
subtype2 46 3
subtype3 53 5
subtype4 34 3

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S11.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 129 22 58 7
subtype1 29 11 4 2
subtype2 37 1 25 0
subtype3 40 1 21 2
subtype4 23 9 8 3

Figure S10.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0184 (Fisher's exact test), Q value = 0.059

Table S12.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2
ALL 185 2 29
subtype1 39 0 4
subtype2 46 2 15
subtype3 58 0 8
subtype4 42 0 2

Figure S11.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'mRNA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0116 (Kruskal-Wallis (anova)), Q value = 0.043

Table S13.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 221 2.2 (4.7)
subtype1 47 3.2 (6.9)
subtype2 63 2.8 (5.4)
subtype3 68 1.7 (2.7)
subtype4 43 0.8 (2.1)

Figure S12.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S14.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 52 90 80
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.427 (logrank test), Q value = 0.56

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 211 40 0.0 - 54.0 (21.0)
subtype1 50 11 0.0 - 53.0 (22.5)
subtype2 81 16 0.9 - 48.0 (21.0)
subtype3 80 13 0.0 - 54.0 (21.0)

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.03 (Kruskal-Wallis (anova)), Q value = 0.085

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 222 69.5 (11.4)
subtype1 52 72.9 (11.6)
subtype2 90 68.3 (10.7)
subtype3 80 68.6 (11.8)

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.00132 (Fisher's exact test), Q value = 0.0066

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 152 68
subtype1 46 6
subtype2 57 32
subtype3 49 30

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0182 (Fisher's exact test), Q value = 0.059

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 47 15 65 5 10 3 22 20 33 1
subtype1 9 3 17 4 3 1 6 2 5 1
subtype2 15 4 23 1 2 1 11 14 19 0
subtype3 23 8 25 0 5 1 5 4 9 0

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0724 (Fisher's exact test), Q value = 0.16

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 46 148 19
subtype1 1 10 33 8
subtype2 1 18 63 8
subtype3 7 18 52 3

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.139 (Fisher's exact test), Q value = 0.26

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 136 43 43
subtype1 34 9 9
subtype2 46 23 21
subtype3 56 11 13

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.148 (Fisher's exact test), Q value = 0.27

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 186 34
subtype1 45 6
subtype2 70 19
subtype3 71 9

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.133 (Fisher's exact test), Q value = 0.26

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 106 116
subtype1 30 22
subtype2 44 46
subtype3 32 48

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.257 (Fisher's exact test), Q value = 0.41

Table S23.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 167 13
subtype1 38 2
subtype2 67 3
subtype3 62 8

Figure S21.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S24.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 129 22 58 7
subtype1 27 18 2 3
subtype2 57 0 31 1
subtype3 45 4 25 3

Figure S22.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0834 (Fisher's exact test), Q value = 0.18

Table S25.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2
ALL 185 2 29
subtype1 45 0 3
subtype2 70 2 17
subtype3 70 0 9

Figure S23.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'mRNA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0585 (Kruskal-Wallis (anova)), Q value = 0.14

Table S26.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 221 2.2 (4.7)
subtype1 52 2.8 (6.4)
subtype2 90 2.4 (4.7)
subtype3 79 1.4 (2.9)

Figure S24.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

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

Table S27.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 215 246 131 22
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.96 (logrank test), Q value = 0.99

Table S28.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 594 128 0.0 - 148.0 (22.0)
subtype1 209 46 0.0 - 139.2 (20.4)
subtype2 237 49 0.4 - 131.5 (23.6)
subtype3 128 29 0.0 - 148.0 (24.1)
subtype4 20 4 0.2 - 52.0 (18.5)

Figure S25.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.141 (Kruskal-Wallis (anova)), Q value = 0.26

Table S29.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 612 66.3 (12.8)
subtype1 213 67.0 (14.1)
subtype2 246 66.7 (11.7)
subtype3 131 64.9 (12.6)
subtype4 22 62.7 (11.2)

Figure S26.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-04 (Fisher's exact test), Q value = 0.00071

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 448 162
subtype1 180 35
subtype2 159 83
subtype3 93 38
subtype4 16 6

Figure S27.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S31.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 104 1 37 172 12 2 24 15 84 55 63 24 2
subtype1 47 1 18 80 7 0 8 2 22 11 8 3 1
subtype2 32 0 10 62 5 2 8 9 43 24 29 13 0
subtype3 21 0 7 27 0 0 6 4 18 17 22 8 0
subtype4 4 0 2 3 0 0 2 0 1 3 4 0 1

Figure S28.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.671 (Fisher's exact test), Q value = 0.77

Table S32.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 20 105 417 69
subtype1 9 43 137 25
subtype2 9 34 174 27
subtype3 2 23 92 14
subtype4 0 5 14 3

