Correlation between aggregated molecular cancer subtypes and selected clinical features
Colon 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/C1348JQF
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 457 patients, 40 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 5 subtypes that correlate to 'PATHOLOGIC_STAGE',  'PATHOLOGY_N_STAGE',  'PATHOLOGY_M_STAGE',  'GENDER',  'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.

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

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

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

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

  • Consensus hierarchical clustering analysis on RPPA data identified 9 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_M_STAGE',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'PATHOLOGY_M_STAGE',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGIC_STAGE',  'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.

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

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

  • 9 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to '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, 40 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 0.914
(0.997)
0.185
(0.436)
0.497
(0.767)
0.808
(0.927)
0.456
(0.731)
0.656
(0.88)
0.216
(0.482)
0.769
(0.909)
0.105
(0.321)
0.546
(0.811)
0.626
(0.88)
0.833
(0.949)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.0834
(0.266)
0.0341
(0.161)
0.85
(0.954)
5.22e-05
(0.00074)
0.154
(0.407)
0.0571
(0.212)
0.00925
(0.0513)
0.000129
(0.00134)
0.000141
(0.00138)
0.00816
(0.0472)
0.651
(0.88)
0.769
(0.909)
PATHOLOGIC STAGE Fisher's exact test 0.0239
(0.116)
0.297
(0.565)
5e-05
(0.00074)
0.171
(0.424)
0.382
(0.663)
0.0453
(0.184)
0.0352
(0.162)
0.00059
(0.0046)
0.00114
(0.00808)
0.0749
(0.249)
0.731
(0.898)
0.65
(0.88)
PATHOLOGY T STAGE Fisher's exact test 0.987
(1.00)
0.285
(0.556)
0.51
(0.773)
0.327
(0.6)
0.721
(0.898)
0.843
(0.953)
0.448
(0.728)
0.775
(0.909)
0.196
(0.443)
0.805
(0.927)
0.978
(1.00)
0.656
(0.88)
PATHOLOGY N STAGE Fisher's exact test 0.00584
(0.0364)
0.614
(0.879)
0.00019
(0.00156)
0.133
(0.366)
0.676
(0.894)
0.166
(0.417)
0.448
(0.728)
0.0868
(0.271)
0.0706
(0.24)
0.0443
(0.184)
0.134
(0.366)
0.288
(0.556)
PATHOLOGY M STAGE Fisher's exact test 0.0459
(0.184)
0.252
(0.539)
2e-05
(0.00052)
0.251
(0.539)
0.289
(0.556)
0.754
(0.909)
0.00473
(0.0307)
0.0477
(0.186)
0.115
(0.333)
0.164
(0.417)
0.27
(0.546)
0.109
(0.328)
GENDER Fisher's exact test 0.0037
(0.0251)
0.0709
(0.24)
0.266
(0.546)
0.305
(0.573)
0.488
(0.767)
0.655
(0.88)
0.0635
(0.225)
0.195
(0.443)
0.881
(0.975)
0.0178
(0.0926)
0.496
(0.767)
0.0587
(0.213)
RADIATION THERAPY Fisher's exact test 0.66
(0.88)
0.442
(0.728)
0.708
(0.895)
0.54
(0.81)
1
(1.00)
0.77
(0.909)
0.749
(0.909)
0.873
(0.972)
0.402
(0.683)
0.984
(1.00)
HISTOLOGICAL TYPE Fisher's exact test 5e-05
(0.00074)
1e-05
(0.000312)
1e-05
(0.000312)
0.00011
(0.00132)
0.0196
(0.0988)
0.365
(0.648)
1e-05
(0.000312)
1e-05
(0.000312)
0.00011
(0.00132)
0.00016
(0.00139)
0.191
(0.443)
0.0364
(0.162)
RESIDUAL TUMOR Fisher's exact test 0.322
(0.598)
0.131
(0.366)
0.0837
(0.266)
0.154
(0.407)
4e-05
(0.00074)
0.00776
(0.0466)
0.00012
(0.00134)
1e-05
(0.000312)
5e-05
(0.00074)
0.00015
(0.00138)
0.459
(0.731)
0.345
(0.626)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.00953
(0.0513)
0.285
(0.556)
0.000858
(0.00637)
0.633
(0.88)
0.566
(0.832)
0.257
(0.543)
0.507
(0.773)
0.183
(0.436)
0.403
(0.683)
0.0446
(0.184)
0.702
(0.895)
0.111
(0.328)
RACE Fisher's exact test 0.599
(0.874)
1
(1.00)
0.0507
(0.193)
0.726
(0.898)
0.375
(0.658)
0.235
(0.516)
0.354
(0.634)
0.788
(0.918)
0.711
(0.895)
0.996
(1.00)
0.264
(0.546)
0.685
(0.895)
ETHNICITY Fisher's exact test 0.606
(0.875)
0.689
(0.895)
0.174
(0.424)
0.158
(0.41)
0.41
(0.687)
0.89
(0.978)
1
(1.00)
0.71
(0.895)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3 4 5
Number of samples 33 18 49 28 25
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.914 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 146 30 0.0 - 54.0 (23.5)
subtype1 32 7 0.0 - 46.6 (19.0)
subtype2 16 2 1.0 - 43.8 (28.0)
subtype3 46 10 2.0 - 51.0 (21.5)
subtype4 28 6 0.0 - 54.0 (23.5)
subtype5 24 5 0.0 - 50.0 (26.0)

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 = 0.0834 (Kruskal-Wallis (anova)), Q value = 0.27

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

nPatients Mean (Std.Dev)
ALL 153 70.8 (11.5)
subtype1 33 75.6 (10.7)
subtype2 18 68.0 (13.1)
subtype3 49 70.2 (11.6)
subtype4 28 70.3 (10.8)
subtype5 25 68.0 (10.9)

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0239 (Fisher's exact test), Q value = 0.12

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

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 29 12 45 5 8 3 12 16 21 1
subtype1 6 2 13 3 3 0 4 0 1 0
subtype2 3 1 4 0 0 1 0 4 5 0
subtype3 9 2 10 0 3 2 4 8 11 0
subtype4 6 5 8 2 1 0 3 0 2 1
subtype5 5 2 10 0 1 0 1 4 2 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.987 (Fisher's exact test), Q value = 1

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

nPatients T1 T2 T3 T4
ALL 4 31 103 15
subtype1 1 6 22 4
subtype2 0 4 12 2
subtype3 2 11 32 4
subtype4 0 6 18 4
subtype5 1 4 19 1

