Colon/Rectal 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 10 different clustering approaches and 11 clinical features across 588 patients, 33 significant findings detected with P value < 0.05.

  • CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'PRIMARY.SITE.OF.DISEASE',  'HISTOLOGICAL.TYPE',  'PATHOLOGY.N',  'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

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

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

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE',  'PRIMARY.SITE.OF.DISEASE',  'GENDER', and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'PATHOLOGY.N',  'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'PATHOLOGY.N',  'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.

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

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

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

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE',  'HISTOLOGICAL.TYPE',  'PATHOLOGICSPREAD(M)',  'TUMOR.STAGE', and 'NEOADJUVANT.THERAPY'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 11 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 33 significant findings detected.

Clinical
Features 		Statistical
Tests 		mRNA
CNMF
subtypes 		mRNA
cHierClus
subtypes 		CN
CNMF 		METHLYATION
CNMF 		RPPA
CNMF
subtypes 		RPPA
cHierClus
subtypes 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRseq
CNMF
subtypes 		MIRseq
cHierClus
subtypes
Time to Death logrank test 0.145 0.805 0.372 0.918 0.273 0.0945 0.12 0.176 0.238 0.598
AGE ANOVA 0.596 0.148 0.628 0.00372 0.467 0.505 0.416 0.884 0.00171 0.00388
PRIMARY SITE OF DISEASE Fisher's exact test 0.0126 0.035 7.44e-05 0.00624 0.469 0.258 0.111 0.265 0.565 0.0614
GENDER Fisher's exact test 0.072 0.796 0.804 0.031 0.474 0.121 0.869 0.924 0.471 0.683
HISTOLOGICAL TYPE Chi-square test 6.39e-07 3.6e-08 4.28e-13 0.00456 0.447 0.272 0.0132 0.00215 0.0981 0.0326
PATHOLOGY T Chi-square test 0.184 0.866 0.934 0.569 0.455 0.527 0.439 0.71 0.0706 0.15
PATHOLOGY N Chi-square test 0.00595 0.65 0.00155 0.429 0.0253 0.0289 0.872 0.969 0.0069 0.199
PATHOLOGICSPREAD(M) Chi-square test 0.00173 0.498 0.2 0.686 0.0189 0.00181 0.28 0.852 1.42e-07 3.56e-08
TUMOR STAGE Chi-square test 0.0119 0.633 0.00415 0.32 0.0429 0.0126 0.762 0.998 0.0189 0.0275
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.393 0.496 0.216 0.0662 0.826 0.165 0.41 1 0.128 0.46
NEOADJUVANT THERAPY Fisher's exact test 0.127 0.712 0.645 0.279 0.735 0.216 0.41 1 4.93e-07 8.37e-05
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 48 73 40
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.145 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 114 15 0.9 - 52.0 (5.5)
subtype1 36 9 1.0 - 52.0 (13.0)
subtype2 26 2 1.0 - 30.0 (1.0)
subtype3 28 2 0.9 - 49.9 (1.0)
subtype4 24 2 0.9 - 52.0 (12.5)

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 = 0.596 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 224 69.3 (11.5)
subtype1 63 67.7 (9.7)
subtype2 48 70.3 (11.8)
subtype3 73 69.6 (12.3)
subtype4 40 70.2 (12.2)

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

'mRNA CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.0126 (Fisher's exact test)

Table S4.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 154 68
subtype1 37 26
subtype2 41 7
subtype3 46 25
subtype4 30 10

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.072 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 107 117
subtype1 33 30
subtype2 29 19
subtype3 31 42
subtype4 14 26

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.39e-07 (Chi-square test)

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

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 128 24 58 7
subtype1 36 1 26 0
subtype2 27 13 4 2
subtype3 45 1 21 2
subtype4 20 9 7 3

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.184 (Chi-square test)

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 9 46 150 17
subtype1 1 12 42 8
subtype2 1 9 32 4
subtype3 5 16 52 0
subtype4 2 9 24 5

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.00595 (Chi-square test)

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

nPatients N0 N1 N2
ALL 137 43 44
subtype1 28 18 17
subtype2 30 6 12
subtype3 47 12 14
subtype4 32 7 1

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

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

P value = 0.00173 (Chi-square test)

