Correlation between aggregated molecular cancer subtypes and selected clinical features
Colon Adenocarcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DR2TBJ
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 12 clinical features across 448 patients, 21 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 'HISTOLOGICAL.TYPE'.

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

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.N.STAGE',  'HISTOLOGICAL.TYPE', and 'NUMBER.OF.LYMPH.NODES'.

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

  • CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.M.STAGE',  'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'AGE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.M.STAGE',  'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE',  'PATHOLOGY.M.STAGE', and 'COMPLETENESS.OF.RESECTION'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE',  'PATHOLOGY.M.STAGE', and 'COMPLETENESS.OF.RESECTION'.

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

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'GENDER'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 21 significant findings detected.

Clinical
Features
Time
to
Death
AGE NEOPLASM
DISEASESTAGE
PATHOLOGY
T
STAGE
PATHOLOGY
N
STAGE
PATHOLOGY
M
STAGE
GENDER HISTOLOGICAL
TYPE
RADIATIONS
RADIATION
REGIMENINDICATION
COMPLETENESS
OF
RESECTION
NUMBER
OF
LYMPH
NODES
RACE
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Kruskal-Wallis (anova) Fisher's exact test
mRNA CNMF subtypes 0.755
(1.00)
0.597
(1.00)
0.00323
(0.381)
0.683
(1.00)
0.00881
(0.987)
0.0326
(1.00)
0.0361
(1.00)
1e-05
(0.0014)
0.161
(1.00)
0.00264
(0.314)
0.455
(1.00)
mRNA cHierClus subtypes 0.477
(1.00)
0.0658
(1.00)
0.628
(1.00)
0.65
(1.00)
0.787
(1.00)
0.897
(1.00)
0.0812
(1.00)
1e-05
(0.0014)
0.545
(1.00)
0.765
(1.00)
1
(1.00)
Copy Number Ratio CNMF subtypes 0.997
(1.00)
0.0746
(1.00)
0.00913
(1.00)
0.705
(1.00)
0.00159
(0.192)
0.00777
(0.878)
0.341
(1.00)
1e-05
(0.0014)
0.186
(1.00)
0.0325
(1.00)
0.00107
(0.133)
0.928
(1.00)
METHLYATION CNMF 0.949
(1.00)
0.00112
(0.138)
0.476
(1.00)
0.28
(1.00)
0.753
(1.00)
0.289
(1.00)
0.0618
(1.00)
0.00709
(0.815)
0.483
(1.00)
0.446
(1.00)
0.871
(1.00)
0.585
(1.00)
RPPA CNMF subtypes 0.83
(1.00)
0.88
(1.00)
0.398
(1.00)
0.645
(1.00)
0.528
(1.00)
0.0492
(1.00)
0.825
(1.00)
0.0357
(1.00)
0.498
(1.00)
0.00736
(0.839)
0.16
(1.00)
0.174
(1.00)
RPPA cHierClus subtypes 0.0961
(1.00)
0.446
(1.00)
0.0119
(1.00)
0.0741
(1.00)
0.182
(1.00)
0.195
(1.00)
0.597
(1.00)
0.447
(1.00)
0.282
(1.00)
0.0921
(1.00)
0.0472
(1.00)
0.263
(1.00)
RNAseq CNMF subtypes 0.978
(1.00)
0.0034
(0.398)
0.0281
(1.00)
0.801
(1.00)
0.255
(1.00)
1e-05
(0.0014)
0.676
(1.00)
3e-05
(0.0039)
0.318
(1.00)
2e-05
(0.00262)
0.953
(1.00)
0.989
(1.00)
RNAseq cHierClus subtypes 0.903
(1.00)
0.000168
(0.0213)
0.0017
(0.204)
0.706
(1.00)
0.142
(1.00)
1e-05
(0.0014)
0.346
(1.00)
1e-05
(0.0014)
0.138
(1.00)
1e-05
(0.0014)
0.235
(1.00)
0.959
(1.00)
MIRSEQ CNMF 0.201
(1.00)
0.000566
(0.0707)
0.0456
(1.00)
0.384
(1.00)
0.29
(1.00)
1e-05
(0.0014)
0.446
(1.00)
0.0991
(1.00)
0.0696
(1.00)
1e-05
(0.0014)
0.517
(1.00)
0.676
(1.00)
MIRSEQ CHIERARCHICAL 0.953
(1.00)
0.000101
(0.0129)
0.138
(1.00)
0.894
(1.00)
0.0816
(1.00)
3e-05
(0.0039)
0.473
(1.00)
0.308
(1.00)
0.0636
(1.00)
0.00021
(0.0265)
0.162
(1.00)
0.277
(1.00)
MIRseq Mature CNMF subtypes 0.147
(1.00)
0.00416
(0.482)
0.554
(1.00)
0.584
(1.00)
0.225
(1.00)
0.428
(1.00)
0.73
(1.00)
0.034
(1.00)
0.484
(1.00)
0.0241
(1.00)
0.66
(1.00)
MIRseq Mature cHierClus subtypes 0.822
(1.00)
0.589
(1.00)
0.817
(1.00)
0.837
(1.00)
0.0508
(1.00)
0.472
(1.00)
0.0012
(0.146)
0.337
(1.00)
0.941
(1.00)
0.0469
(1.00)
0.994
(1.00)
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 38 61 30 24
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.755 (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 141 26 0.9 - 54.0 (23.0)
subtype1 34 7 0.9 - 46.6 (23.7)
subtype2 56 12 1.0 - 51.0 (22.0)
subtype3 28 4 1.0 - 54.0 (18.6)
subtype4 23 3 0.9 - 50.0 (27.0)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.597 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 153 70.8 (11.5)
subtype1 38 72.4 (11.4)
subtype2 61 69.9 (11.7)
subtype3 30 72.0 (10.9)
subtype4 24 68.9 (12.3)