Figure S29.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S33.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 346 150 115
subtype1 157 32 26
subtype2 117 80 46
subtype3 61 33 37
subtype4 11 5 6

Figure S30.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00025

Table S34.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 453 88
subtype1 175 13
subtype2 178 41
subtype3 88 29
subtype4 12 5

Figure S31.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.417 (Fisher's exact test), Q value = 0.55

Table S35.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 289 325
subtype1 106 109
subtype2 112 134
subtype3 64 67
subtype4 7 15

Figure S32.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.268 (Fisher's exact test), Q value = 0.42

Table S36.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 482 29
subtype1 178 6
subtype2 189 15
subtype3 100 7
subtype4 15 1

Figure S33.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S37.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 382 62 146 13
subtype1 138 41 26 8
subtype2 151 6 81 2
subtype3 80 12 33 3
subtype4 13 3 6 0

Figure S34.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0423 (Fisher's exact test), Q value = 0.11

Table S38.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 446 6 37 29
subtype1 164 2 6 15
subtype2 173 1 17 8
subtype3 94 3 13 4
subtype4 15 0 1 2

Figure S35.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 1.33e-06 (Kruskal-Wallis (anova)), Q value = 9.2e-05

Table S39.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 580 2.2 (4.7)
subtype1 201 1.5 (4.0)
subtype2 235 2.1 (3.7)
subtype3 124 3.5 (6.8)
subtype4 20 2.6 (4.0)

Figure S36.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.777 (Fisher's exact test), Q value = 0.84

Table S40.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 352
subtype1 1 125
subtype2 2 139
subtype3 2 75
subtype4 0 13

Figure S37.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S41.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 116 130 145
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.945 (logrank test), Q value = 0.98

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 384 87 0.2 - 148.0 (22.5)
subtype1 115 25 0.2 - 148.0 (22.1)
subtype2 127 29 0.4 - 139.2 (24.7)
subtype3 142 33 0.9 - 140.4 (21.4)

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000852 (Kruskal-Wallis (anova)), Q value = 0.0045

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 389 64.4 (13.0)
subtype1 115 61.0 (13.8)
subtype2 130 64.1 (12.1)
subtype3 144 67.3 (12.6)

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.00871 (Fisher's exact test), Q value = 0.034

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 293 96
subtype1 84 30
subtype2 88 42
subtype3 121 24

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.167 (Fisher's exact test), Q value = 0.29

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 54 1 21 113 8 2 9 12 62 36 29 23 2
subtype1 13 0 5 35 1 0 2 3 17 13 13 8 0
subtype2 14 0 7 37 2 2 2 7 25 8 9 11 0
subtype3 27 1 9 41 5 0 5 2 20 15 7 4 2

Figure S41.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.567 (Fisher's exact test), Q value = 0.69

Table S46.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 11 55 271 51
subtype1 3 11 85 17
subtype2 3 18 92 15
subtype3 5 26 94 19

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.122 (Fisher's exact test), Q value = 0.24

Table S47.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 213 103 71
subtype1 57 35 24
subtype2 68 40 20
subtype3 88 28 27

Figure S43.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.169 (Fisher's exact test), Q value = 0.29

Table S48.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 266 53
subtype1 77 20
subtype2 89 20
subtype3 100 13

Figure S44.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.391 (Fisher's exact test), Q value = 0.54

Table S49.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 179 212
subtype1 50 66
subtype2 56 74
subtype3 73 72

Figure S45.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.149 (Fisher's exact test), Q value = 0.27

Table S50.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 318 16
subtype1 102 3
subtype2 100 9
subtype3 116 4

Figure S46.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S51.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 251 39 90 6
subtype1 66 17 28 2
subtype2 86 1 42 0
subtype3 99 21 20 4

Figure S47.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.494 (Fisher's exact test), Q value = 0.63

Table S52.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 259 5 7 29
subtype1 80 1 2 5
subtype2 86 1 2 8
subtype3 93 3 3 16

Figure S48.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.398 (Kruskal-Wallis (anova)), Q value = 0.54

Table S53.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 357 2.3 (4.8)
subtype1 108 3.1 (6.7)
subtype2 118 1.8 (3.3)
subtype3 131 2.0 (4.0)

Figure S49.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.743 (Fisher's exact test), Q value = 0.82

Table S54.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 338
subtype1 2 104
subtype2 2 113
subtype3 1 121

Figure S50.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S55.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 113 196 180
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.316 (logrank test), Q value = 0.46

Table S56.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 476 105 0.0 - 140.4 (21.6)
subtype1 108 27 0.0 - 117.1 (22.0)
subtype2 191 46 0.4 - 140.4 (22.0)
subtype3 177 32 0.0 - 135.7 (20.4)

Figure S51.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.371 (Kruskal-Wallis (anova)), Q value = 0.52