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00584 (Fisher's exact test), Q value = 0.036

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

nPatients N0 N1 N2
ALL 94 28 31
subtype1 25 5 3
subtype2 8 2 8
subtype3 23 13 13
subtype4 21 6 1
subtype5 17 2 6

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0459 (Fisher's exact test), Q value = 0.18

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

nPatients 0 1
ALL 129 22
subtype1 31 1
subtype2 13 5
subtype3 38 11
subtype4 25 3
subtype5 22 2

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0037 (Fisher's exact test), Q value = 0.025

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

nPatients FEMALE MALE
ALL 75 78
subtype1 24 9
subtype2 10 8
subtype3 25 24
subtype4 9 19
subtype5 7 18

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.66 (Fisher's exact test), Q value = 0.88

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

nPatients NO YES
ALL 124 3
subtype1 26 0
subtype2 14 0
subtype3 40 2
subtype4 24 0
subtype5 20 1

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00074

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 129 22
subtype1 22 11
subtype2 16 2
subtype3 49 0
subtype4 20 7
subtype5 22 2

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

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.322 (Fisher's exact test), Q value = 0.6

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

nPatients R0 R1 R2
ALL 128 1 19
subtype1 28 0 1
subtype2 15 0 3
subtype3 38 1 10
subtype4 25 0 3
subtype5 22 0 2

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

'mRNA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00953 (Kruskal-Wallis (anova)), Q value = 0.051

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

nPatients Mean (Std.Dev)
ALL 152 2.1 (4.5)
subtype1 33 1.5 (4.4)
subtype2 18 3.9 (5.8)
subtype3 49 2.7 (5.1)
subtype4 27 0.8 (2.4)
subtype5 25 2.1 (3.6)

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.599 (Fisher's exact test), Q value = 0.87

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

nPatients BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11
subtype1 0 3
subtype2 0 4
subtype3 1 2
subtype4 0 1
subtype5 0 1

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 44 70 39
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.185 (logrank test), Q value = 0.44

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

nPatients nDeath Duration Range (Median), Month
ALL 146 30 0.0 - 54.0 (23.5)
subtype1 43 10 0.0 - 53.0 (22.0)
subtype2 68 17 0.0 - 54.0 (23.0)
subtype3 35 3 0.0 - 47.0 (24.0)

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 = 0.0341 (Kruskal-Wallis (anova)), Q value = 0.16

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

nPatients Mean (Std.Dev)
ALL 153 70.8 (11.5)
subtype1 44 73.7 (11.1)
subtype2 70 71.0 (10.8)
subtype3 39 67.0 (12.5)

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

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.297 (Fisher's exact test), Q value = 0.56

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

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 29 12 45 5 8 3 12 16 21 1
subtype1 8 3 14 4 3 1 5 1 4 0
subtype2 10 7 18 1 3 2 4 11 13 1
subtype3 11 2 13 0 2 0 3 4 4 0

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.285 (Fisher's exact test), Q value = 0.56

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

nPatients T1 T2 T3 T4
ALL 4 31 103 15
subtype1 1 9 28 6
subtype2 2 10 50 8
subtype3 1 12 25 1

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.614 (Fisher's exact test), Q value = 0.88

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

nPatients N0 N1 N2
ALL 94 28 31
subtype1 30 7 7
subtype2 38 15 17
subtype3 26 6 7

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.252 (Fisher's exact test), Q value = 0.54

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

nPatients 0 1
ALL 129 22
subtype1 39 4
subtype2 56 14
subtype3 34 4

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0709 (Fisher's exact test), Q value = 0.24

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

nPatients FEMALE MALE
ALL 75 78
subtype1 27 17
subtype2 34 36
subtype3 14 25

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

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.442 (Fisher's exact test), Q value = 0.73

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

nPatients NO YES
ALL 124 3
subtype1 34 0
subtype2 57 3
subtype3 33 0

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00031

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 129 22
subtype1 25 18
subtype2 66 4
subtype3 38 0

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

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.131 (Fisher's exact test), Q value = 0.37

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

nPatients R0 R1 R2
ALL 128 1 19
subtype1 39 0 2
subtype2 55 1 13
subtype3 34 0 4

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

'mRNA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.285 (Kruskal-Wallis (anova)), Q value = 0.56

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

nPatients Mean (Std.Dev)
ALL 152 2.1 (4.5)
subtype1 44 1.9 (4.4)
subtype2 69 2.7 (5.3)
subtype3 39 1.4 (2.5)

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

'mRNA cHierClus subtypes' versus 'RACE'

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

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

nPatients BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11
subtype1 0 5
subtype2 1 5
subtype3 0 1

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

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
Number of samples 53 219 177
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.497 (logrank test), Q value = 0.77

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

nPatients nDeath Duration Range (Median), Month
ALL 436 101 0.0 - 148.0 (22.0)
subtype1 51 13 0.9 - 119.7 (16.0)
subtype2 214 51 0.5 - 148.0 (24.0)
subtype3 171 37 0.0 - 139.2 (22.1)

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 = 0.85 (Kruskal-Wallis (anova)), Q value = 0.95

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

nPatients Mean (Std.Dev)
ALL 447 66.9 (13.1)
subtype1 53 67.4 (12.3)
subtype2 219 66.7 (12.3)
subtype3 175 67.1 (14.2)

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 'PATHOLOGIC_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00074

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: '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 74 1 29 132 10 1 20 8 59 41 45 17 2
subtype1 10 0 5 18 0 0 2 1 2 4 7 2 0
subtype2 29 0 12 48 3 1 11 7 34 22 33 13 1
subtype3 35 1 12 66 7 0 7 0 23 15 5 2 1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.51 (Fisher's exact test), Q value = 0.77

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

nPatients T1 T2 T3 T4
ALL 11 77 305 55
subtype1 0 12 37 4
subtype2 6 32 155 26
subtype3 5 33 113 25

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00019 (Fisher's exact test), Q value = 0.0016

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

nPatients N0 N1 N2
ALL 262 105 82
subtype1 34 9 10
subtype2 104 68 47
subtype3 124 28 25

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00052

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

nPatients 0 1
ALL 328 64
subtype1 38 9
subtype2 146 47
subtype3 144 8

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.266 (Fisher's exact test), Q value = 0.55

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

nPatients FEMALE MALE
ALL 213 236
subtype1 25 28
subtype2 96 123
subtype3 92 85

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.708 (Fisher's exact test), Q value = 0.89