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00323 (Fisher's exact test), Q value = 0.38

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

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 1 14 3 3 1 3 1 5 0
subtype2 11 2 12 0 2 2 5 13 14 0
subtype3 7 6 9 2 1 0 3 0 1 1
subtype4 5 3 10 0 2 0 1 2 1 0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.683 (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 7 24 6
subtype2 2 12 42 5
subtype3 0 7 19 4
subtype4 1 5 18 0

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 = 0.00881 (Fisher's exact test), Q value = 0.99

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 8
subtype2 27 15 19
subtype3 24 5 1
subtype4 18 3 3

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 = 0.0326 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 M1A
ALL 129 21 1
subtype1 32 5 0
subtype2 47 14 0
subtype3 28 1 1
subtype4 22 1 0

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 = 0.0361 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 75 78
subtype1 25 13
subtype2 31 30
subtype3 10 20
subtype4 9 15

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 129 22
subtype1 25 13
subtype2 60 1
subtype3 22 7
subtype4 22 1

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

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

P value = 0.00264 (Kruskal-Wallis (anova)), Q value = 1

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 152 2.1 (4.5)
subtype1 38 2.7 (5.8)
subtype2 61 2.8 (4.8)
subtype3 29 0.7 (2.3)
subtype4 24 1.2 (2.6)

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.455 (Fisher's exact test), Q value = 0.31

Table S11.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients BLACK OR AFRICAN AMERICAN WHITE
ALL 1 11
subtype1 0 6
subtype2 1 4
subtype3 0 0
subtype4 0 1

Figure S10.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S12.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 46 73 34
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.477 (logrank test), Q value = 1

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 141 26 0.9 - 54.0 (23.0)
subtype1 42 8 0.9 - 53.0 (23.2)
subtype2 69 15 1.0 - 54.0 (21.0)
subtype3 30 3 0.9 - 39.0 (24.0)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0658 (Kruskal-Wallis (anova)), Q value = 1

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 153 70.8 (11.5)
subtype1 46 73.4 (11.8)
subtype2 73 70.6 (10.6)
subtype3 34 67.5 (12.5)

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.628 (Fisher's exact test), Q value = 1

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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 9 3 14 4 3 1 5 1 5 0
subtype2 12 7 20 1 4 2 4 10 12 1
subtype3 8 2 11 0 1 0 3 5 4 0

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.65 (Fisher's exact test), Q value = 1

Table S16.  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 10 28 7
subtype2 2 13 51 7
subtype3 1 8 24 1

Figure S14.  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 = 0.787 (Fisher's exact test), Q value = 1

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 94 28 31
subtype1 31 7 8
subtype2 42 16 15
subtype3 21 5 8

Figure S15.  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 = 0.897 (Fisher's exact test), Q value = 1

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A
ALL 129 21 1
subtype1 40 5 0
subtype2 60 12 1
subtype3 29 4 0

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0812 (Fisher's exact test), Q value = 1

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 75 78
subtype1 28 18
subtype2 35 38
subtype3 12 22

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 129 22
subtype1 27 18
subtype2 69 4
subtype3 33 0

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.765 (Kruskal-Wallis (anova)), Q value = 1

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 152 2.1 (4.5)
subtype1 46 2.3 (5.3)
subtype2 72 2.2 (4.6)
subtype3 34 1.7 (2.7)

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'mRNA cHierClus subtypes' versus 'RACE'

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

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

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

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

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

Table S23.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 201 174 64
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.997 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 422 87 0.1 - 140.4 (19.6)
subtype1 191 40 0.1 - 139.2 (20.1)
subtype2 168 34 0.1 - 140.4 (20.0)
subtype3 63 13 0.9 - 119.7 (17.3)

Figure S21.  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 'AGE'