Table S57.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 488 66.5 (12.7)
subtype1 113 67.2 (13.0)
subtype2 195 67.0 (12.5)
subtype3 180 65.6 (12.8)

Figure S52.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.914 (Fisher's exact test), Q value = 0.97

Table S58.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 358 131
subtype1 81 32
subtype2 144 52
subtype3 133 47

Figure S53.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.139 (Fisher's exact test), Q value = 0.26

Table S59.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 75 1 28 147 10 1 22 13 69 43 46 19 2
subtype1 16 1 10 28 1 0 5 1 15 12 12 8 1
subtype2 30 0 13 51 5 1 11 6 29 16 24 5 1
subtype3 29 0 5 68 4 0 6 6 25 15 10 6 0

Figure S54.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.681 (Fisher's exact test), Q value = 0.77

Table S60.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 11 81 338 56
subtype1 0 19 80 12
subtype2 6 33 132 24
subtype3 5 29 126 20

Figure S55.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.196 (Fisher's exact test), Q value = 0.33

Table S61.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 274 122 89
subtype1 59 26 26
subtype2 103 56 36
subtype3 112 40 27

Figure S56.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0375 (Fisher's exact test), Q value = 0.1

Table S62.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 368 67
subtype1 82 22
subtype2 143 29
subtype3 143 16

Figure S57.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.919 (Fisher's exact test), Q value = 0.97

Table S63.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 234 255
subtype1 56 57
subtype2 93 103
subtype3 85 95

Figure S58.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.558 (Fisher's exact test), Q value = 0.69

Table S64.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 386 23
subtype1 93 5
subtype2 142 11
subtype3 151 7

Figure S59.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0821 (Fisher's exact test), Q value = 0.18

Table S65.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 313 42 118 10
subtype1 72 9 28 3
subtype2 134 9 46 5
subtype3 107 24 44 2

Figure S60.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00038 (Fisher's exact test), Q value = 0.0022

Table S66.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 365 3 29 19
subtype1 91 0 9 2
subtype2 123 2 18 11
subtype3 151 1 2 6

Figure S61.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.173 (Kruskal-Wallis (anova)), Q value = 0.29

Table S67.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 463 2.1 (4.5)
subtype1 110 2.2 (3.8)
subtype2 183 2.3 (4.5)
subtype3 170 1.9 (5.0)

Figure S62.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.291 (Fisher's exact test), Q value = 0.44

Table S68.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 299
subtype1 0 50
subtype2 2 111
subtype3 0 138

Figure S63.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S69.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 236 167 86
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.838 (logrank test), Q value = 0.9

Table S70.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 476 105 0.0 - 140.4 (21.6)
subtype1 233 47 0.0 - 135.7 (20.6)
subtype2 161 43 0.9 - 140.4 (22.4)
subtype3 82 15 0.0 - 88.2 (20.5)

Figure S64.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.128 (Kruskal-Wallis (anova)), Q value = 0.25

Table S71.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 488 66.5 (12.7)
subtype1 236 65.8 (12.3)
subtype2 166 66.6 (12.7)
subtype3 86 68.4 (13.8)

Figure S65.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.171 (Fisher's exact test), Q value = 0.29

Table S72.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 358 131
subtype1 175 61
subtype2 127 40
subtype3 56 30

Figure S66.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00032 (Fisher's exact test), Q value = 0.0019

Table S73.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 75 1 28 147 10 1 22 13 69 43 46 19 2
subtype1 36 1 6 84 3 0 4 5 35 23 16 13 1
subtype2 28 0 9 47 5 1 12 6 20 16 15 5 1
subtype3 11 0 13 16 2 0 6 2 14 4 15 1 0

Figure S67.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.572 (Fisher's exact test), Q value = 0.69

Table S74.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 11 81 338 56
subtype1 5 35 168 26
subtype2 3 33 108 23
subtype3 3 13 62 7

Figure S68.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.579 (Fisher's exact test), Q value = 0.69

Table S75.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 274 122 89
subtype1 137 54 43
subtype2 92 41 33
subtype3 45 27 13

Figure S69.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.402 (Fisher's exact test), Q value = 0.54

Table S76.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 368 67
subtype1 180 31
subtype2 125 20
subtype3 63 16

Figure S70.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.677 (Fisher's exact test), Q value = 0.77

Table S77.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 234 255
subtype1 111 125
subtype2 78 89
subtype3 45 41

Figure S71.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0978 (Fisher's exact test), Q value = 0.2

Table S78.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 386 23
subtype1 195 10
subtype2 126 5
subtype3 65 8

Figure S72.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.258 (Fisher's exact test), Q value = 0.41

Table S79.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 313 42 118 10
subtype1 147 27 57 4
subtype2 114 11 35 4
subtype3 52 4 26 2