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

nPatients NO YES
ALL 370 9
subtype1 44 0
subtype2 185 6
subtype3 141 3

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00031

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 382 62
subtype1 49 3
subtype2 206 10
subtype3 127 49

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0837 (Fisher's exact test), Q value = 0.27

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

nPatients R0 R1 R2 RX
ALL 323 4 25 25
subtype1 38 0 3 4
subtype2 152 2 18 8
subtype3 133 2 4 13

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.000858 (Kruskal-Wallis (anova)), Q value = 0.0064

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

nPatients Mean (Std.Dev)
ALL 424 2.1 (4.4)
subtype1 48 2.0 (3.7)
subtype2 211 2.5 (5.0)
subtype3 165 1.6 (3.8)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0507 (Fisher's exact test), Q value = 0.19

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11 59 212
subtype1 1 4 7 23
subtype2 0 2 29 105
subtype3 0 5 23 84

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.606 (Fisher's exact test), Q value = 0.87

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 269
subtype1 1 32
subtype2 2 129
subtype3 1 108

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 84 103 106
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.808 (logrank test), Q value = 0.93

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

nPatients nDeath Duration Range (Median), Month
ALL 289 70 0.2 - 148.0 (22.1)
subtype1 84 21 0.2 - 148.0 (21.1)
subtype2 101 24 0.5 - 139.2 (24.5)
subtype3 104 25 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 = 5.22e-05 (Kruskal-Wallis (anova)), Q value = 0.00074

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

nPatients Mean (Std.Dev)
ALL 291 64.9 (13.2)
subtype1 83 59.5 (13.8)
subtype2 103 65.8 (12.2)
subtype3 105 68.2 (12.7)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.171 (Fisher's exact test), Q value = 0.42

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: '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 43 1 15 93 6 1 6 5 47 26 23 16 2
subtype1 8 0 5 30 1 0 2 0 14 9 9 3 0
subtype2 14 0 7 27 1 1 1 4 19 5 8 10 1
subtype3 21 1 3 36 4 0 3 1 14 12 6 3 1

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.327 (Fisher's exact test), Q value = 0.6

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

nPatients T1 T2 T3 T4
ALL 7 43 202 40
subtype1 2 6 63 13
subtype2 3 17 71 12
subtype3 2 20 68 15

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.133 (Fisher's exact test), Q value = 0.37

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

nPatients N0 N1 N2
ALL 171 73 49
subtype1 46 23 15
subtype2 58 32 13
subtype3 67 18 21

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

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.251 (Fisher's exact test), Q value = 0.54

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

nPatients 0 1
ALL 197 41
subtype1 60 12
subtype2 66 19
subtype3 71 10

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.305 (Fisher's exact test), Q value = 0.57

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

nPatients FEMALE MALE
ALL 135 158
subtype1 37 47
subtype2 43 60
subtype3 55 51

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.54 (Fisher's exact test), Q value = 0.81

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

nPatients NO YES
ALL 249 5
subtype1 78 1
subtype2 84 3
subtype3 87 1

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.00011 (Fisher's exact test), Q value = 0.0013

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 251 39
subtype1 66 17
subtype2 99 3
subtype3 86 19

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

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.154 (Fisher's exact test), Q value = 0.41

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

nPatients R0 R1 R2 RX
ALL 193 3 5 24
subtype1 57 0 1 6
subtype2 72 0 2 5
subtype3 64 3 2 13

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

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.633 (Kruskal-Wallis (anova)), Q value = 0.88

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

nPatients Mean (Std.Dev)
ALL 269 2.0 (4.4)
subtype1 78 2.6 (6.5)
subtype2 95 1.5 (2.8)
subtype3 96 2.0 (3.7)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.726 (Fisher's exact test), Q value = 0.9

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11 58 205
subtype1 1 4 17 60
subtype2 0 2 18 73
subtype3 0 5 23 72

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.689 (Fisher's exact test), Q value = 0.89

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 261
subtype1 2 77
subtype2 1 89
subtype3 1 95

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 87 144 127
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.456 (logrank test), Q value = 0.73

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

nPatients nDeath Duration Range (Median), Month
ALL 350 78 0.0 - 140.4 (22.0)
subtype1 84 20 0.0 - 91.8 (20.1)
subtype2 140 34 0.9 - 140.4 (24.3)
subtype3 126 24 0.0 - 135.7 (20.3)

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 = 0.154 (Kruskal-Wallis (anova)), Q value = 0.41

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

nPatients Mean (Std.Dev)
ALL 357 66.9 (13.0)
subtype1 87 65.8 (13.7)
subtype2 143 68.5 (12.3)
subtype3 127 65.9 (13.3)

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

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.382 (Fisher's exact test), Q value = 0.66

Table S58.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: '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 21 115 9 1 17 6 48 34 30 15 2
subtype1 12 1 4 28 1 0 4 2 12 10 8 4 1
subtype2 21 0 14 38 4 1 7 1 23 11 16 5 1
subtype3 21 0 3 49 4 0 6 3 13 13 6 6 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.721 (Fisher's exact test), Q value = 0.9

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

nPatients T1 T2 T3 T4
ALL 6 58 247 46
subtype1 0 16 61 9
subtype2 2 22 101 19
subtype3 4 20 85 18

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.676 (Fisher's exact test), Q value = 0.89

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

nPatients N0 N1 N2
ALL 210 85 63
subtype1 49 21 17
subtype2 80 38 26
subtype3 81 26 20

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.289 (Fisher's exact test), Q value = 0.56

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

nPatients 0 1
ALL 270 47
subtype1 66 13
subtype2 103 22
subtype3 101 12

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.488 (Fisher's exact test), Q value = 0.77

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

nPatients FEMALE MALE
ALL 173 185
subtype1 45 42
subtype2 64 80
subtype3 64 63

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 298 7
subtype1 73 2
subtype2 116 3
subtype3 109 2

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0196 (Fisher's exact test), Q value = 0.099

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 313 42
subtype1 75 12
subtype2 134 9
subtype3 104 21

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

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 4e-05 (Fisher's exact test), Q value = 0.00074

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

nPatients R0 R1 R2 RX
ALL 267 1 17 16
subtype1 75 0 5 1
subtype2 85 1 12 11
subtype3 107 0 0 4

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

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.566 (Kruskal-Wallis (anova)), Q value = 0.83