P value = 0.0746 (Kruskal-Wallis (anova)), Q value = 1

Table S25.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 437 67.0 (13.0)
subtype1 199 68.0 (14.1)
subtype2 174 66.7 (12.2)
subtype3 64 64.5 (11.5)

Figure S22.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00913 (Fisher's exact test), Q value = 1

Table S26.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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 72 1 28 131 8 1 21 12 55 38 43 18 2
subtype1 40 1 15 72 6 0 10 3 21 14 11 3 1
subtype2 22 0 6 45 2 1 7 8 24 18 23 12 1
subtype3 10 0 7 14 0 0 4 1 10 6 9 3 0

Figure S23.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.705 (Fisher's exact test), Q value = 1

Table S27.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 76 300 51
subtype1 6 38 130 26
subtype2 5 26 124 19
subtype3 0 12 46 6

Figure S24.  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 = 0.00159 (Fisher's exact test), Q value = 0.19

Table S28.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 256 103 79
subtype1 138 35 28
subtype2 83 53 37
subtype3 35 15 14

Figure S25.  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 = 0.00777 (Fisher's exact test), Q value = 0.88

Table S29.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 320 52 8 2 50
subtype1 159 12 1 1 25
subtype2 117 30 5 1 18
subtype3 44 10 2 0 7

Figure S26.  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 = 0.341 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 207 232
subtype1 102 99
subtype2 75 99
subtype3 30 34

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S31.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 378 59
subtype1 154 47
subtype2 166 6
subtype3 58 6

Figure S28.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.186 (Fisher's exact test), Q value = 1

Table S32.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 436
subtype1 0 201
subtype2 2 172
subtype3 1 63

Figure S29.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0325 (Fisher's exact test), Q value = 1

Table S33.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 313 3 24 25
subtype1 147 2 4 15
subtype2 122 1 13 7
subtype3 44 0 7 3

Figure S30.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.00107 (Kruskal-Wallis (anova)), Q value = 0.13

Table S34.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 415 2.1 (4.4)
subtype1 188 1.7 (4.1)
subtype2 167 2.6 (5.2)
subtype3 60 1.9 (2.9)

Figure S31.  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 = 0.928 (Fisher's exact test), Q value = 1

Table S35.  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 50 211
subtype1 1 6 23 97
subtype2 0 3 19 85
subtype3 0 2 8 29

Figure S32.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S36.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 115 92 71
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.949 (logrank test), Q value = 1

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 274 60 0.1 - 140.4 (17.8)
subtype1 113 27 0.1 - 140.4 (22.0)
subtype2 90 19 0.1 - 135.7 (17.3)
subtype3 71 14 0.1 - 102.4 (15.6)

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00112 (Kruskal-Wallis (anova)), Q value = 0.14

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 277 65.1 (13.1)
subtype1 115 65.9 (12.5)
subtype2 92 67.5 (13.1)
subtype3 70 60.3 (13.0)

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.476 (Fisher's exact test), Q value = 1

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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 41 1 14 89 5 1 7 9 43 23 20 16 1
subtype1 16 0 6 32 1 1 3 4 20 8 10 10 0
subtype2 19 1 3 29 3 0 4 2 12 8 4 4 1
subtype3 6 0 5 28 1 0 0 3 11 7 6 2 0

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.28 (Fisher's exact test), Q value = 1

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 7 42 193 35
subtype1 3 19 81 12
subtype2 3 18 58 12
subtype3 1 5 54 11

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.753 (Fisher's exact test), Q value = 1

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 163 70 44
subtype1 63 32 19
subtype2 58 19 15
subtype3 42 19 10

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.289 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 190 28 7 1 47
subtype1 73 16 4 0 21
subtype2 63 5 2 1 18
subtype3 54 7 1 0 8

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0618 (Fisher's exact test), Q value = 1

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 126 152
subtype1 46 69
subtype2 51 41
subtype3 29 42

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00709 (Fisher's exact test), Q value = 0.82

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 244 34
subtype1 109 6
subtype2 76 16
subtype3 59 12

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.483 (Fisher's exact test), Q value = 1

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 275
subtype1 2 113
subtype2 0 92
subtype3 1 70

Figure S41.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.446 (Fisher's exact test), Q value = 1

Table S46.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 179 2 4 23
subtype1 72 0 3 9
subtype2 60 2 1 10
subtype3 47 0 0 4

Figure S42.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.871 (Kruskal-Wallis (anova)), Q value = 1

Table S47.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 255 1.9 (4.4)
subtype1 106 2.2 (5.7)
subtype2 84 1.5 (2.7)
subtype3 65 2.1 (3.8)

Figure S43.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.585 (Fisher's exact test), Q value = 1

Table S48.  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 43 205
subtype1 0 4 18 82
subtype2 0 2 14 71
subtype3 1 5 11 52