Figure S73.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00015 (Fisher's exact test), Q value = 0.00094

Table S80.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 365 3 29 19
subtype1 192 1 4 7
subtype2 108 2 13 10
subtype3 65 0 12 2

Figure S74.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.666 (Kruskal-Wallis (anova)), Q value = 0.77

Table S81.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 463 2.1 (4.5)
subtype1 225 2.1 (5.0)
subtype2 155 2.4 (4.3)
subtype3 83 1.7 (3.8)

Figure S75.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.077 (Fisher's exact test), Q value = 0.17

Table S82.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 299
subtype1 0 173
subtype2 1 96
subtype3 1 30

Figure S76.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S83.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 157 129 132 203
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0628 (logrank test), Q value = 0.15

Table S84.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 602 128 0.0 - 148.0 (22.1)
subtype1 153 40 0.4 - 140.4 (25.2)
subtype2 128 19 2.5 - 135.7 (23.6)
subtype3 130 35 0.2 - 148.0 (20.7)
subtype4 191 34 0.0 - 117.1 (20.0)

Figure S77.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000821 (Kruskal-Wallis (anova)), Q value = 0.0045

Table S85.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 619 66.3 (12.7)
subtype1 157 63.6 (12.4)
subtype2 128 66.1 (13.3)
subtype3 131 65.7 (13.1)
subtype4 203 69.0 (11.9)

Figure S78.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.06 (Fisher's exact test), Q value = 0.14

Table S86.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 454 163
subtype1 108 49
subtype2 102 25
subtype3 103 29
subtype4 141 60

Figure S79.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00176 (Fisher's exact test), Q value = 0.0086

Table S87.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 104 1 38 177 12 2 25 15 84 55 62 24 2
subtype1 23 0 8 39 3 2 2 3 27 12 16 13 0
subtype2 28 1 9 35 1 0 6 4 18 7 7 5 2
subtype3 13 0 5 46 2 0 3 3 18 18 15 5 0
subtype4 40 0 16 57 6 0 14 5 21 18 24 1 0

Figure S80.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0239 (Fisher's exact test), Q value = 0.073

Table S88.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 20 105 423 70
subtype1 6 26 104 19
subtype2 3 29 84 12
subtype3 1 13 95 23
subtype4 10 37 140 16

Figure S81.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.137 (Fisher's exact test), Q value = 0.26

Table S89.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 352 150 115
subtype1 85 45 24
subtype2 79 32 18
subtype3 67 30 34
subtype4 121 43 39

Figure S82.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.101 (Fisher's exact test), Q value = 0.21

Table S90.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 460 87
subtype1 103 29
subtype2 89 13
subtype3 93 20
subtype4 175 25

Figure S83.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.416 (Fisher's exact test), Q value = 0.55

Table S91.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 290 331
subtype1 69 88
subtype2 62 67
subtype3 56 76
subtype4 103 100

Figure S84.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.184 (Fisher's exact test), Q value = 0.31

Table S92.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 489 30
subtype1 117 10
subtype2 110 5
subtype3 116 3
subtype4 146 12

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00017

Table S93.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 388 62 147 13
subtype1 104 2 49 0
subtype2 80 20 20 4
subtype3 84 19 26 3
subtype4 120 21 52 6

Figure S86.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S94.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 452 6 36 30
subtype1 98 2 8 10
subtype2 84 2 3 9
subtype3 95 1 4 11
subtype4 175 1 21 0

Figure S87.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.209 (Kruskal-Wallis (anova)), Q value = 0.34

Table S95.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 586 2.2 (4.7)
subtype1 144 1.9 (3.8)
subtype2 119 1.9 (4.3)
subtype3 122 3.1 (6.4)
subtype4 201 2.1 (4.3)

Figure S88.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.291 (Fisher's exact test), Q value = 0.44

Table S96.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 352
subtype1 4 118
subtype2 0 93
subtype3 1 118
subtype4 0 23

Figure S89.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S97.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 81 86 64 57 90 140 103
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.392 (logrank test), Q value = 0.54

Table S98.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 602 128 0.0 - 148.0 (22.1)
subtype1 80 22 1.0 - 148.0 (26.0)
subtype2 84 16 1.0 - 133.2 (21.3)
subtype3 62 17 0.2 - 135.7 (20.2)
subtype4 57 8 2.5 - 91.8 (24.1)
subtype5 90 23 0.4 - 129.3 (22.5)
subtype6 128 20 0.0 - 117.1 (21.0)
subtype7 101 22 0.0 - 64.0 (24.0)

Figure S90.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000871 (Kruskal-Wallis (anova)), Q value = 0.0045

Table S99.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 619 66.3 (12.7)
subtype1 81 64.2 (13.1)
subtype2 86 65.0 (13.4)
subtype3 62 64.7 (15.4)
subtype4 57 64.7 (12.8)
subtype5 90 63.9 (11.6)
subtype6 140 68.0 (10.9)
subtype7 103 70.8 (12.2)