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A
ALL 186 34 1
subtype1 44 19 0
subtype2 41 6 0
subtype3 64 8 0
subtype4 37 1 1

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0119 (Chi-square test)

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

nPatients I II III IV
ALL 46 86 55 32
subtype1 9 16 19 17
subtype2 8 21 12 5
subtype3 19 28 18 8
subtype4 10 21 6 2

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

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

P value = 0.393 (Fisher's exact test)

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

nPatients NO YES
ALL 1 223
subtype1 0 63
subtype2 1 47
subtype3 0 73
subtype4 0 40

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.127 (Fisher's exact test)

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

nPatients NO YES
ALL 8 216
subtype1 3 60
subtype2 4 44
subtype3 1 72
subtype4 0 40

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S13.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 47 64 113
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.805 (logrank test)

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 114 15 0.9 - 52.0 (5.5)
subtype1 12 1 0.9 - 17.0 (1.0)
subtype2 35 3 1.0 - 41.0 (1.0)
subtype3 67 11 0.9 - 52.0 (12.0)

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.148 (ANOVA)

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 224 69.3 (11.5)
subtype1 47 67.5 (13.0)
subtype2 64 71.6 (11.3)
subtype3 113 68.8 (10.8)

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

'mRNA cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.035 (Fisher's exact test)

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 154 68
subtype1 34 12
subtype2 51 13
subtype3 69 43

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.796 (Fisher's exact test)

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 107 117
subtype1 22 25
subtype2 33 31
subtype3 52 61

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.6e-08 (Chi-square test)

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 128 24 58 7
subtype1 33 1 11 1
subtype2 29 20 9 3
subtype3 66 3 38 3

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.866 (Chi-square test)

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 9 46 150 17
subtype1 1 9 35 2
subtype2 2 13 41 6
subtype3 6 24 74 9

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.65 (Chi-square test)

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 137 43 44
subtype1 28 11 8
subtype2 43 11 10
subtype3 66 21 26

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.498 (Chi-square test)

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A
ALL 186 34 1
subtype1 38 8 0
subtype2 57 6 0
subtype3 91 20 1

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.633 (Chi-square test)

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 46 86 55 32
subtype1 9 19 11 8
subtype2 13 29 15 5
subtype3 24 38 29 19

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.496 (Fisher's exact test)

Table S23.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 223
subtype1 0 47
subtype2 1 63
subtype3 0 113

Figure S21.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.712 (Fisher's exact test)

Table S24.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 8 216
subtype1 2 45
subtype2 3 61
subtype3 3 110

Figure S22.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'CN CNMF'

Table S25.  Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels 1 2 3 4
Number of samples 200 234 100 41
'CN CNMF' versus 'Time to Death'

P value = 0.372 (logrank test)

Table S26.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 444 60 0.1 - 135.5 (7.0)
subtype1 150 22 0.1 - 129.1 (8.0)
subtype2 191 22 0.1 - 135.5 (6.0)
subtype3 73 12 0.2 - 112.7 (6.6)
subtype4 30 4 0.3 - 87.8 (13.3)

Figure S23.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.628 (ANOVA)

Table S27.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 574 66.6 (12.7)
subtype1 200 66.8 (11.2)
subtype2 233 66.8 (14.1)
subtype3 100 66.8 (12.1)
subtype4 41 64.1 (13.4)

Figure S24.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 7.44e-05 (Fisher's exact test)

Table S28.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 412 159
subtype1 124 72
subtype2 192 42
subtype3 66 34
subtype4 30 11

Figure S25.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'CN CNMF' versus 'GENDER'

P value = 0.804 (Fisher's exact test)

Table S29.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 267 308
subtype1 88 112
subtype2 113 121
subtype3 48 52
subtype4 18 23

Figure S26.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'GENDER'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 4.28e-13 (Chi-square test)

Table S30.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 356 54 143 13
subtype1 121 3 72 0
subtype2 144 46 32 9
subtype3 61 5 28 4
subtype4 30 0 11 0

Figure S27.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.934 (Chi-square test)

Table S31.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 20 101 392 59
subtype1 7 31 140 22
subtype2 10 44 154 24
subtype3 2 18 69 11
subtype4 1 8 29 2

Figure S28.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.00155 (Chi-square test)

