Colon Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 9 clinical features across 422 patients, 13 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 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

  • 4 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE',  'PATHOLOGY.N', and 'TUMOR.STAGE'.

  • 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 4 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GENDER',  'HISTOLOGICAL.TYPE', and 'TUMOR.STAGE'.

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

  • CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE' and 'PATHOLOGICSPREAD(M)'.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'PATHOLOGICSPREAD(M)'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 13 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER HISTOLOGICAL
TYPE 		PATHOLOGY
T 		PATHOLOGY
N 		PATHOLOGICSPREAD(M) TUMOR
STAGE 		RADIATIONS
RADIATION
REGIMENINDICATION
Statistical Tests logrank test ANOVA Fisher's exact test Fisher's exact test Chi-square test Chi-square test Chi-square test Chi-square test Fisher's exact test
mRNA CNMF subtypes 0.31
(1.00) 		0.418
(1.00) 		0.0625
(1.00) 		3.12e-06
(0.000262) 		0.89
(1.00) 		0.0267
(1.00) 		0.0549
(1.00) 		0.0374
(1.00)
mRNA cHierClus subtypes 0.755
(1.00) 		0.146
(1.00) 		0.0978
(1.00) 		3.57e-09
(3.14e-07) 		0.561
(1.00) 		0.58
(1.00) 		0.848
(1.00) 		0.509
(1.00)
CN CNMF 0.755
(1.00) 		0.472
(1.00) 		0.487
(1.00) 		3.7e-08
(3.22e-06) 		0.944
(1.00) 		0.00113
(0.0895) 		0.00999
(0.729) 		3.6e-05
(0.00292) 		0.337
(1.00)
METHLYATION CNMF 0.928
(1.00) 		0.00205
(0.16) 		0.0531
(1.00) 		0.0769
(1.00) 		0.375
(1.00) 		0.904
(1.00) 		0.304
(1.00) 		0.036
(1.00) 		0.473
(1.00)
RPPA CNMF subtypes 0.429
(1.00) 		0.501
(1.00) 		0.306
(1.00) 		0.015
(1.00) 		0.23
(1.00) 		0.141
(1.00) 		0.186
(1.00) 		0.117
(1.00) 		0.302
(1.00)
RPPA cHierClus subtypes 0.302
(1.00) 		0.169
(1.00) 		0.348
(1.00) 		0.464
(1.00) 		0.28
(1.00) 		0.123
(1.00) 		0.0432
(1.00) 		0.0747
(1.00) 		0.339
(1.00)
RNAseq CNMF subtypes 0.871
(1.00) 		0.105
(1.00) 		0.00324
(0.246) 		2.4e-07
(2.04e-05) 		0.7
(1.00) 		0.0246
(1.00) 		0.022
(1.00) 		0.00265
(0.204) 		1
(1.00)
RNAseq cHierClus subtypes 0.311
(1.00) 		0.00338
(0.254) 		0.456
(1.00) 		8.97e-08
(7.71e-06) 		0.638
(1.00) 		0.238
(1.00) 		0.342
(1.00) 		0.631
(1.00) 		1
(1.00)
MIRseq CNMF subtypes 0.246
(1.00) 		0.000748
(0.0599) 		0.525
(1.00) 		0.0162
(1.00) 		0.264
(1.00) 		0.515
(1.00) 		1.25e-05
(0.00102) 		0.596
(1.00) 		0.544
(1.00)
MIRseq cHierClus subtypes 0.521
(1.00) 		0.00978
(0.724) 		0.514
(1.00) 		0.67
(1.00) 		0.498
(1.00) 		0.95
(1.00) 		7.64e-06
(0.000634) 		0.448
(1.00) 		1
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 63 35 31 26
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.31 (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 75 11 0.9 - 52.0 (5.0)
subtype1 27 6 1.0 - 41.0 (14.4)
subtype2 22 1 1.0 - 30.0 (1.0)
subtype3 18 3 1.0 - 52.0 (8.0)
subtype4 8 1 0.9 - 1.0 (1.0)