Figure S91.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S100.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 454 163
subtype1 59 22
subtype2 73 13
subtype3 60 4
subtype4 32 23
subtype5 59 31
subtype6 88 51
subtype7 83 19

Figure S92.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00013 (Fisher's exact test), Q value = 0.00084

Table S101.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 104 1 38 177 12 2 25 15 84 55 62 24 2
subtype1 14 0 1 26 2 1 1 2 11 10 5 5 0
subtype2 15 1 6 23 1 0 3 3 14 5 5 3 1
subtype3 13 0 4 28 1 0 1 1 5 6 2 1 1
subtype4 8 0 4 8 1 0 3 3 12 3 6 5 0
subtype5 7 0 3 25 1 1 1 1 17 11 9 9 0
subtype6 28 0 7 37 1 0 8 4 16 14 25 0 0
subtype7 19 0 13 30 5 0 8 1 9 6 10 1 0

Figure S93.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.608 (Fisher's exact test), Q value = 0.72

Table S102.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 20 105 423 70
subtype1 2 14 55 10
subtype2 5 15 53 12
subtype3 2 11 44 7
subtype4 1 9 40 7
subtype5 0 9 66 13
subtype6 6 30 94 10
subtype7 4 17 71 11

Figure S94.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0129 (Fisher's exact test), Q value = 0.046

Table S103.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 352 150 115
subtype1 45 18 17
subtype2 52 23 11
subtype3 46 9 9
subtype4 26 22 9
subtype5 38 27 22
subtype6 77 34 29
subtype7 68 17 18

Figure S95.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0882 (Fisher's exact test), Q value = 0.19

Table S104.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 460 87
subtype1 58 10
subtype2 57 8
subtype3 47 4
subtype4 34 11
subtype5 60 18
subtype6 113 25
subtype7 91 11

Figure S96.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.365 (Fisher's exact test), Q value = 0.52

Table S105.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 290 331
subtype1 38 43
subtype2 41 45
subtype3 29 35
subtype4 18 39
subtype5 43 47
subtype6 68 72
subtype7 53 50

Figure S97.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.496 (Fisher's exact test), Q value = 0.63

Table S106.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 489 30
subtype1 59 3
subtype2 72 2
subtype3 53 1
subtype4 50 4
subtype5 79 5
subtype6 97 10
subtype7 79 5

Figure S98.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S107.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 388 62 147 13
subtype1 56 2 22 0
subtype2 58 14 11 2
subtype3 47 12 3 1
subtype4 29 3 22 1
subtype5 53 6 30 1
subtype6 85 3 50 0
subtype7 60 22 9 8

Figure S99.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S108.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 452 6 36 30
subtype1 46 1 2 8
subtype2 58 0 1 4
subtype3 43 1 1 9
subtype4 34 2 2 2
subtype5 66 0 0 7
subtype6 115 2 22 0
subtype7 90 0 8 0

Figure S100.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.023 (Kruskal-Wallis (anova)), Q value = 0.072

Table S109.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 586 2.2 (4.7)
subtype1 74 2.1 (3.2)
subtype2 78 1.8 (4.5)
subtype3 57 1.4 (3.3)
subtype4 54 2.5 (5.1)
subtype5 82 3.1 (6.5)
subtype6 138 2.2 (4.4)
subtype7 103 2.2 (5.1)

Figure S101.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.936 (Fisher's exact test), Q value = 0.98

Table S110.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 5 352
subtype1 1 72
subtype2 2 67
subtype3 1 59
subtype4 0 45
subtype5 1 81
subtype6 0 17
subtype7 0 11

Figure S102.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S111.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4
Number of samples 154 95 87 213
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.018 (logrank test), Q value = 0.059

Table S112.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 530 116 0.0 - 148.0 (23.7)
subtype1 151 23 0.2 - 148.0 (22.0)
subtype2 95 27 0.4 - 129.3 (22.4)
subtype3 85 26 0.9 - 140.4 (31.0)
subtype4 199 40 0.0 - 117.1 (21.0)

Figure S103.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00463 (Kruskal-Wallis (anova)), Q value = 0.021

Table S113.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 548 66.8 (12.7)
subtype1 153 65.3 (13.0)
subtype2 95 64.9 (13.3)
subtype3 87 65.6 (11.5)
subtype4 213 69.3 (12.3)

Figure S104.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S114.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 405 140
subtype1 127 27
subtype2 75 19
subtype3 41 44
subtype4 162 50

Figure S105.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00181 (Fisher's exact test), Q value = 0.0086