Figure S44.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 80 128 27 96
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.83 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 319 65 0.1 - 140.4 (20.3)
subtype1 78 12 0.1 - 131.5 (21.0)
subtype2 121 29 0.1 - 140.4 (21.0)
subtype3 26 4 1.0 - 52.0 (23.0)
subtype4 94 20 0.1 - 135.7 (19.6)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.88 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 330 67.3 (13.0)
subtype1 80 66.8 (12.9)
subtype2 127 68.1 (12.3)
subtype3 27 66.9 (11.9)
subtype4 96 66.7 (14.3)

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

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.398 (Fisher's exact test), Q value = 1

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

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 49 1 20 108 8 1 18 8 43 29 28 14 1
subtype1 10 0 6 23 3 0 2 3 14 10 5 4 0
subtype2 18 0 10 34 3 1 8 2 18 8 18 5 1
subtype3 3 1 1 12 0 0 2 0 3 3 1 1 0
subtype4 18 0 3 39 2 0 6 3 8 8 4 4 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.645 (Fisher's exact test), Q value = 1

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

nPatients T1 T2 T3 T4
ALL 6 53 233 38
subtype1 0 12 59 9
subtype2 2 21 90 15
subtype3 0 3 22 1
subtype4 4 17 62 13

Figure S48.  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 = 0.528 (Fisher's exact test), Q value = 1

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

nPatients N0 N1 N2
ALL 196 80 55
subtype1 42 23 15
subtype2 72 32 24
subtype3 17 6 4
subtype4 65 19 12

Figure S49.  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 = 0.0492 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 M1A M1B MX
ALL 259 36 6 1 26
subtype1 65 6 3 0 5
subtype2 92 23 0 1 11
subtype3 24 2 0 0 1
subtype4 78 5 3 0 9

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.825 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 155 176
subtype1 38 42
subtype2 56 72
subtype3 13 14
subtype4 48 48

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0357 (Fisher's exact test), Q value = 1

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 293 38
subtype1 68 12
subtype2 121 7
subtype3 23 4
subtype4 81 15

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

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

P value = 0.498 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 3 328
subtype1 2 78
subtype2 1 127
subtype3 0 27
subtype4 0 96

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

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

P value = 0.00736 (Fisher's exact test), Q value = 0.84

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

nPatients R0 R2 RX
ALL 241 16 16
subtype1 61 1 6
subtype2 78 13 6
subtype3 26 1 0
subtype4 76 1 4

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

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

P value = 0.16 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 311 2.0 (4.5)
subtype1 74 2.9 (6.8)
subtype2 120 2.0 (3.8)
subtype3 27 1.7 (3.3)
subtype4 90 1.2 (2.6)

Figure S55.  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 = 0.174 (Fisher's exact test), Q value = 1

Table S61.  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 21 181
subtype1 1 5 7 43
subtype2 0 5 6 63
subtype3 0 0 2 6
subtype4 0 1 6 69

Figure S56.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S62.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 60 49 56 68 69 29
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0961 (logrank test), Q value = 1

Table S63.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 319 65 0.1 - 140.4 (20.3)
subtype1 59 8 0.1 - 131.5 (18.1)
subtype2 45 8 0.9 - 85.0 (24.0)
subtype3 55 18 0.1 - 119.9 (18.0)
subtype4 65 11 0.2 - 140.4 (22.4)
subtype5 67 10 0.1 - 135.7 (20.1)
subtype6 28 10 2.0 - 107.7 (18.0)

Figure S57.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.446 (Kruskal-Wallis (anova)), Q value = 1

Table S64.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 330 67.3 (13.0)
subtype1 60 66.2 (11.5)
subtype2 49 70.1 (12.9)
subtype3 55 67.5 (14.0)
subtype4 68 66.5 (12.4)
subtype5 69 66.1 (14.0)
subtype6 29 68.9 (13.1)

Figure S58.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0119 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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 49 1 20 108 8 1 18 8 43 29 28 14 1
subtype1 7 0 1 22 1 0 0 2 10 7 4 6 0
subtype2 10 0 7 7 1 0 6 1 5 4 7 1 0
subtype3 6 0 2 16 3 1 1 0 11 7 6 2 1
subtype4 7 1 6 20 1 0 7 2 10 4 6 2 0
subtype5 17 0 2 28 1 0 2 3 4 6 3 3 0
subtype6 2 0 2 15 1 0 2 0 3 1 2 0 0

Figure S59.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0741 (Fisher's exact test), Q value = 1

Table S66.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 6 53 233 38
subtype1 0 7 45 8
subtype2 0 13 33 3
subtype3 0 7 38 11
subtype4 2 8 51 6
subtype5 4 16 42 7
subtype6 0 2 24 3

Figure S60.  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 = 0.182 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 196 80 55
subtype1 32 15 13
subtype2 28 13 8
subtype3 28 15 13
subtype4 37 22 9
subtype5 51 10 8
subtype6 20 5 4