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 = 0.418 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 155 70.6 (11.7)
subtype1 63 69.2 (11.9)
subtype2 35 72.1 (11.7)
subtype3 31 72.7 (10.9)
subtype4 26 69.3 (12.1)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0625 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 76 79
subtype1 32 31
subtype2 23 12
subtype3 11 20
subtype4 10 16

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.12e-06 (Fisher's exact test), Q value = 0.00026

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 128 24
subtype1 62 1
subtype2 22 12
subtype3 21 9
subtype4 23 2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.89 (Chi-square test), Q value = 1

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 4 31 105 13
subtype1 2 13 43 5
subtype2 1 7 23 2
subtype3 0 6 20 5
subtype4 1 5 19 1

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0267 (Chi-square test), Q value = 1

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

nPatients N0 N1 N2
ALL 95 28 32
subtype1 29 15 19
subtype2 24 4 7
subtype3 24 6 1
subtype4 18 3 5

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

'mRNA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0549 (Chi-square test), Q value = 1

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A
ALL 129 22 1
subtype1 48 15 0
subtype2 30 4 0
subtype3 28 1 1
subtype4 23 2 0

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0374 (Chi-square test), Q value = 1

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

nPatients I II III IV
ALL 28 63 39 22
subtype1 12 15 21 15
subtype2 6 17 7 3
subtype3 5 18 5 2
subtype4 5 13 6 2

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 47 48 14 46
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.755 (logrank test), Q value = 1

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 75 11 0.9 - 52.0 (5.0)
subtype1 28 2 1.0 - 30.0 (1.0)
subtype2 27 5 1.0 - 41.0 (15.0)
subtype3 7 2 0.9 - 52.0 (14.4)
subtype4 13 2 0.9 - 34.0 (1.0)

Figure S9.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.146 (ANOVA), Q value = 1

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 155 70.6 (11.7)
subtype1 47 73.1 (11.5)
subtype2 48 70.7 (9.6)
subtype3 14 71.6 (12.8)
subtype4 46 67.5 (13.0)

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0978 (Fisher's exact test), Q value = 1

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 76 79
subtype1 27 20
subtype2 27 21
subtype3 6 8
subtype4 16 30

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.57e-09 (Fisher's exact test), Q value = 3.1e-07

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 128 24
subtype1 25 20
subtype2 46 2
subtype3 12 2
subtype4 45 0

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.561 (Chi-square test), Q value = 1

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 4 31 105 13
subtype1 1 10 28 6
subtype2 1 7 36 4
subtype3 1 1 11 1
subtype4 1 13 30 2

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.58 (Chi-square test), Q value = 1

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 95 28 32
subtype1 31 7 9
subtype2 25 13 10
subtype3 10 1 3
subtype4 29 7 10

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'mRNA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.848 (Chi-square test), Q value = 1

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A
ALL 129 22 1
subtype1 40 6 0
subtype2 38 8 1
subtype3 12 2 0
subtype4 39 6 0

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.509 (Chi-square test), Q value = 1

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 28 63 39 22
subtype1 9 21 10 5
subtype2 5 17 16 9
subtype3 2 8 2 2
subtype4 12 17 11 6

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #3: 'CN CNMF'

Table S19.  Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels 1 2 3 4
Number of samples 170 187 32 24
'CN CNMF' versus 'Time to Death'

P value = 0.755 (logrank test), Q value = 1

Table S20.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 319 49 0.1 - 135.5 (7.7)
subtype1 127 23 0.1 - 129.1 (10.0)
subtype2 149 19 0.1 - 129.1 (6.1)
subtype3 23 4 0.3 - 135.5 (7.0)
subtype4 20 3 0.2 - 112.7 (6.0)

Figure S17.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.472 (ANOVA), Q value = 1