Table S115.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 94 1 37 152 10 1 25 11 73 47 57 21 1
subtype1 27 1 7 45 2 1 6 1 28 11 10 7 1
subtype2 11 0 4 25 1 0 1 2 15 12 9 8 0
subtype3 11 0 5 22 1 0 3 5 16 8 9 3 0
subtype4 45 0 21 60 6 0 15 3 14 16 29 3 0

Figure S106.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.283 (Fisher's exact test), Q value = 0.43

Table S116.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 20 94 376 56
subtype1 6 25 108 14
subtype2 2 11 64 16
subtype3 5 13 63 6
subtype4 7 45 141 20

Figure S107.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00287 (Fisher's exact test), Q value = 0.013

Table S117.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 311 134 100
subtype1 90 41 22
subtype2 44 28 20
subtype3 41 31 15
subtype4 136 34 43

Figure S108.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.704 (Fisher's exact test), Q value = 0.79

Table S118.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 413 78
subtype1 108 18
subtype2 63 16
subtype3 65 12
subtype4 177 32

Figure S109.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.621 (Fisher's exact test), Q value = 0.73

Table S119.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 257 292
subtype1 76 78
subtype2 39 56
subtype3 40 47
subtype4 102 111

Figure S110.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.0627 (Fisher's exact test), Q value = 0.15

Table S120.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 427 26
subtype1 128 3
subtype2 84 5
subtype3 51 7
subtype4 164 11

Figure S111.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S121.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 347 54 127 10
subtype1 110 16 27 0
subtype2 61 14 17 2
subtype3 39 0 42 2
subtype4 137 24 41 6

Figure S112.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S122.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 386 6 36 26
subtype1 102 2 2 16
subtype2 61 0 3 7
subtype3 49 2 6 1
subtype4 174 2 25 2

Figure S113.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.163 (Kruskal-Wallis (anova)), Q value = 0.29

Table S123.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 516 2.2 (4.7)
subtype1 135 1.7 (3.1)
subtype2 88 3.1 (7.1)
subtype3 85 2.3 (4.5)
subtype4 208 2.1 (4.4)

Figure S114.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.768 (Fisher's exact test), Q value = 0.84

Table S124.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 307
subtype1 2 137
subtype2 0 79
subtype3 0 54
subtype4 0 37

Figure S115.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S125.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5 6 7 8 9
Number of samples 49 74 30 89 78 55 57 33 84
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.254 (logrank test), Q value = 0.41

Table S126.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 530 116 0.0 - 148.0 (23.7)
subtype1 49 7 0.2 - 124.3 (21.4)
subtype2 73 17 0.5 - 100.0 (17.9)
subtype3 29 6 1.0 - 133.2 (30.1)
subtype4 85 23 0.0 - 148.0 (25.1)
subtype5 76 22 0.0 - 135.7 (21.5)
subtype6 52 8 1.0 - 139.2 (19.8)
subtype7 57 19 1.9 - 140.4 (25.1)
subtype8 28 5 0.0 - 45.6 (27.0)
subtype9 81 9 0.9 - 109.3 (24.0)

Figure S116.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.0129 (Kruskal-Wallis (anova)), Q value = 0.046

Table S127.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 548 66.8 (12.7)
subtype1 49 66.3 (13.1)
subtype2 74 62.9 (13.2)
subtype3 30 66.8 (11.9)
subtype4 89 64.5 (13.1)
subtype5 77 70.0 (12.9)
subtype6 55 66.0 (12.9)
subtype7 57 68.5 (11.8)
subtype8 33 70.9 (13.3)
subtype9 84 68.0 (10.7)

Figure S117.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_TISSUE_SITE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00039

Table S128.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 405 140
subtype1 43 6
subtype2 57 16
subtype3 23 7
subtype4 59 30
subtype5 69 9
subtype6 41 13
subtype7 34 22
subtype8 29 4
subtype9 50 33

Figure S118.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.00799 (Fisher's exact test), Q value = 0.032

Table S129.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 94 1 37 152 10 1 25 11 73 47 57 21 1
subtype1 7 0 3 18 1 0 3 0 8 3 2 0 0
subtype2 9 0 4 25 0 0 0 2 12 10 4 6 0
subtype3 10 0 1 9 2 0 2 0 3 0 2 1 0
subtype4 12 1 3 18 1 1 2 2 13 13 16 4 0
subtype5 16 0 12 20 3 0 7 0 6 8 4 1 0
subtype6 10 0 3 15 1 0 5 2 8 2 7 0 0
subtype7 6 0 1 18 1 0 1 3 7 2 5 5 1
subtype8 5 0 1 12 0 0 2 1 4 1 6 1 0
subtype9 19 0 9 17 1 0 3 1 12 8 11 3 0

Figure S119.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.241 (Fisher's exact test), Q value = 0.39