Figure S61.  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 = 0.195 (Fisher's exact test), Q value = 1

Table S68.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 259 36 6 1 26
subtype1 46 7 3 0 3
subtype2 40 8 0 0 1
subtype3 42 8 0 1 5
subtype4 51 8 0 0 9
subtype5 57 3 3 0 6
subtype6 23 2 0 0 2

Figure S62.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.597 (Fisher's exact test), Q value = 1

Table S69.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 155 176
subtype1 27 33
subtype2 25 24
subtype3 23 33
subtype4 34 34
subtype5 29 40
subtype6 17 12

Figure S63.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.447 (Fisher's exact test), Q value = 1

Table S70.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 293 38
subtype1 51 9
subtype2 46 3
subtype3 52 4
subtype4 61 7
subtype5 58 11
subtype6 25 4

Figure S64.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.282 (Fisher's exact test), Q value = 1

Table S71.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 328
subtype1 2 58
subtype2 0 49
subtype3 1 55
subtype4 0 68
subtype5 0 69
subtype6 0 29

Figure S65.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0921 (Fisher's exact test), Q value = 1

Table S72.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R2 RX
ALL 241 16 16
subtype1 44 2 2
subtype2 40 5 1
subtype3 30 1 7
subtype4 49 5 2
subtype5 56 1 3
subtype6 22 2 1

Figure S66.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0472 (Kruskal-Wallis (anova)), Q value = 1

Table S73.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 311 2.0 (4.5)
subtype1 56 3.2 (7.6)
subtype2 48 1.5 (2.3)
subtype3 51 2.8 (5.0)
subtype4 65 1.6 (3.0)
subtype5 64 1.2 (3.0)
subtype6 27 1.2 (2.5)

Figure S67.  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 = 0.263 (Fisher's exact test), Q value = 1

Table S74.  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 21 181
subtype1 0 3 5 43
subtype2 0 0 1 14
subtype3 1 4 4 41
subtype4 0 1 7 19
subtype5 0 1 3 50
subtype6 0 2 1 14

Figure S68.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S75.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 139 151 148
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.978 (logrank test), Q value = 1

Table S76.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 421 88 0.1 - 140.4 (20.0)
subtype1 136 31 0.1 - 140.4 (20.4)
subtype2 148 30 0.1 - 135.7 (16.9)
subtype3 137 27 0.9 - 83.8 (24.0)

Figure S69.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0034 (Kruskal-Wallis (anova)), Q value = 0.4

Table S77.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 437 67.1 (13.0)
subtype1 139 65.2 (12.0)
subtype2 150 66.2 (14.0)
subtype3 148 69.8 (12.4)

Figure S70.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0281 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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 72 1 27 133 9 1 21 12 55 38 42 17 1
subtype1 22 0 8 38 1 1 4 4 21 9 13 13 0
subtype2 24 1 7 54 2 0 5 4 20 15 12 3 1
subtype3 26 0 12 41 6 0 12 4 14 14 17 1 0

Figure S71.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.801 (Fisher's exact test), Q value = 1

Table S79.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 76 299 51
subtype1 3 26 94 16
subtype2 4 21 104 21
subtype3 4 29 101 14

Figure S72.  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 = 0.255 (Fisher's exact test), Q value = 1

Table S80.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 258 102 77
subtype1 78 41 19
subtype2 91 31 29
subtype3 89 30 29

Figure S73.  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 = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S81.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 325 50 8 1 47
subtype1 90 19 7 0 22
subtype2 108 14 0 1 25
subtype3 127 17 1 0 0

Figure S74.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.676 (Fisher's exact test), Q value = 1

Table S82.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 206 232
subtype1 61 78
subtype2 73 78
subtype3 72 76

Figure S75.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.0039

Table S83.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 379 57
subtype1 134 5
subtype2 119 32
subtype3 126 20

Figure S76.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.318 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 435
subtype1 2 137
subtype2 1 150
subtype3 0 148

Figure S77.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 2e-05 (Fisher's exact test), Q value = 0.0026

Table S85.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 313 3 23 24
subtype1 87 0 5 10
subtype2 98 2 3 14
subtype3 128 1 15 0

Figure S78.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.953 (Kruskal-Wallis (anova)), Q value = 1

Table S86.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 414 2.0 (4.4)
subtype1 130 2.1 (5.7)
subtype2 138 2.1 (3.9)
subtype3 146 1.9 (3.6)

Figure S79.  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 = 0.989 (Fisher's exact test), Q value = 1

Table S87.  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 44 213
subtype1 0 5 18 91
subtype2 1 6 22 106
subtype3 0 0 4 16

Figure S80.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S88.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 104 74 89 132 39
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.903 (logrank test), Q value = 1