Table S21.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 412 67.3 (13.0)
subtype1 170 66.4 (12.4)
subtype2 186 68.3 (13.7)
subtype3 32 66.0 (13.0)
subtype4 24 68.2 (12.0)

Figure S18.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.487 (Fisher's exact test), Q value = 1

Table S22.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 193 220
subtype1 77 93
subtype2 92 95
subtype3 16 16
subtype4 8 16

Figure S19.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 3.7e-08 (Fisher's exact test), Q value = 3.2e-06

Table S23.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 356 54
subtype1 162 7
subtype2 140 45
subtype3 31 1
subtype4 23 1

Figure S20.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.944 (Chi-square test), Q value = 1

Table S24.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 11 72 283 45
subtype1 5 27 120 18
subtype2 6 33 124 22
subtype3 0 6 23 3
subtype4 0 6 16 2

Figure S21.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.00113 (Chi-square test), Q value = 0.089

Table S25.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 245 95 71
subtype1 78 54 36
subtype2 131 29 27
subtype3 20 7 5
subtype4 16 5 3

Figure S22.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.00999 (Chi-square test), Q value = 0.73

Table S26.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 312 49 7 1 36
subtype1 119 32 3 0 14
subtype2 153 10 1 1 18
subtype3 21 6 2 0 2
subtype4 19 1 1 0 2

Figure S23.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 3.6e-05 (Chi-square test), Q value = 0.0029

Table S27.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 67 157 116 56
subtype1 23 46 60 35
subtype2 35 89 44 11
subtype3 4 13 5 8
subtype4 5 9 7 2

Figure S24.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.337 (Fisher's exact test), Q value = 1

Table S28.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 410
subtype1 3 167
subtype2 0 187
subtype3 0 32
subtype4 0 24

Figure S25.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S29.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 106 86 63
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.928 (logrank test), Q value = 1

Table S30.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 237 36 0.1 - 135.5 (7.6)
subtype1 95 15 0.1 - 135.5 (8.0)
subtype2 81 13 0.1 - 129.1 (8.0)
subtype3 61 8 0.1 - 102.4 (5.8)

Figure S26.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00205 (ANOVA), Q value = 0.16

Table S31.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 254 65.4 (13.3)
subtype1 106 66.3 (13.1)
subtype2 86 68.0 (13.1)
subtype3 62 60.5 (12.6)

Figure S27.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0531 (Fisher's exact test), Q value = 1

Table S32.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 115 140
subtype1 42 64
subtype2 48 38
subtype3 25 38

Figure S28.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0769 (Fisher's exact test), Q value = 1

Table S33.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 225 30
subtype1 99 7
subtype2 73 13
subtype3 53 10

Figure S29.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.375 (Chi-square test), Q value = 1

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 7 38 178 31
subtype1 3 18 72 13
subtype2 3 16 58 8
subtype3 1 4 48 10

Figure S30.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.904 (Chi-square test), Q value = 1

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 152 64 37
subtype1 59 29 16
subtype2 53 20 13
subtype3 40 15 8

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.304 (Chi-square test), Q value = 1

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 180 27 6 1 36
subtype1 69 16 4 0 16
subtype2 61 6 1 1 14
subtype3 50 5 1 0 6

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.036 (Chi-square test), Q value = 1

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 37 98 72 34
subtype1 15 34 31 20
subtype2 18 32 23 9
subtype3 4 32 18 5

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 252
subtype1 2 104
subtype2 0 86
subtype3 1 62

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S39.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 65 119 7 78
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.429 (logrank test), Q value = 1

Table S40.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 206 19 0.1 - 105.3 (6.0)
subtype1 56 2 0.1 - 87.8 (5.2)
subtype2 82 7 0.1 - 75.2 (6.5)
subtype3 2 0 0.6 - 18.1 (9.4)
subtype4 66 10 0.1 - 105.3 (6.0)