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

nPatients Mean (Std.Dev)
ALL 337 2.0 (4.4)
subtype1 85 2.1 (3.8)
subtype2 133 2.1 (3.9)
subtype3 119 1.9 (5.3)

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.375 (Fisher's exact test), Q value = 0.66

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11 48 181
subtype1 0 1 12 32
subtype2 0 7 19 67
subtype3 1 3 17 82

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.174 (Fisher's exact test), Q value = 0.42

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 234
subtype1 0 45
subtype2 2 86
subtype3 0 103

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 4 5 6 7 8 9
Number of samples 52 33 34 38 43 87 25 20 26
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.656 (logrank test), Q value = 0.88

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

nPatients nDeath Duration Range (Median), Month
ALL 350 78 0.0 - 140.4 (22.0)
subtype1 51 13 0.0 - 109.3 (18.1)
subtype2 32 8 1.0 - 139.2 (23.1)
subtype3 32 5 0.9 - 140.4 (29.5)
subtype4 37 12 2.0 - 131.5 (18.2)
subtype5 41 9 0.0 - 85.0 (27.0)
subtype6 86 16 0.0 - 135.7 (20.3)
subtype7 25 8 2.0 - 133.2 (17.9)
subtype8 20 2 8.3 - 88.2 (23.9)
subtype9 26 5 1.0 - 130.7 (24.8)

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 = 0.0571 (Kruskal-Wallis (anova)), Q value = 0.21

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

nPatients Mean (Std.Dev)
ALL 357 66.9 (13.0)
subtype1 52 67.1 (12.1)
subtype2 32 66.3 (11.5)
subtype3 34 68.2 (11.5)
subtype4 38 69.8 (13.3)
subtype5 43 69.7 (13.0)
subtype6 87 65.8 (13.7)
subtype7 25 68.8 (12.4)
subtype8 20 57.6 (14.2)
subtype9 26 66.0 (13.5)

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

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0453 (Fisher's exact test), Q value = 0.18

Table S72.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: '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 21 115 9 1 17 6 48 34 30 15 2
subtype1 11 0 0 16 0 0 0 1 9 6 7 2 0
subtype2 6 0 2 12 0 0 4 0 3 4 1 1 0
subtype3 5 0 3 6 1 0 2 2 5 5 3 1 0
subtype4 3 0 1 12 2 1 1 0 4 7 4 2 1
subtype5 4 0 9 11 2 0 3 1 6 1 6 0 0
subtype6 17 0 3 32 2 0 5 1 8 7 4 5 0
subtype7 2 0 1 13 1 0 1 0 3 1 2 0 0
subtype8 3 1 1 4 0 0 1 0 6 0 3 1 0
subtype9 3 0 1 9 1 0 0 1 4 3 0 3 1

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.843 (Fisher's exact test), Q value = 0.95

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

nPatients T1 T2 T3 T4
ALL 6 58 247 46
subtype1 0 11 32 9
subtype2 0 6 23 4
subtype3 1 7 22 4
subtype4 0 4 26 8
subtype5 0 7 33 3
subtype6 3 16 58 10
subtype7 0 2 20 3
subtype8 1 2 15 1
subtype9 1 3 18 4

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.166 (Fisher's exact test), Q value = 0.42

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

nPatients N0 N1 N2
ALL 210 85 63
subtype1 27 13 12
subtype2 21 7 5
subtype3 16 11 7
subtype4 19 6 13
subtype5 28 11 4
subtype6 58 15 14
subtype7 17 5 3
subtype8 9 9 2
subtype9 15 8 3

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.754 (Fisher's exact test), Q value = 0.91

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

nPatients 0 1
ALL 270 47
subtype1 37 9
subtype2 25 2
subtype3 27 4
subtype4 27 7
subtype5 36 6
subtype6 65 9
subtype7 20 2
subtype8 13 4
subtype9 20 4

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.655 (Fisher's exact test), Q value = 0.88

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

nPatients FEMALE MALE
ALL 173 185
subtype1 24 28
subtype2 18 15
subtype3 18 16
subtype4 16 22
subtype5 20 23
subtype6 37 50
subtype7 15 10
subtype8 9 11
subtype9 16 10

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

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.77 (Fisher's exact test), Q value = 0.91

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

nPatients NO YES
ALL 298 7
subtype1 42 2
subtype2 27 0
subtype3 28 1
subtype4 26 1
subtype5 36 2
subtype6 76 1
subtype7 21 0
subtype8 20 0
subtype9 22 0

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.365 (Fisher's exact test), Q value = 0.65

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 313 42
subtype1 44 8
subtype2 27 6
subtype3 32 2
subtype4 36 1
subtype5 39 4
subtype6 73 13
subtype7 22 3
subtype8 19 1
subtype9 21 4

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

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00776 (Fisher's exact test), Q value = 0.047

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

nPatients R0 R1 R2 RX
ALL 267 1 17 16
subtype1 40 0 1 3
subtype2 26 0 1 1
subtype3 25 1 3 1
subtype4 15 0 3 6
subtype5 36 0 5 1
subtype6 72 0 1 3
subtype7 20 0 2 0
subtype8 12 0 1 1
subtype9 21 0 0 0

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

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.257 (Kruskal-Wallis (anova)), Q value = 0.54

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

nPatients Mean (Std.Dev)
ALL 337 2.0 (4.4)
subtype1 48 3.4 (8.1)
subtype2 30 2.0 (4.2)
subtype3 33 2.1 (3.3)
subtype4 34 3.5 (5.3)
subtype5 42 1.0 (1.7)
subtype6 81 1.7 (3.6)
subtype7 24 1.2 (2.6)
subtype8 19 1.4 (2.0)
subtype9 26 1.4 (1.8)

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

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.235 (Fisher's exact test), Q value = 0.52

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11 48 181
subtype1 1 3 8 30
subtype2 0 2 8 9
subtype3 0 0 4 7
subtype4 0 2 4 28
subtype5 0 1 1 9
subtype6 0 1 14 55
subtype7 0 1 1 13
subtype8 0 1 4 13
subtype9 0 0 4 17

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.158 (Fisher's exact test), Q value = 0.41

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 2 234
subtype1 0 42
subtype2 0 17
subtype3 0 10
subtype4 1 32
subtype5 1 10
subtype6 0 70
subtype7 0 14
subtype8 0 18
subtype9 0 21

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 127 95 98 135
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.216 (logrank test), Q value = 0.48