Table S89.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 421 88 0.1 - 140.4 (20.0)
subtype1 102 24 0.1 - 140.4 (20.4)
subtype2 74 15 0.3 - 91.8 (16.1)
subtype3 87 20 0.1 - 135.7 (16.1)
subtype4 122 22 0.9 - 83.8 (24.0)
subtype5 36 7 1.0 - 54.0 (23.7)

Figure S81.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000168 (Kruskal-Wallis (anova)), Q value = 0.021

Table S90.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 437 67.1 (13.0)
subtype1 104 65.8 (11.7)
subtype2 74 64.7 (13.1)
subtype3 88 64.8 (14.7)
subtype4 132 68.6 (12.4)
subtype5 39 74.8 (10.5)

Figure S82.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0017 (Fisher's exact test), Q value = 0.2

Table S91.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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 72 1 27 133 9 1 21 12 55 38 42 17 1
subtype1 17 0 7 31 1 1 3 2 16 6 7 9 0
subtype2 7 0 3 24 1 0 2 2 15 6 6 6 0
subtype3 18 1 4 30 1 0 4 3 9 10 5 1 1
subtype4 26 0 8 34 2 0 8 5 10 16 22 1 0
subtype5 4 0 5 14 4 0 4 0 5 0 2 0 0

Figure S83.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.706 (Fisher's exact test), Q value = 1

Table S92.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 76 299 51
subtype1 2 19 71 12
subtype2 1 8 54 11
subtype3 3 16 58 11
subtype4 4 29 88 11
subtype5 1 4 28 6

Figure S84.  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 = 0.142 (Fisher's exact test), Q value = 1

Table S93.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 258 102 77
subtype1 65 24 14
subtype2 36 26 12
subtype3 56 17 16
subtype4 73 29 30
subtype5 28 6 5

Figure S85.  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 = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S94.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 325 50 8 1 47
subtype1 70 11 4 0 18
subtype2 49 9 3 0 13
subtype3 63 6 0 1 16
subtype4 107 22 1 0 0
subtype5 36 2 0 0 0

Figure S86.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.346 (Fisher's exact test), Q value = 1

Table S95.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 206 232
subtype1 47 57
subtype2 31 43
subtype3 40 49
subtype4 64 68
subtype5 24 15

Figure S87.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S96.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 379 57
subtype1 96 8
subtype2 67 7
subtype3 71 18
subtype4 122 8
subtype5 23 16

Figure S88.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.138 (Fisher's exact test), Q value = 1

Table S97.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 435
subtype1 1 103
subtype2 2 72
subtype3 0 89
subtype4 0 132
subtype5 0 39

Figure S89.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S98.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 313 3 23 24
subtype1 58 0 2 10
subtype2 54 0 0 5
subtype3 56 2 1 9
subtype4 110 1 19 0
subtype5 35 0 1 0

Figure S90.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.235 (Kruskal-Wallis (anova)), Q value = 1

Table S99.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 414 2.0 (4.4)
subtype1 95 1.6 (3.0)
subtype2 69 2.6 (6.7)
subtype3 81 1.7 (3.2)
subtype4 130 2.3 (4.4)
subtype5 39 1.6 (4.3)

Figure S91.  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 = 0.959 (Fisher's exact test), Q value = 1

Table S100.  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 44 213
subtype1 0 3 16 71
subtype2 1 3 11 56
subtype3 0 5 13 66
subtype4 0 0 4 15
subtype5 0 0 0 5

Figure S92.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S101.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 160 66 180
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.201 (logrank test), Q value = 1

Table S102.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 389 81 0.1 - 140.4 (21.0)
subtype1 158 31 0.1 - 140.4 (20.4)
subtype2 66 14 0.1 - 100.0 (13.4)
subtype3 165 36 0.9 - 83.8 (24.0)

Figure S93.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.000566 (Kruskal-Wallis (anova)), Q value = 0.071

Table S103.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 405 67.3 (13.0)
subtype1 159 66.5 (12.4)
subtype2 66 62.6 (14.4)
subtype3 180 69.9 (12.5)

Figure S94.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0456 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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 28 121 8 1 21 11 49 32 42 16 1
subtype1 24 1 7 56 2 1 7 4 24 11 11 7 1
subtype2 7 0 3 21 0 0 2 3 10 8 4 4 0
subtype3 35 0 18 44 6 0 12 4 15 13 27 5 0

Figure S95.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.384 (Fisher's exact test), Q value = 1

Table S105.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 69 279 46
subtype1 5 24 115 15
subtype2 0 9 46 11
subtype3 6 36 118 20

Figure S96.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.29 (Fisher's exact test), Q value = 1

Table S106.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 239 95 71
subtype1 98 39 22
subtype2 33 19 14
subtype3 108 37 35

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S107.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 305 50 7 1 36
subtype1 113 12 5 1 26
subtype2 48 8 0 0 9
subtype3 144 30 2 0 1