Figure S35.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.501 (ANOVA), Q value = 1

Table S41.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 268 66.6 (13.3)
subtype1 65 66.7 (12.0)
subtype2 118 66.2 (13.4)
subtype3 7 74.1 (9.4)
subtype4 78 66.3 (14.6)

Figure S36.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.306 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 127 142
subtype1 34 31
subtype2 55 64
subtype3 1 6
subtype4 37 41

Figure S37.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.015 (Fisher's exact test), Q value = 1

Table S43.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 238 30
subtype1 51 14
subtype2 112 7
subtype3 6 1
subtype4 69 8

Figure S38.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.23 (Chi-square test), Q value = 1

Table S44.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 5 45 185 31
subtype1 0 9 46 10
subtype2 1 22 82 11
subtype3 0 0 7 0
subtype4 4 14 50 10

Figure S39.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.141 (Chi-square test), Q value = 1

Table S45.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 160 66 42
subtype1 33 19 12
subtype2 66 31 22
subtype3 6 0 1
subtype4 55 16 7

Figure S40.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RPPA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.186 (Chi-square test), Q value = 1

Table S46.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 202 31 6 1 26
subtype1 50 7 3 0 4
subtype2 84 20 0 1 13
subtype3 7 0 0 0 0
subtype4 61 4 3 0 9

Figure S41.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.117 (Chi-square test), Q value = 1

Table S47.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 40 107 77 38
subtype1 7 24 22 10
subtype2 19 39 35 21
subtype3 0 6 1 0
subtype4 14 38 19 7

Figure S42.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.302 (Fisher's exact test), Q value = 1

Table S48.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 266
subtype1 2 63
subtype2 1 118
subtype3 0 7
subtype4 0 78

Figure S43.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S49.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 143 25 49 52
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.302 (logrank test), Q value = 1

Table S50.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 206 19 0.1 - 105.3 (6.0)
subtype1 100 11 0.1 - 103.0 (6.6)
subtype2 15 1 0.1 - 105.3 (8.0)
subtype3 44 6 0.1 - 100.0 (5.1)
subtype4 47 1 0.1 - 87.8 (5.4)

Figure S44.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.169 (ANOVA), Q value = 1

Table S51.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 268 66.6 (13.3)
subtype1 142 68.0 (13.6)
subtype2 25 67.9 (12.0)
subtype3 49 63.9 (13.8)
subtype4 52 64.6 (12.6)

Figure S45.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.348 (Fisher's exact test), Q value = 1

Table S52.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 127 142
subtype1 71 72
subtype2 14 11
subtype3 18 31
subtype4 24 28

Figure S46.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.464 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 238 30
subtype1 129 13
subtype2 23 2
subtype3 41 8
subtype4 45 7

Figure S47.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.28 (Chi-square test), Q value = 1

Table S54.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 5 45 185 31
subtype1 1 23 99 17
subtype2 0 3 20 2
subtype3 3 12 30 4
subtype4 1 7 36 8

Figure S48.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.123 (Chi-square test), Q value = 1

Table S55.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 160 66 42
subtype1 78 39 26
subtype2 19 5 1
subtype3 36 8 5
subtype4 27 14 10

Figure S49.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RPPA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0432 (Chi-square test), Q value = 1

Table S56.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 202 31 6 1 26
subtype1 103 22 0 1 17
subtype2 22 0 0 0 1
subtype3 39 2 3 0 5
subtype4 38 7 3 0 3

Figure S50.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0747 (Chi-square test), Q value = 1

Table S57.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 40 107 77 38
subtype1 20 51 45 23
subtype2 2 16 6 0
subtype3 11 23 10 5
subtype4 7 17 16 10

Figure S51.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.339 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 266
subtype1 1 142
subtype2 0 25
subtype3 0 49
subtype4 2 50

Figure S52.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S59.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 68 61 40 23
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.871 (logrank test), Q value = 1

