Colon Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 8 different clustering approaches and 10 clinical features across 422 patients, 20 significant findings detected with P value < 0.05.

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

  • 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',  'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE',  'GENDER',  'HISTOLOGICAL.TYPE', and 'TUMOR.STAGE'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'PATHOLOGICSPREAD(M)'.

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

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'AGE',  'PATHOLOGICSPREAD(M)', and 'NEOADJUVANT.THERAPY'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 10 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 20 significant findings detected.

Clinical
Features 		Statistical
Tests 		mRNA
CNMF
subtypes 		mRNA
cHierClus
subtypes 		CN
CNMF 		METHLYATION
CNMF 		RPPA
CNMF
subtypes 		RPPA
cHierClus
subtypes 		MIRseq
CNMF
subtypes 		MIRseq
cHierClus
subtypes
Time to Death logrank test 0.0584 0.275 0.275 0.503 0.193 0.0562 0.0619 0.289
AGE ANOVA 0.418 0.146 0.472 0.00333 0.501 0.169 0.00164 0.00948
GENDER Fisher's exact test 0.0625 0.0978 0.487 0.0449 0.306 0.348 0.989 0.869
HISTOLOGICAL TYPE Fisher's exact test 3.12e-06 3.57e-09 3.7e-08 0.0271 0.015 0.464 0.237 0.741
PATHOLOGY T Chi-square test 0.89 0.561 0.944 0.24 0.23 0.28 0.494 0.491
PATHOLOGY N Chi-square test 0.0267 0.58 0.00113 0.91 0.141 0.123 0.294 0.148
PATHOLOGICSPREAD(M) Chi-square test 0.0549 0.848 0.00999 0.329 0.186 0.0432 1.86e-05 3.19e-05
TUMOR STAGE Chi-square test 0.0374 0.509 3.6e-05 0.0284 0.117 0.0747 0.425 0.353
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.337 0.618 0.302 0.339 0.229 1
NEOADJUVANT THERAPY Fisher's exact test 0.266 0.732 0.391 0.78 0.172 0.387 0.000649 0.00854
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 = 0.0584 (logrank test)

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)

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)

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)

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)

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)

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)

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)

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 = 0.275 (logrank test)

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)

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)

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)

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)

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)

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)

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)

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 = 0.275 (logrank test)

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)

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)

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)

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)

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)

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)

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)

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)

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'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.391 (Fisher's exact test)

Table S29.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 58 355
subtype1 28 142
subtype2 26 161
subtype3 3 29
subtype4 1 23

Figure S26.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S30.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 106 82 66
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.503 (logrank test)

Table S31.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 236 36 0.1 - 135.5 (7.5)
subtype1 95 15 0.1 - 135.5 (8.0)
subtype2 77 12 0.1 - 129.1 (8.1)
subtype3 64 9 0.1 - 102.4 (5.5)

Figure S27.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00333 (ANOVA)

Table S32.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 253 65.4 (13.3)
subtype1 106 66.6 (13.0)
subtype2 82 67.5 (13.2)
subtype3 65 60.7 (12.8)

Figure S28.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0449 (Fisher's exact test)

Table S33.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 114 140
subtype1 41 65
subtype2 46 36
subtype3 27 39

Figure S29.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0271 (Fisher's exact test)

Table S34.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 224 30
subtype1 100 6
subtype2 69 13
subtype3 55 11

Figure S30.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.24 (Chi-square test)

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 7 38 177 31
subtype1 3 18 72 13
subtype2 3 16 55 7
subtype3 1 4 50 11

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.91 (Chi-square test)

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 152 63 37
subtype1 59 28 17
subtype2 52 19 11
subtype3 41 16 9

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.329 (Chi-square test)

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 179 27 6 1 36
subtype1 70 16 4 0 15
subtype2 58 5 1 1 14
subtype3 51 6 1 0 7

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.0284 (Chi-square test)

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 37 98 71 34
subtype1 15 34 31 20
subtype2 18 31 21 8
subtype3 4 33 19 6

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.618 (Fisher's exact test)

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 251
subtype1 2 104
subtype2 0 82
subtype3 1 65

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.78 (Fisher's exact test)

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 51 203
subtype1 19 87
subtype2 18 64
subtype3 14 52

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S41.  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 = 0.193 (logrank test)

Table S42.  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 S37.  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)

Table S43.  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 S38.  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)

Table S44.  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 S39.  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)

Table S45.  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 S40.  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)

Table S46.  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 S41.  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)

Table S47.  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 S42.  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)

Table S48.  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 S43.  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)

Table S49.  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 S44.  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)

Table S50.  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 S45.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.172 (Fisher's exact test)

Table S51.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 51 218
subtype1 18 47
subtype2 20 99
subtype3 0 7
subtype4 13 65

Figure S46.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S52.  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 = 0.0562 (logrank test)