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.446 (Fisher's exact test), Q value = 1

Table S108.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 191 215
subtype1 74 86
subtype2 27 39
subtype3 90 90

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0991 (Fisher's exact test), Q value = 1

Table S109.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 351 53
subtype1 145 15
subtype2 53 13
subtype3 153 25

Figure S100.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0696 (Fisher's exact test), Q value = 1

Table S110.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 403
subtype1 0 160
subtype2 2 64
subtype3 1 179

Figure S101.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S111.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 282 3 25 22
subtype1 94 1 2 15
subtype2 43 0 0 5
subtype3 145 2 23 2

Figure S102.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.517 (Kruskal-Wallis (anova)), Q value = 1

Table S112.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 382 2.0 (4.5)
subtype1 146 1.6 (3.0)
subtype2 61 3.2 (8.0)
subtype3 175 2.0 (3.8)

Figure S103.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.676 (Fisher's exact test), Q value = 1

Table S113.  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 7 12 132
subtype2 1 3 7 51
subtype3 0 1 4 30

Figure S104.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S114.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 136 50 79 141
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.953 (logrank test), Q value = 1

Table S115.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 389 81 0.1 - 140.4 (21.0)
subtype1 133 30 0.1 - 140.4 (20.1)
subtype2 50 9 0.1 - 100.0 (15.0)
subtype3 74 15 0.1 - 139.2 (18.0)
subtype4 132 27 0.9 - 131.5 (27.0)

Figure S105.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000101 (Kruskal-Wallis (anova)), Q value = 0.013

Table S116.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 405 67.3 (13.0)
subtype1 135 66.3 (13.3)
subtype2 50 63.2 (13.1)
subtype3 79 64.9 (13.1)
subtype4 141 71.2 (11.9)

Figure S106.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.138 (Fisher's exact test), Q value = 1

Table S117.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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 28 121 8 1 21 11 49 32 42 16 1
subtype1 20 0 7 50 4 1 6 3 15 10 9 6 1
subtype2 6 0 2 19 0 0 1 3 8 2 3 4 0
subtype3 11 1 4 19 0 0 4 3 12 8 14 1 0
subtype4 29 0 15 33 4 0 10 2 14 12 16 5 0

Figure S107.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.894 (Fisher's exact test), Q value = 1

Table S118.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 69 279 46
subtype1 4 21 94 17
subtype2 1 6 39 4
subtype3 2 13 56 7
subtype4 4 29 90 18

Figure S108.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0816 (Fisher's exact test), Q value = 1

Table S119.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 239 95 71
subtype1 90 26 19
subtype2 27 17 6
subtype3 38 21 20
subtype4 84 31 26

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 3e-05 (Fisher's exact test), Q value = 0.0039

Table S120.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 305 50 7 1 36
subtype1 97 11 3 1 21
subtype2 36 6 1 0 7
subtype3 55 14 1 0 8
subtype4 117 19 2 0 0

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.473 (Fisher's exact test), Q value = 1

Table S121.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 191 215
subtype1 57 79
subtype2 23 27
subtype3 40 39
subtype4 71 70

Figure S111.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.308 (Fisher's exact test), Q value = 1

Table S122.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 351 53
subtype1 120 16
subtype2 40 10
subtype3 71 7
subtype4 120 20

Figure S112.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0636 (Fisher's exact test), Q value = 1

Table S123.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 403
subtype1 0 136
subtype2 2 48
subtype3 0 79
subtype4 1 140

Figure S113.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.00021 (Fisher's exact test), Q value = 0.026

Table S124.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 282 3 25 22
subtype1 76 1 3 11
subtype2 37 0 0 5
subtype3 51 1 9 5
subtype4 118 1 13 1

Figure S114.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.162 (Kruskal-Wallis (anova)), Q value = 1

Table S125.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 382 2.0 (4.5)
subtype1 128 1.6 (3.2)
subtype2 44 1.6 (2.9)
subtype3 72 2.9 (6.5)
subtype4 138 2.2 (4.7)

Figure S115.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.277 (Fisher's exact test), Q value = 1

Table S126.  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 4 11 106
subtype2 1 3 3 43
subtype3 0 4 6 32
subtype4 0 0 3 32

Figure S116.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S127.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 29 32 24
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.147 (logrank test), Q value = 1

Table S128.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 27 0.1 - 140.4 (16.2)
subtype1 29 12 0.1 - 140.4 (12.0)
subtype2 32 10 0.1 - 100.0 (15.5)
subtype3 24 5 0.2 - 131.5 (25.7)

Figure S117.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.00416 (Kruskal-Wallis (anova)), Q value = 0.48

Table S129.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 84 65.1 (13.0)
subtype1 29 68.1 (13.2)
subtype2 32 59.5 (13.1)
subtype3 23 69.1 (10.3)