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

nPatients nDeath Duration Range (Median), Month
ALL 443 102 0.0 - 148.0 (22.4)
subtype1 124 34 0.5 - 148.0 (25.1)
subtype2 93 16 3.0 - 135.7 (22.1)
subtype3 97 26 0.2 - 100.0 (19.7)
subtype4 129 26 0.0 - 109.3 (22.0)

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 = 0.00925 (Kruskal-Wallis (anova)), Q value = 0.051

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

nPatients Mean (Std.Dev)
ALL 453 67.0 (13.1)
subtype1 127 65.4 (12.8)
subtype2 94 66.0 (13.7)
subtype3 97 65.5 (13.1)
subtype4 135 70.1 (12.5)

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

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0352 (Fisher's exact test), Q value = 0.16

Table S86.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: '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 74 1 30 137 10 1 20 8 59 41 45 17 2
subtype1 19 0 7 31 1 1 4 2 18 10 17 11 0
subtype2 21 1 6 31 2 0 4 1 13 7 4 2 2
subtype3 11 0 4 37 1 0 2 1 15 10 12 3 0
subtype4 23 0 13 38 6 0 10 4 13 14 12 1 0

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.448 (Fisher's exact test), Q value = 0.73

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

nPatients T1 T2 T3 T4
ALL 11 77 310 56
subtype1 3 23 85 16
subtype2 2 20 62 10
subtype3 1 10 70 17
subtype4 5 24 93 13

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.448 (Fisher's exact test), Q value = 0.73

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

nPatients N0 N1 N2
ALL 268 105 82
subtype1 69 35 23
subtype2 63 20 12
subtype3 54 23 21
subtype4 82 27 26

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00473 (Fisher's exact test), Q value = 0.031

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

nPatients 0 1
ALL 334 64
subtype1 80 28
subtype2 66 8
subtype3 69 15
subtype4 119 13

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0635 (Fisher's exact test), Q value = 0.23

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

nPatients FEMALE MALE
ALL 215 240
subtype1 51 76
subtype2 51 44
subtype3 41 57
subtype4 72 63

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.749 (Fisher's exact test), Q value = 0.91

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

nPatients NO YES
ALL 377 9
subtype1 101 2
subtype2 81 2
subtype3 89 1
subtype4 106 4

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00031

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 388 62
subtype1 123 2
subtype2 72 21
subtype3 81 17
subtype4 112 22

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

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00012 (Fisher's exact test), Q value = 0.0013

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

nPatients R0 R1 R2 RX
ALL 329 4 24 25
subtype1 75 1 11 7
subtype2 62 1 2 10
subtype3 72 1 2 8
subtype4 120 1 9 0

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

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.507 (Kruskal-Wallis (anova)), Q value = 0.77

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

nPatients Mean (Std.Dev)
ALL 430 2.1 (4.4)
subtype1 119 2.0 (4.0)
subtype2 87 1.5 (2.9)
subtype3 91 2.9 (6.7)
subtype4 133 1.9 (3.6)

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.354 (Fisher's exact test), Q value = 0.63

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11 59 213
subtype1 0 4 19 70
subtype2 0 4 23 51
subtype3 1 3 14 75
subtype4 0 0 3 17

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.41 (Fisher's exact test), Q value = 0.69

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 4 270
subtype1 3 86
subtype2 0 74
subtype3 1 90
subtype4 0 20

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
Number of samples 79 108 96 133 39
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.769 (logrank test), Q value = 0.91

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

nPatients nDeath Duration Range (Median), Month
ALL 443 102 0.0 - 148.0 (22.4)
subtype1 79 19 0.5 - 91.8 (22.1)
subtype2 106 25 1.0 - 148.0 (25.1)
subtype3 94 25 0.2 - 135.7 (19.5)
subtype4 126 25 0.0 - 109.3 (24.0)
subtype5 38 8 0.0 - 54.0 (23.5)

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 = 0.000129 (Kruskal-Wallis (anova)), Q value = 0.0013

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

nPatients Mean (Std.Dev)
ALL 453 67.0 (13.1)
subtype1 79 65.4 (13.1)
subtype2 108 65.2 (12.2)
subtype3 94 64.6 (14.7)
subtype4 133 68.6 (12.3)
subtype5 39 74.9 (10.4)

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00059 (Fisher's exact test), Q value = 0.0046

Table S100.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: '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 74 1 30 137 10 1 20 8 59 41 45 17 2
subtype1 9 0 3 25 1 0 2 1 16 5 7 8 0
subtype2 15 0 9 33 1 1 3 1 17 8 8 7 0
subtype3 20 1 4 31 2 0 3 1 11 12 6 1 2
subtype4 24 0 9 35 2 0 8 5 10 16 23 1 0
subtype5 6 0 5 13 4 0 4 0 5 0 1 0 0

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.775 (Fisher's exact test), Q value = 0.91

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

nPatients T1 T2 T3 T4
ALL 11 77 310 56
subtype1 1 10 55 13
subtype2 2 16 78 12
subtype3 3 18 60 14
subtype4 4 27 91 11
subtype5 1 6 26 6

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0868 (Fisher's exact test), Q value = 0.27

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

nPatients N0 N1 N2
ALL 268 105 82
subtype1 39 28 12
subtype2 67 24 17
subtype3 60 18 18
subtype4 73 29 31
subtype5 29 6 4

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0477 (Fisher's exact test), Q value = 0.19

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

nPatients 0 1
ALL 334 64
subtype1 52 15
subtype2 72 15
subtype3 66 9
subtype4 107 24
subtype5 37 1

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.195 (Fisher's exact test), Q value = 0.44

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

nPatients FEMALE MALE
ALL 215 240
subtype1 33 46
subtype2 47 61
subtype3 47 49
subtype4 63 70
subtype5 25 14

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

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.873 (Fisher's exact test), Q value = 0.97

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

nPatients NO YES
ALL 377 9
subtype1 75 2
subtype2 86 1
subtype3 80 2
subtype4 107 3
subtype5 29 1

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00031

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 388 62
subtype1 73 6
subtype2 99 7
subtype3 71 24
subtype4 124 8
subtype5 21 17

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

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 1e-05 (Fisher's exact test), Q value = 0.00031

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

nPatients R0 R1 R2 RX
ALL 329 4 24 25
subtype1 59 0 0 5
subtype2 63 1 2 9
subtype3 61 2 2 11
subtype4 110 1 20 0
subtype5 36 0 0 0