Table S53.  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 S47.  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)

Table S54.  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 S48.  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)

Table S55.  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 S49.  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)

Table S56.  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 S50.  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)

Table S57.  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 S51.  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)

Table S58.  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 S52.  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)

Table S59.  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 S53.  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)

Table S60.  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 S54.  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)

Table S61.  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 S55.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.387 (Fisher's exact test)

Table S62.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 51 218
subtype1 23 120
subtype2 5 20
subtype3 9 40
subtype4 14 38

Figure S56.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S63.  Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 156 46 176
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0619 (logrank test)

Table S64.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 280 49 0.1 - 135.5 (8.1)
subtype1 144 25 0.1 - 135.5 (9.6)
subtype2 43 9 0.1 - 100.0 (5.2)
subtype3 93 15 0.9 - 71.7 (8.1)

Figure S57.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.00164 (ANOVA)

Table S65.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 378 67.4 (13.1)
subtype1 156 66.1 (12.9)
subtype2 46 62.8 (14.0)
subtype3 176 69.7 (12.6)

Figure S58.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.989 (Fisher's exact test)

Table S66.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 183 195
subtype1 75 81
subtype2 22 24
subtype3 86 90

Figure S59.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.237 (Fisher's exact test)

Table S67.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 327 48
subtype1 141 15
subtype2 38 8
subtype3 148 25

Figure S60.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.494 (Chi-square test)

Table S68.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 10 64 264 37
subtype1 4 22 113 16
subtype2 0 6 34 6
subtype3 6 36 117 15

Figure S61.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.294 (Chi-square test)

Table S69.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 222 89 66
subtype1 93 38 25
subtype2 22 16 8
subtype3 107 35 33

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 1.86e-05 (Chi-square test)

Table S70.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 286 49 7 1 28
subtype1 109 14 5 1 24
subtype2 34 7 1 0 4
subtype3 143 28 1 0 0

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.425 (Chi-square test)

Table S71.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 62 144 106 55
subtype1 25 62 47 21
subtype2 3 17 16 8
subtype3 34 65 43 26

Figure S64.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.229 (Fisher's exact test)

Table S72.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 376
subtype1 0 156
subtype2 1 45
subtype3 1 175

Figure S65.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.000649 (Fisher's exact test)

Table S73.  Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 55 323
subtype1 32 124
subtype2 10 36
subtype3 13 163

Figure S66.  Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S74.  Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 179 50 108 41
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.289 (logrank test)

Table S75.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 280 49 0.1 - 135.5 (8.1)
subtype1 96 16 0.9 - 71.7 (8.6)
subtype2 46 9 0.1 - 129.1 (6.7)
subtype3 99 16 0.1 - 135.5 (9.4)
subtype4 39 8 0.9 - 124.1 (8.9)

Figure S67.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.00948 (ANOVA)

Table S76.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 378 67.4 (13.1)
subtype1 179 69.6 (12.5)
subtype2 50 66.3 (12.1)
subtype3 108 64.3 (13.4)
subtype4 41 67.0 (14.4)

Figure S68.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.869 (Fisher's exact test)

Table S77.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 183 195
subtype1 90 89
subtype2 22 28
subtype3 52 56
subtype4 19 22

Figure S69.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.741 (Fisher's exact test)

Table S78.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA
ALL 327 48
subtype1 150 26
subtype2 44 6
subtype3 97 11
subtype4 36 5

Figure S70.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.491 (Chi-square test)

Table S79.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 10 64 264 37
subtype1 6 36 118 17
subtype2 2 5 38 4
subtype3 2 15 77 14
subtype4 0 8 31 2

Figure S71.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.148 (Chi-square test)

Table S80.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 222 89 66
subtype1 107 38 33
subtype2 27 15 8
subtype3 57 32 19
subtype4 31 4 6

Figure S72.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 3.19e-05 (Chi-square test)

Table S81.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 286 49 7 1 28
subtype1 144 29 2 0 0
subtype2 34 6 2 0 8
subtype3 76 13 2 0 15
subtype4 32 1 1 1 5

Figure S73.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.353 (Chi-square test)

Table S82.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 62 144 106 55
subtype1 34 64 44 28
subtype2 8 17 17 8
subtype3 14 40 36 16
subtype4 6 23 9 3

Figure S74.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 1 (Fisher's exact test)

Table S83.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 376
subtype1 1 178
subtype2 0 50
subtype3 1 107
subtype4 0 41

Figure S75.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00854 (Fisher's exact test)

Table S84.  Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 55 323
subtype1 15 164
subtype2 11 39
subtype3 22 86
subtype4 7 34

Figure S76.  Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = COAD.mergedcluster.txt

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

  • Number of patients = 422

  • Number of clustering approaches = 8

  • Number of selected clinical features = 10

  • 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

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)
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