Table S60.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 108 16 0.9 - 71.7 (12.4)
subtype1 36 6 0.9 - 71.7 (15.9)
subtype2 41 5 1.0 - 49.0 (8.1)
subtype3 25 5 0.9 - 52.0 (12.0)
subtype4 6 0 1.0 - 4.0 (1.0)

Figure S53.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.105 (ANOVA), Q value = 1

Table S61.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 192 69.7 (12.4)
subtype1 68 66.8 (13.0)
subtype2 61 72.0 (12.2)
subtype3 40 70.6 (10.9)
subtype4 23 70.1 (12.9)

Figure S54.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00324 (Fisher's exact test), Q value = 0.25

Table S62.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 99 93
subtype1 38 30
subtype2 40 21
subtype3 13 27
subtype4 8 15

Figure S55.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.4e-07 (Fisher's exact test), Q value = 2e-05

Table S63.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 162 27
subtype1 67 0
subtype2 41 19
subtype3 32 7
subtype4 22 1

Figure S56.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.7 (Chi-square test), Q value = 1

Table S64.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 6 38 128 18
subtype1 2 15 44 7
subtype2 2 8 41 8
subtype3 1 8 28 3
subtype4 1 7 15 0

Figure S57.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0246 (Chi-square test), Q value = 1

Table S65.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 114 41 36
subtype1 31 20 16
subtype2 34 15 12
subtype3 31 4 5
subtype4 18 2 3

Figure S58.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.022 (Chi-square test), Q value = 1

Table S66.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A
ALL 159 28 2
subtype1 47 18 1
subtype2 53 7 1
subtype3 37 2 0
subtype4 22 1 0

Figure S59.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.00265 (Chi-square test), Q value = 0.2

Table S67.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 35 73 50 27
subtype1 11 16 21 17
subtype2 8 25 19 7
subtype3 8 22 6 2
subtype4 8 10 4 1

Figure S60.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S68.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 191
subtype1 1 67
subtype2 0 61
subtype3 0 40
subtype4 0 23

Figure S61.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S69.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 67 19 106
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.311 (logrank test), Q value = 1

Table S70.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 108 16 0.9 - 71.7 (12.4)
subtype1 44 8 1.0 - 52.0 (8.0)
subtype2 11 0 7.0 - 34.0 (15.2)
subtype3 53 8 0.9 - 71.7 (10.9)

Figure S62.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00338 (ANOVA), Q value = 0.25

Table S71.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 192 69.7 (12.4)
subtype1 67 73.4 (11.6)
subtype2 19 71.1 (12.6)
subtype3 106 67.0 (12.4)

Figure S63.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.456 (Fisher's exact test), Q value = 1

Table S72.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 99 93
subtype1 36 31
subtype2 12 7
subtype3 51 55

Figure S64.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.97e-08 (Fisher's exact test), Q value = 7.7e-06

Table S73.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 162 27
subtype1 43 22
subtype2 17 2
subtype3 102 3

Figure S65.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.638 (Chi-square test), Q value = 1

Table S74.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 6 38 128 18
subtype1 2 14 41 8
subtype2 0 2 14 3
subtype3 4 22 73 7

Figure S66.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.238 (Chi-square test), Q value = 1

Table S75.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 114 41 36
subtype1 44 15 8
subtype2 10 2 6
subtype3 60 24 22

Figure S67.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.342 (Chi-square test), Q value = 1

Table S76.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A
ALL 159 28 2
subtype1 59 6 1
subtype2 13 5 0
subtype3 87 17 1

Figure S68.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.631 (Chi-square test), Q value = 1

Table S77.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 35 73 50 27
subtype1 14 29 16 6
subtype2 2 7 4 4
subtype3 19 37 30 17

Figure S69.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S78.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 191
subtype1 0 67
subtype2 0 19
subtype3 1 105

Figure S70.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S79.  Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 182 163 62
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.246 (logrank test), Q value = 1