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

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.183 (Kruskal-Wallis (anova)), Q value = 0.44

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

nPatients Mean (Std.Dev)
ALL 430 2.1 (4.4)
subtype1 74 2.5 (6.5)
subtype2 99 1.7 (3.1)
subtype3 87 1.9 (3.4)
subtype4 131 2.5 (4.8)
subtype5 39 1.0 (2.2)

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.788 (Fisher's exact test), Q value = 0.92

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11 59 213
subtype1 0 3 13 60
subtype2 0 3 25 65
subtype3 1 5 17 68
subtype4 0 0 4 15
subtype5 0 0 0 5

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.89 (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 4 270
subtype1 1 72
subtype2 2 86
subtype3 1 88
subtype4 0 19
subtype5 0 5

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 5
Number of samples 61 88 72 78 107
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.105 (logrank test), Q value = 0.32

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

nPatients nDeath Duration Range (Median), Month
ALL 394 91 0.0 - 148.0 (23.6)
subtype1 59 13 1.0 - 133.2 (29.2)
subtype2 86 14 0.2 - 148.0 (20.3)
subtype3 71 16 0.5 - 74.8 (22.1)
subtype4 75 19 0.9 - 140.4 (26.0)
subtype5 103 29 0.0 - 106.8 (24.0)

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 = 0.000141 (Kruskal-Wallis (anova)), Q value = 0.0014

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

nPatients Mean (Std.Dev)
ALL 405 67.3 (13.0)
subtype1 61 66.6 (12.7)
subtype2 87 63.9 (14.1)
subtype3 72 64.6 (13.0)
subtype4 78 68.1 (12.0)
subtype5 107 72.0 (11.8)

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

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00114 (Fisher's exact test), Q value = 0.0081

Table S114.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: '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 66 1 29 120 8 1 20 7 52 34 42 15 1
subtype1 12 0 3 17 1 1 1 0 8 6 9 2 0
subtype2 12 1 6 29 2 0 5 0 17 7 4 1 1
subtype3 7 0 2 22 1 0 1 2 14 6 7 7 0
subtype4 11 0 4 28 0 0 5 5 6 3 10 4 0
subtype5 24 0 14 24 4 0 8 0 7 12 12 1 0

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

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.196 (Fisher's exact test), Q value = 0.44

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

nPatients T1 T2 T3 T4
ALL 11 68 280 46
subtype1 3 10 41 7
subtype2 1 12 65 9
subtype3 1 8 49 14
subtype4 3 16 56 3
subtype5 3 22 69 13

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

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0706 (Fisher's exact test), Q value = 0.24

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

nPatients N0 N1 N2
ALL 238 96 72
subtype1 34 14 13
subtype2 55 23 10
subtype3 34 23 15
subtype4 46 21 11
subtype5 69 15 23

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

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.115 (Fisher's exact test), Q value = 0.33

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

nPatients 0 1
ALL 307 58
subtype1 40 11
subtype2 63 6
subtype3 49 14
subtype4 62 14
subtype5 93 13

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.881 (Fisher's exact test), Q value = 0.98

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

nPatients FEMALE MALE
ALL 191 215
subtype1 28 33
subtype2 41 47
subtype3 32 40
subtype4 35 43
subtype5 55 52

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.402 (Fisher's exact test), Q value = 0.68

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

nPatients NO YES
ALL 333 8
subtype1 51 0
subtype2 72 2
subtype3 65 2
subtype4 58 0
subtype5 87 4

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.00011 (Fisher's exact test), Q value = 0.0013

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 347 54
subtype1 59 2
subtype2 70 17
subtype3 61 11
subtype4 73 2
subtype5 84 22

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

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 5e-05 (Fisher's exact test), Q value = 0.00074

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

nPatients R0 R1 R2 RX
ALL 284 4 25 22
subtype1 36 1 3 6
subtype2 57 1 1 12
subtype3 49 0 2 3
subtype4 52 0 10 0
subtype5 90 2 9 1

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

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.403 (Kruskal-Wallis (anova)), Q value = 0.68

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

nPatients Mean (Std.Dev)
ALL 383 2.0 (4.5)
subtype1 54 2.4 (4.1)
subtype2 78 1.5 (2.9)
subtype3 69 3.2 (7.8)
subtype4 78 1.5 (3.3)
subtype5 104 1.9 (3.2)

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.711 (Fisher's exact test), Q value = 0.89

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11 23 213
subtype1 0 3 4 47
subtype2 0 6 8 69
subtype3 1 2 7 52
subtype4 0 0 1 28
subtype5 0 0 3 17

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

'MIRSEQ CNMF' versus 'ETHNICITY'

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

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 1 241
subtype1 0 51
subtype2 1 80
subtype3 0 61
subtype4 0 29
subtype5 0 20

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 79 55 47 17 56 21 27 43 61
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.546 (logrank test), Q value = 0.81

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

nPatients nDeath Duration Range (Median), Month
ALL 394 91 0.0 - 148.0 (23.6)
subtype1 79 19 0.2 - 140.4 (20.4)
subtype2 55 12 0.5 - 100.0 (16.0)
subtype3 45 7 1.0 - 139.2 (22.1)
subtype4 16 5 6.2 - 135.7 (23.7)
subtype5 54 16 0.0 - 148.0 (20.7)
subtype6 20 5 1.0 - 133.2 (34.3)
subtype7 23 5 0.9 - 45.6 (30.0)
subtype8 42 13 1.0 - 76.0 (21.0)
subtype9 60 9 0.0 - 109.3 (29.0)

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 = 0.00816 (Kruskal-Wallis (anova)), Q value = 0.047

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

nPatients Mean (Std.Dev)
ALL 405 67.3 (13.0)
subtype1 79 66.6 (13.4)
subtype2 55 63.9 (13.6)
subtype3 47 65.9 (13.1)
subtype4 16 67.2 (14.1)
subtype5 56 65.0 (13.5)
subtype6 21 65.6 (10.9)
subtype7 27 73.5 (11.4)
subtype8 43 72.6 (11.2)
subtype9 61 69.0 (12.4)

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.0749 (Fisher's exact test), Q value = 0.25