Table S130.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 20 94 376 56
subtype1 2 7 37 2
subtype2 1 9 52 12
subtype3 4 6 16 4
subtype4 2 12 63 10
subtype5 1 17 50 10
subtype6 4 10 37 4
subtype7 1 10 41 5
subtype8 2 4 22 5
subtype9 3 19 58 4

Figure S120.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.0404 (Fisher's exact test), Q value = 0.11

Table S131.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 311 134 100
subtype1 33 11 4
subtype2 38 20 15
subtype3 22 4 4
subtype4 37 24 26
subtype5 53 11 14
subtype6 31 18 6
subtype7 31 17 9
subtype8 18 9 6
subtype9 48 20 16

Figure S121.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.037 (Fisher's exact test), Q value = 0.1

Table S132.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 413 78
subtype1 33 2
subtype2 55 9
subtype3 24 3
subtype4 60 20
subtype5 69 5
subtype6 43 7
subtype7 35 11
subtype8 26 7
subtype9 68 14

Figure S122.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.759 (Fisher's exact test), Q value = 0.83

Table S133.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 257 292
subtype1 20 29
subtype2 33 41
subtype3 14 16
subtype4 44 45
subtype5 40 38
subtype6 28 27
subtype7 24 33
subtype8 19 14
subtype9 35 49

Figure S123.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.514 (Fisher's exact test), Q value = 0.64

Table S134.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 427 26
subtype1 45 1
subtype2 63 6
subtype3 22 0
subtype4 64 5
subtype5 63 3
subtype6 41 2
subtype7 38 5
subtype8 25 0
subtype9 66 4

Figure S124.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S135.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 347 54 127 10
subtype1 38 5 6 0
subtype2 43 14 15 1
subtype3 21 2 6 1
subtype4 51 7 27 3
subtype5 52 16 6 3
subtype6 39 1 13 0
subtype7 31 2 22 0
subtype8 24 5 2 1
subtype9 48 2 30 1

Figure S125.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.00723 (Fisher's exact test), Q value = 0.03

Table S136.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 386 6 36 26
subtype1 34 1 1 4
subtype2 51 0 1 8
subtype3 20 0 1 2
subtype4 55 2 11 2
subtype5 61 1 3 4
subtype6 40 0 4 5
subtype7 27 1 1 1
subtype8 29 0 3 0
subtype9 69 1 11 0

Figure S126.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0365 (Kruskal-Wallis (anova)), Q value = 0.1

Table S137.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 516 2.2 (4.7)
subtype1 46 1.2 (2.4)
subtype2 64 2.4 (4.3)
subtype3 28 1.4 (3.2)
subtype4 82 2.7 (3.8)
subtype5 76 1.8 (3.9)
subtype6 49 2.1 (7.3)
subtype7 56 2.2 (4.9)
subtype8 33 3.0 (6.8)
subtype9 82 2.4 (5.0)

Figure S127.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.298 (Fisher's exact test), Q value = 0.44

Table S138.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 307
subtype1 1 41
subtype2 0 70
subtype3 0 19
subtype4 0 50
subtype5 0 29
subtype6 1 26
subtype7 0 49
subtype8 0 11
subtype9 0 12

Figure S128.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S139.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 35 40 39 22
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.565 (logrank test), Q value = 0.69

Table S140.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 136 43 0.2 - 140.4 (25.1)
subtype1 35 12 0.2 - 139.2 (13.3)
subtype2 40 13 0.4 - 129.3 (28.9)
subtype3 39 10 4.0 - 140.4 (27.1)
subtype4 22 8 6.2 - 131.5 (21.9)

Figure S129.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.122 (Kruskal-Wallis (anova)), Q value = 0.24

Table S141.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 135 64.9 (12.6)
subtype1 35 64.7 (13.7)
subtype2 40 62.6 (11.5)
subtype3 39 65.0 (13.3)
subtype4 21 69.3 (11.0)

Figure S130.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S142.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 85 50
subtype1 30 5
subtype2 27 13
subtype3 10 28
subtype4 18 4

Figure S131.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.093 (Fisher's exact test), Q value = 0.2

Table S143.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 15 7 37 1 1 4 21 15 13 9
subtype1 4 1 11 1 0 1 3 5 2 1
subtype2 1 2 11 0 0 1 7 4 7 5
subtype3 2 1 10 0 1 2 8 4 4 3
subtype4 8 3 5 0 0 0 3 2 0 0

Figure S132.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0481 (Fisher's exact test), Q value = 0.12

Table S144.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 4 16 99 15
subtype1 2 4 23 6
subtype2 0 1 32 5
subtype3 1 4 31 3
subtype4 1 7 13 1

Figure S133.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0297 (Fisher's exact test), Q value = 0.085

Table S145.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 64 39 30
subtype1 20 7 8
subtype2 14 14 10
subtype3 13 15 10
subtype4 17 3 2