Table S80.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 309 49 0.1 - 135.5 (7.5)
subtype1 98 17 0.9 - 71.7 (7.3)
subtype2 153 26 0.1 - 135.5 (9.4)
subtype3 58 6 0.1 - 100.0 (4.3)

Figure S71.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.000748 (ANOVA), Q value = 0.06

Table S81.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 406 67.3 (13.1)
subtype1 182 69.8 (12.5)
subtype2 162 66.0 (12.6)
subtype3 62 63.3 (14.8)

Figure S72.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.525 (Fisher's exact test), Q value = 1

Table S82.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 192 215
subtype1 91 91
subtype2 75 88
subtype3 26 36

Figure S73.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0162 (Fisher's exact test), Q value = 1

Table S83.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 349 55
subtype1 154 25
subtype2 148 15
subtype3 47 15

Figure S74.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.264 (Chi-square test), Q value = 1

Table S84.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 11 69 279 45
subtype1 6 37 120 17
subtype2 5 23 117 17
subtype3 0 9 42 11

Figure S75.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.515 (Chi-square test), Q value = 1

Table S85.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 241 94 70
subtype1 111 36 34
subtype2 98 40 25
subtype3 32 18 11

Figure S76.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 1.25e-05 (Chi-square test), Q value = 0.001

Table S86.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 305 50 7 1 36
subtype1 148 28 1 0 1
subtype2 111 15 5 1 28
subtype3 46 7 1 0 7

Figure S77.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.596 (Chi-square test), Q value = 1

Table S87.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 65 155 113 57
subtype1 34 69 45 27
subtype2 26 63 48 22
subtype3 5 23 20 8

Figure S78.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.544 (Fisher's exact test), Q value = 1

Table S88.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 404
subtype1 1 181
subtype2 1 162
subtype3 1 61

Figure S79.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S89.  Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 180 45 182
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.521 (logrank test), Q value = 1

Table S90.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 309 49 0.1 - 135.5 (7.5)
subtype1 97 16 0.9 - 71.7 (9.1)
subtype2 42 8 0.1 - 129.1 (6.7)
subtype3 170 25 0.1 - 135.5 (7.0)

Figure S80.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.00978 (ANOVA), Q value = 0.72

Table S91.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 406 67.3 (13.1)
subtype1 180 69.5 (12.6)
subtype2 45 66.4 (11.8)
subtype3 181 65.3 (13.6)

Figure S81.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.514 (Fisher's exact test), Q value = 1

Table S92.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 192 215
subtype1 90 90
subtype2 22 23
subtype3 80 102

Figure S82.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.67 (Fisher's exact test), Q value = 1

Table S93.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 349 55
subtype1 151 26
subtype2 41 4
subtype3 157 25

Figure S83.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.498 (Chi-square test), Q value = 1

Table S94.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 11 69 279 45
subtype1 6 36 119 17
subtype2 2 5 30 7
subtype3 3 28 130 21

Figure S84.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.95 (Chi-square test), Q value = 1

Table S95.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 241 94 70
subtype1 107 39 33
subtype2 27 10 8
subtype3 107 45 29

Figure S85.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 7.64e-06 (Chi-square test), Q value = 0.00063

Table S96.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 305 50 7 1 36
subtype1 145 29 2 0 0
subtype2 30 5 2 0 6
subtype3 130 16 3 1 30

Figure S86.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.448 (Chi-square test), Q value = 1

Table S97.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 65 155 113 57
subtype1 34 64 45 28
subtype2 8 18 10 7
subtype3 23 73 58 22

Figure S87.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S98.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 404
subtype1 1 179
subtype2 0 45
subtype3 2 180

Figure S88.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data
Input

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

  • Clinical data file = COAD-TP.clin.merged.picked.txt

  • Number of patients = 422

  • Number of clustering approaches = 10

  • Number of selected clinical features = 9

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

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

Chi-square test

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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