Table S32.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 328 140 103
subtype1 96 64 38
subtype2 157 45 32
subtype3 51 22 26
subtype4 24 9 7

Figure S29.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.2 (Chi-square test)

Table S33.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 435 71 9 1 49
subtype1 143 35 4 0 16
subtype2 189 16 2 1 21
subtype3 72 15 2 0 10
subtype4 31 5 1 0 2

Figure S30.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.00415 (Chi-square test)

Table S34.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 97 207 166 81
subtype1 28 58 68 38
subtype2 47 100 58 19
subtype3 16 32 32 18
subtype4 6 17 8 6

Figure S31.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.216 (Fisher's exact test)

Table S35.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 566
subtype1 3 197
subtype2 2 232
subtype3 2 98
subtype4 2 39

Figure S32.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.645 (Fisher's exact test)

Table S36.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 82 493
subtype1 25 175
subtype2 35 199
subtype3 14 86
subtype4 8 33

Figure S33.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S37.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 141 111 97
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.918 (logrank test)

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 324 44 0.1 - 135.5 (7.0)
subtype1 131 18 0.1 - 135.5 (7.1)
subtype2 102 16 0.1 - 129.1 (7.5)
subtype3 91 10 0.1 - 102.4 (6.0)

Figure S34.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00372 (ANOVA)

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 348 64.8 (13.0)
subtype1 141 65.0 (12.6)
subtype2 111 67.5 (12.9)
subtype3 96 61.5 (13.1)

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.00624 (Fisher's exact test)

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 254 93
subtype1 94 47
subtype2 93 18
subtype3 67 28

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.031 (Fisher's exact test)

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 156 193
subtype1 61 80
subtype2 60 51
subtype3 35 62

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00456 (Chi-square test)

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 224 30 87 6
subtype1 89 5 45 2
subtype2 80 13 15 3
subtype3 55 12 27 1

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.569 (Chi-square test)

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 11 51 243 42
subtype1 4 21 100 16
subtype2 4 20 76 10
subtype3 3 10 67 16

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.429 (Chi-square test)

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 193 93 58
subtype1 71 43 23
subtype2 69 23 19
subtype3 53 27 16

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.686 (Chi-square test)

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 246 37 8 1 50
subtype1 96 19 4 0 21
subtype2 80 10 1 1 16
subtype3 70 8 3 0 13

Figure S41.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.32 (Chi-square test)

Table S46.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 48 126 106 49
subtype1 16 46 49 23
subtype2 21 44 27 14
subtype3 11 36 30 12

Figure S42.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.0662 (Fisher's exact test)

Table S47.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 341
subtype1 4 137
subtype2 0 111
subtype3 4 93

Figure S43.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.279 (Fisher's exact test)

Table S48.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 74 275
subtype1 28 113
subtype2 20 91
subtype3 26 71

Figure S44.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 96 160 20 123
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.273 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 308 30 0.1 - 121.1 (6.0)
subtype1 70 4 0.1 - 87.8 (4.4)
subtype2 117 13 0.1 - 121.1 (6.0)
subtype3 13 0 0.2 - 41.0 (7.6)
subtype4 108 13 0.1 - 105.3 (7.0)

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 = 0.467 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 398 66.2 (12.8)
subtype1 96 66.6 (12.0)
subtype2 159 66.1 (12.9)
subtype3 20 70.3 (10.0)
subtype4 123 65.5 (13.8)

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

'RPPA CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.469 (Fisher's exact test)

Table S52.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 269 130
subtype1 64 32
subtype2 106 54
subtype3 11 9
subtype4 88 35

Figure S47.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.474 (Fisher's exact test)

Table S53.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 187 212
subtype1 39 57
subtype2 81 79
subtype3 10 10
subtype4 57 66

Figure S48.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.447 (Chi-square test)

Table S54.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 238 30 117 10
subtype1 54 10 26 4
subtype2 99 7 49 4
subtype3 9 2 8 1
subtype4 76 11 34 1

Figure S49.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.455 (Chi-square test)

Table S55.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 10 68 276 41
subtype1 0 14 68 13
subtype2 5 26 110 16
subtype3 0 4 16 0
subtype4 5 24 82 12

Figure S50.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0253 (Chi-square test)