Table S128.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: '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 66 1 29 120 8 1 20 7 52 34 42 15 1
subtype1 11 0 5 30 1 0 2 1 10 5 4 3 1
subtype2 7 0 2 21 0 0 1 2 10 4 3 4 0
subtype3 7 0 3 13 0 0 4 2 7 4 6 0 0
subtype4 3 0 2 7 1 0 0 0 2 2 0 0 0
subtype5 7 1 1 13 0 1 2 0 6 10 12 2 0
subtype6 4 0 1 6 2 0 2 0 3 0 2 1 0
subtype7 4 0 0 10 0 0 2 1 3 1 6 0 0
subtype8 8 0 6 9 3 0 5 0 4 4 2 1 0
subtype9 15 0 9 11 1 0 2 1 7 4 7 4 0

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.805 (Fisher's exact test), Q value = 0.93

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

nPatients T1 T2 T3 T4
ALL 11 68 280 46
subtype1 2 13 57 7
subtype2 1 7 41 6
subtype3 2 9 33 3
subtype4 0 3 12 2
subtype5 0 7 39 9
subtype6 2 2 14 3
subtype7 1 4 17 5
subtype8 1 8 27 7
subtype9 2 15 40 4

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.0443 (Fisher's exact test), Q value = 0.18

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

nPatients N0 N1 N2
ALL 238 96 72
subtype1 54 17 8
subtype2 30 17 8
subtype3 25 14 8
subtype4 13 2 2
subtype5 24 10 22
subtype6 13 5 3
subtype7 14 9 4
subtype8 28 7 8
subtype9 37 15 9

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.164 (Fisher's exact test), Q value = 0.42

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

nPatients 0 1
ALL 307 58
subtype1 55 8
subtype2 41 7
subtype3 36 6
subtype4 14 0
subtype5 37 14
subtype6 16 3
subtype7 21 6
subtype8 39 3
subtype9 48 11

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0178 (Fisher's exact test), Q value = 0.093

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

nPatients FEMALE MALE
ALL 191 215
subtype1 29 50
subtype2 24 31
subtype3 25 22
subtype4 11 6
subtype5 24 32
subtype6 11 10
subtype7 18 9
subtype8 27 16
subtype9 22 39

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.984 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 333 8
subtype1 66 2
subtype2 50 2
subtype3 39 0
subtype4 12 0
subtype5 41 1
subtype6 14 0
subtype7 21 0
subtype8 38 1
subtype9 52 2

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.00016 (Fisher's exact test), Q value = 0.0014

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 347 54
subtype1 69 9
subtype2 43 12
subtype3 45 1
subtype4 13 3
subtype5 49 6
subtype6 19 2
subtype7 22 5
subtype8 29 14
subtype9 58 2

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

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.00015 (Fisher's exact test), Q value = 0.0014

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

nPatients R0 R1 R2 RX
ALL 284 4 25 22
subtype1 44 1 1 7
subtype2 42 0 0 5
subtype3 32 0 3 5
subtype4 9 0 0 3
subtype5 35 2 8 0
subtype6 12 0 1 1
subtype7 24 0 3 0
subtype8 36 1 1 1
subtype9 50 0 8 0

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0446 (Kruskal-Wallis (anova)), Q value = 0.18

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

nPatients Mean (Std.Dev)
ALL 383 2.0 (4.5)
subtype1 74 1.2 (2.8)
subtype2 49 1.9 (3.6)
subtype3 42 2.7 (7.9)
subtype4 15 0.9 (1.8)
subtype5 54 3.3 (4.5)
subtype6 20 1.6 (3.5)
subtype7 27 2.6 (6.2)
subtype8 43 1.9 (4.4)
subtype9 59 1.8 (3.4)

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.996 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11 23 213
subtype1 0 3 7 63
subtype2 1 3 4 47
subtype3 0 3 3 20
subtype4 0 1 2 13
subtype5 0 1 3 29
subtype6 0 0 1 13
subtype7 0 0 1 8
subtype8 0 0 1 9
subtype9 0 0 1 11

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.71 (Fisher's exact test), Q value = 0.89

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 1 241
subtype1 0 70
subtype2 1 53
subtype3 0 25
subtype4 0 15
subtype5 0 33
subtype6 0 14
subtype7 0 9
subtype8 0 10
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 5 6
Number of samples 18 20 16 11 11 9
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.626 (logrank test), Q value = 0.88

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

nPatients nDeath Duration Range (Median), Month
ALL 85 31 0.2 - 140.4 (23.9)
subtype1 18 6 7.5 - 140.4 (28.9)
subtype2 20 6 0.2 - 74.8 (26.5)
subtype3 16 6 1.4 - 91.8 (12.5)
subtype4 11 3 2.0 - 65.8 (14.3)
subtype5 11 6 6.2 - 131.5 (24.4)
subtype6 9 4 1.6 - 38.2 (13.3)

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 = 0.651 (Kruskal-Wallis (anova)), Q value = 0.88

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

nPatients Mean (Std.Dev)
ALL 84 65.1 (13.0)
subtype1 18 68.0 (9.2)
subtype2 20 62.9 (13.2)
subtype3 16 63.6 (15.8)
subtype4 11 63.2 (11.3)
subtype5 10 66.8 (17.1)
subtype6 9 67.4 (12.5)

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 'PATHOLOGIC_STAGE'

P value = 0.731 (Fisher's exact test), Q value = 0.9

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

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 9 6 26 1 1 13 10 9 3
subtype1 1 1 7 0 0 2 2 1 0
subtype2 2 2 5 0 0 4 3 3 1
subtype3 2 0 6 0 0 3 1 2 0
subtype4 1 0 2 0 1 3 0 2 2
subtype5 3 2 3 0 0 1 2 0 0
subtype6 0 1 3 1 0 0 2 1 0

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.978 (Fisher's exact test), Q value = 1

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

nPatients T1 T2 T3 T4
ALL 2 9 63 11
subtype1 1 2 13 2
subtype2 0 2 16 2
subtype3 0 2 11 3
subtype4 0 1 9 1
subtype5 1 2 7 1
subtype6 0 0 7 2

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.134 (Fisher's exact test), Q value = 0.37

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

nPatients N0 N1 N2
ALL 45 22 18
subtype1 10 5 3
subtype2 9 6 5
subtype3 9 2 5
subtype4 3 7 1
subtype5 8 2 1
subtype6 6 0 3

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.27 (Fisher's exact test), Q value = 0.55

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

nPatients 0 1
ALL 57 12
subtype1 13 1
subtype2 11 4
subtype3 12 2
subtype4 7 4
subtype5 9 0
subtype6 5 1

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.496 (Fisher's exact test), Q value = 0.77