Figure S118.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.554 (Fisher's exact test), Q value = 1

Table S130.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 9 6 27 1 1 14 9 9 3
subtype1 2 1 13 1 0 2 2 2 1
subtype2 3 3 7 0 0 7 5 5 2
subtype3 4 2 7 0 1 5 2 2 0

Figure S119.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.584 (Fisher's exact test), Q value = 1

Table S131.  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 21 5
subtype2 1 2 25 4
subtype3 0 5 17 2

Figure S120.  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 = 0.225 (Fisher's exact test), Q value = 1

Table S132.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 46 22 17
subtype1 19 4 6
subtype2 13 11 8
subtype3 14 7 3

Figure S121.  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 = 0.428 (Fisher's exact test), Q value = 1

Table S133.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 56 10 2 14
subtype1 18 2 1 6
subtype2 18 6 1 6
subtype3 20 2 0 2

Figure S122.  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 = 0.73 (Fisher's exact test), Q value = 1

Table S134.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 38 47
subtype1 14 15
subtype2 15 17
subtype3 9 15

Figure S123.  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 = 0.034 (Fisher's exact test), Q value = 1

Table S135.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 75 10
subtype1 26 3
subtype2 25 7
subtype3 24 0

Figure S124.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0241 (Kruskal-Wallis (anova)), Q value = 1

Table S136.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 80 2.6 (6.4)
subtype1 29 1.7 (3.6)
subtype2 27 4.8 (10.0)
subtype3 24 1.3 (2.5)

Figure S125.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.66 (Fisher's exact test), Q value = 1

Table S137.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 3 6 73
subtype1 0 0 1 27
subtype2 1 2 3 25
subtype3 0 1 2 21

Figure S126.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S138.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 12 22 17 24 10
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.822 (logrank test), Q value = 1

Table S139.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 85 27 0.1 - 140.4 (16.2)
subtype1 12 4 0.2 - 62.8 (11.6)
subtype2 22 7 1.3 - 61.9 (18.8)
subtype3 17 5 0.2 - 131.5 (28.2)
subtype4 24 7 0.1 - 100.0 (11.5)
subtype5 10 4 1.3 - 140.4 (13.8)

Figure S127.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.589 (Kruskal-Wallis (anova)), Q value = 1

Table S140.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 84 65.1 (13.0)
subtype1 12 65.1 (14.4)
subtype2 22 62.7 (14.6)
subtype3 16 67.9 (11.3)
subtype4 24 63.7 (13.0)
subtype5 10 69.2 (10.6)

Figure S128.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

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

Table S141.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 9 6 27 1 1 14 9 9 3
subtype1 1 2 4 1 0 1 1 1 0
subtype2 1 0 4 0 1 6 2 5 2
subtype3 2 2 6 0 0 3 2 1 0
subtype4 4 1 8 0 0 3 3 2 1
subtype5 1 1 5 0 0 1 1 0 0

Figure S129.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.837 (Fisher's exact test), Q value = 1

Table S142.  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 2 19 1
subtype3 1 2 13 1
subtype4 1 3 16 4
subtype5 0 1 7 2

Figure S130.  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 = 0.0508 (Fisher's exact test), Q value = 1

Table S143.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 46 22 17
subtype1 9 2 1
subtype2 5 11 6
subtype3 10 4 3
subtype4 14 4 6
subtype5 8 1 1

Figure S131.  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 = 0.472 (Fisher's exact test), Q value = 1

Table S144.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 56 10 2 14
subtype1 6 1 0 4
subtype2 12 6 1 3
subtype3 14 1 0 2
subtype4 16 2 1 4
subtype5 8 0 0 1

Figure S132.  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 = 0.0012 (Fisher's exact test), Q value = 0.15

Table S145.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 38 47
subtype1 1 11
subtype2 17 5
subtype3 7 10
subtype4 8 16
subtype5 5 5

Figure S133.  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 = 0.337 (Fisher's exact test), Q value = 1

Table S146.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 75 10
subtype1 11 1
subtype2 19 3
subtype3 17 0
subtype4 19 5
subtype5 9 1

Figure S134.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0469 (Kruskal-Wallis (anova)), Q value = 1

Table S147.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 80 2.6 (6.4)
subtype1 12 1.2 (3.4)
subtype2 19 5.6 (11.5)
subtype3 17 1.6 (2.9)
subtype4 22 2.3 (4.1)
subtype5 10 1.2 (2.4)

Figure S135.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.994 (Fisher's exact test), Q value = 1

Table S148.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: '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 1 2 19
subtype3 0 1 2 14
subtype4 1 1 1 21
subtype5 0 0 0 9

Figure S136.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

Methods & Data
Input
  • Cluster data file = COAD-TP.mergedcluster.txt

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

  • Number of patients = 448

  • Number of clustering approaches = 12

  • Number of selected clinical features = 12

  • 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)