Figure S134.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0112 (Fisher's exact test), Q value = 0.043

Table S146.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 87 21
subtype1 21 3
subtype2 23 12
subtype3 24 6
subtype4 19 0

Figure S135.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.491 (Fisher's exact test), Q value = 0.63

Table S147.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 58 78
subtype1 14 21
subtype2 21 19
subtype3 14 25
subtype4 9 13

Figure S136.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0575 (Fisher's exact test), Q value = 0.14

Table S148.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 108 5
subtype1 26 0
subtype2 37 1
subtype3 25 4
subtype4 20 0

Figure S137.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 9.2e-05

Table S149.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 74 9 49 1
subtype1 23 5 5 0
subtype2 24 3 13 0
subtype3 10 0 27 1
subtype4 17 1 4 0

Figure S138.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.281 (Fisher's exact test), Q value = 0.43

Table S150.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 74 2 2 7
subtype1 19 0 0 0
subtype2 26 0 1 2
subtype3 16 2 1 3
subtype4 13 0 0 2

Figure S139.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0051 (Kruskal-Wallis (anova)), Q value = 0.022

Table S151.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 126 2.8 (6.2)
subtype1 35 2.1 (4.1)
subtype2 37 3.6 (8.4)
subtype3 34 4.1 (6.5)
subtype4 20 0.7 (1.5)

Figure S140.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S152.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 63 16 34 23
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.379 (logrank test), Q value = 0.53

Table S153.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 136 43 0.2 - 140.4 (25.1)
subtype1 63 23 0.2 - 100.0 (21.4)
subtype2 16 4 4.0 - 129.3 (29.6)
subtype3 34 10 7.2 - 131.5 (26.4)
subtype4 23 6 0.4 - 140.4 (25.5)

Figure S141.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.706 (Kruskal-Wallis (anova)), Q value = 0.79

Table S154.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 135 64.9 (12.6)
subtype1 63 64.4 (13.2)
subtype2 16 63.1 (13.1)
subtype3 33 67.2 (12.4)
subtype4 23 64.0 (11.2)

Figure S142.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 7e-05 (Fisher's exact test), Q value = 0.00052

Table S155.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients COLON RECTUM
ALL 85 50
subtype1 52 11
subtype2 5 10
subtype3 17 17
subtype4 11 12

Figure S143.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.923 (Fisher's exact test), Q value = 0.97

Table S156.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 15 7 37 1 1 4 21 15 13 9
subtype1 9 3 17 1 0 0 7 8 7 5
subtype2 0 1 5 0 0 1 4 1 2 2
subtype3 4 2 8 0 1 2 7 4 2 1
subtype4 2 1 7 0 0 1 3 2 2 1

Figure S144.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.516 (Fisher's exact test), Q value = 0.64

Table S157.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 4 16 99 15
subtype1 1 9 42 10
subtype2 0 0 15 1
subtype3 2 5 25 2
subtype4 1 2 17 2

Figure S145.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.658 (Fisher's exact test), Q value = 0.77

Table S158.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 64 39 30
subtype1 33 14 15
subtype2 6 5 5
subtype3 15 12 7
subtype4 10 8 3

Figure S146.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.518 (Fisher's exact test), Q value = 0.64

Table S159.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 87 21
subtype1 37 12
subtype2 10 3
subtype3 25 3
subtype4 15 3

Figure S147.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.339 (Fisher's exact test), Q value = 0.49

Table S160.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 58 78
subtype1 27 36
subtype2 10 6
subtype3 12 22
subtype4 9 14

Figure S148.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0248 (Fisher's exact test), Q value = 0.074

Table S161.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients NO YES
ALL 108 5
subtype1 59 0
subtype2 12 2
subtype3 24 2
subtype4 13 1

Figure S149.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00012 (Fisher's exact test), Q value = 0.00081

Table S162.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 74 9 49 1
subtype1 43 9 11 0
subtype2 5 0 10 0
subtype3 17 0 16 1
subtype4 9 0 12 0

Figure S150.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.158 (Fisher's exact test), Q value = 0.28

Table S163.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 74 2 2 7
subtype1 37 0 0 3
subtype2 11 0 1 1
subtype3 15 2 0 1
subtype4 11 0 1 2

Figure S151.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.328 (Kruskal-Wallis (anova)), Q value = 0.48

Table S164.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 126 2.8 (6.2)
subtype1 59 3.1 (7.3)
subtype2 15 3.7 (5.6)
subtype3 32 3.1 (5.8)
subtype4 20 1.2 (2.1)

Figure S152.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/COADREAD-TP/22555066/COADREAD-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/COADREAD-TP/22506534/COADREAD-TP.merged_data.txt

  • Number of patients = 626

  • Number of clustering approaches = 12

  • Number of selected clinical features = 13

  • 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)