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

nPatients FEMALE MALE
ALL 38 47
subtype1 10 8
subtype2 10 10
subtype3 5 11
subtype4 6 5
subtype5 5 6
subtype6 2 7

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.191 (Fisher's exact test), Q value = 0.44

Table S147.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 74 9
subtype1 18 0
subtype2 16 4
subtype3 15 1
subtype4 10 1
subtype5 10 1
subtype6 5 2

Figure S136.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.459 (Fisher's exact test), Q value = 0.73

Table S148.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 43 1 5
subtype1 5 1 1
subtype2 14 0 1
subtype3 7 0 0
subtype4 8 0 1
subtype5 5 0 2
subtype6 4 0 0

Figure S137.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.702 (Kruskal-Wallis (anova)), Q value = 0.89

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

nPatients Mean (Std.Dev)
ALL 80 2.6 (6.4)
subtype1 18 1.6 (3.1)
subtype2 18 5.5 (11.9)
subtype3 16 2.1 (3.8)
subtype4 10 1.2 (1.8)
subtype5 9 0.9 (1.5)
subtype6 9 3.1 (5.2)

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.264 (Fisher's exact test), Q value = 0.55

Table S150.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 3 6 73
subtype1 0 0 1 17
subtype2 0 1 3 15
subtype3 0 0 1 15
subtype4 0 0 0 11
subtype5 0 2 1 8
subtype6 1 0 0 7

Figure S139.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S151.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7 8 9
Number of samples 12 9 8 12 9 7 13 9 6
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.833 (logrank test), Q value = 0.95

Table S152.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 31 0.2 - 140.4 (23.9)
subtype1 12 5 0.2 - 62.8 (16.2)
subtype2 9 2 13.2 - 74.8 (31.6)
subtype3 8 3 2.0 - 65.8 (17.8)
subtype4 12 4 7.5 - 131.5 (31.5)
subtype5 9 4 5.1 - 91.8 (10.9)
subtype6 7 2 1.6 - 43.3 (12.9)
subtype7 13 5 1.4 - 100.0 (21.1)
subtype8 9 3 7.0 - 140.4 (14.3)
subtype9 6 3 24.4 - 130.7 (30.9)

Figure S140.  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 = 0.769 (Kruskal-Wallis (anova)), Q value = 0.91

Table S153.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 84 65.1 (13.0)
subtype1 12 65.1 (14.4)
subtype2 9 64.1 (14.0)
subtype3 8 63.9 (16.1)
subtype4 11 68.4 (12.3)
subtype5 9 64.9 (15.5)
subtype6 7 57.1 (12.2)
subtype7 13 64.3 (12.7)
subtype8 9 67.7 (10.0)
subtype9 6 69.7 (11.1)

Figure S141.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.65 (Fisher's exact test), Q value = 0.88

Table S154.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 9 6 26 1 1 13 10 9 3
subtype1 1 2 4 1 0 1 1 1 0
subtype2 0 0 3 0 0 3 2 0 1
subtype3 1 0 1 0 1 2 0 2 0
subtype4 1 2 3 0 0 3 2 0 0
subtype5 1 0 4 0 0 0 1 1 0
subtype6 0 0 2 0 0 0 2 2 1
subtype7 3 1 2 0 0 4 0 2 1
subtype8 1 1 4 0 0 0 1 0 0
subtype9 1 0 3 0 0 0 1 1 0

Figure S142.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.656 (Fisher's exact test), Q value = 0.88

Table S155.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 2 9 63 11
subtype1 0 1 8 3
subtype2 0 0 9 0
subtype3 0 2 6 0
subtype4 0 2 10 0
subtype5 0 1 7 1
subtype6 0 0 5 2
subtype7 1 2 8 2
subtype8 0 1 6 2
subtype9 1 0 4 1

Figure S143.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.288 (Fisher's exact test), Q value = 0.56

Table S156.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 45 22 18
subtype1 9 2 1
subtype2 3 4 2
subtype3 2 5 1
subtype4 7 3 2
subtype5 6 0 3
subtype6 2 2 3
subtype7 6 4 3
subtype8 7 1 1
subtype9 3 1 2

Figure S144.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.109 (Fisher's exact test), Q value = 0.33

Table S157.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 57 12
subtype1 6 1
subtype2 7 1
subtype3 4 2
subtype4 11 0
subtype5 6 1
subtype6 2 3
subtype7 9 3
subtype8 8 0
subtype9 4 1

Figure S145.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0587 (Fisher's exact test), Q value = 0.21

Table S158.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 38 47
subtype1 1 11
subtype2 7 2
subtype3 6 2
subtype4 5 7
subtype5 4 5
subtype6 3 4
subtype7 5 8
subtype8 5 4
subtype9 2 4

Figure S146.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0364 (Fisher's exact test), Q value = 0.16

Table S159.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 74 9
subtype1 11 1
subtype2 8 1
subtype3 7 1
subtype4 12 0
subtype5 8 1
subtype6 3 4
subtype7 12 1
subtype8 7 0
subtype9 6 0

Figure S147.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.345 (Fisher's exact test), Q value = 0.63

Table S160.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 43 1 5
subtype1 7 0 0
subtype2 7 0 0
subtype3 5 0 2
subtype4 3 0 0
subtype5 3 0 0
subtype6 4 0 0
subtype7 8 0 2
subtype8 4 0 0
subtype9 2 1 1

Figure S148.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.111 (Kruskal-Wallis (anova)), Q value = 0.33

Table S161.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 80 2.6 (6.4)
subtype1 12 1.2 (3.4)
subtype2 8 3.8 (4.9)
subtype3 6 1.5 (2.3)
subtype4 12 1.0 (1.8)
subtype5 9 2.3 (4.8)
subtype6 7 4.6 (5.4)
subtype7 11 5.9 (14.7)
subtype8 9 0.6 (1.3)
subtype9 6 3.7 (4.4)

Figure S149.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.685 (Fisher's exact test), Q value = 0.89

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 3 6 73
subtype1 0 0 1 10
subtype2 0 0 2 7
subtype3 0 1 0 7
subtype4 0 1 2 9
subtype5 0 0 1 8
subtype6 1 0 0 6
subtype7 0 1 0 12
subtype8 0 0 0 8
subtype9 0 0 0 6

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

Methods & Data
Input
  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/COAD-TP/22541003/COAD-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/COAD-TP/22506513/COAD-TP.merged_data.txt

  • Number of patients = 457

  • 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)