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

nPatients N0 N1 N2
ALL 224 103 68
subtype1 45 29 20
subtype2 82 47 31
subtype3 15 2 2
subtype4 82 25 15

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

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

P value = 0.0189 (Chi-square test)

Table S57.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 300 49 8 1 36
subtype1 72 12 5 0 5
subtype2 111 29 0 1 17
subtype3 18 1 0 0 1
subtype4 99 7 3 0 13

Figure S52.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0429 (Chi-square test)

Table S58.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 61 147 119 57
subtype1 11 31 32 16
subtype2 23 50 51 30
subtype3 4 11 3 1
subtype4 23 55 33 10

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

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

P value = 0.826 (Fisher's exact test)

Table S59.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 391
subtype1 3 93
subtype2 3 157
subtype3 0 20
subtype4 2 121

Figure S54.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.735 (Fisher's exact test)

Table S60.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 68 331
subtype1 20 76
subtype2 25 135
subtype3 3 17
subtype4 20 103

Figure S55.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S61.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 210 34 72 83
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0945 (logrank test)

Table S62.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 308 30 0.1 - 121.1 (6.0)
subtype1 148 15 0.1 - 121.1 (5.9)
subtype2 22 3 0.1 - 105.3 (8.1)
subtype3 65 2 0.1 - 87.8 (5.4)
subtype4 73 10 0.1 - 100.0 (6.1)

Figure S56.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.505 (ANOVA)

Table S63.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 398 66.2 (12.8)
subtype1 209 66.8 (13.2)
subtype2 34 67.9 (11.8)
subtype3 72 65.1 (12.1)
subtype4 83 65.1 (12.8)

Figure S57.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.258 (Fisher's exact test)

Table S64.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 269 130
subtype1 138 72
subtype2 28 6
subtype3 49 23
subtype4 54 29

Figure S58.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.121 (Fisher's exact test)

Table S65.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 187 212
subtype1 105 105
subtype2 19 15
subtype3 33 39
subtype4 30 53

Figure S59.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.272 (Chi-square test)

Table S66.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 238 30 117 10
subtype1 125 12 63 6
subtype2 25 3 5 1
subtype3 40 9 20 3
subtype4 48 6 29 0

Figure S60.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.527 (Chi-square test)

Table S67.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 10 68 276 41
subtype1 5 35 145 22
subtype2 1 3 28 2
subtype3 1 11 48 11
subtype4 3 19 55 6

Figure S61.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0289 (Chi-square test)

Table S68.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 224 103 68
subtype1 107 62 40
subtype2 22 7 5
subtype3 35 20 15
subtype4 60 14 8

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

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

P value = 0.00181 (Chi-square test)

Table S69.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 300 49 8 1 36
subtype1 153 37 0 1 19
subtype2 29 0 0 0 3
subtype3 53 8 5 0 4
subtype4 65 4 3 0 10

Figure S63.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0126 (Chi-square test)

Table S70.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 61 147 119 57
subtype1 31 69 65 37
subtype2 3 18 12 0
subtype3 9 22 24 13
subtype4 18 38 18 7

Figure S64.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.165 (Fisher's exact test)

Table S71.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 391
subtype1 3 207
subtype2 0 34
subtype3 4 68
subtype4 1 82

Figure S65.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.216 (Fisher's exact test)

Table S72.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 68 331
subtype1 31 179
subtype2 8 26
subtype3 17 55
subtype4 12 71

Figure S66.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S73.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 17 17 27 22
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.12 (logrank test)

Table S74.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 46 4 0.9 - 72.1 (8.2)
subtype1 6 1 1.0 - 64.0 (11.6)
subtype2 8 0 1.0 - 17.0 (1.0)
subtype3 16 0 0.9 - 61.9 (8.5)
subtype4 16 3 0.9 - 72.1 (7.0)

Figure S67.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.416 (ANOVA)

Table S75.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 83 67.6 (10.5)
subtype1 17 64.8 (9.6)
subtype2 17 66.1 (9.8)
subtype3 27 68.2 (12.3)
subtype4 22 70.1 (9.3)

Figure S68.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.111 (Fisher's exact test)

Table S76.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 11 71
subtype1 3 14
subtype2 3 13
subtype3 5 22
subtype4 0 22

Figure S69.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.869 (Fisher's exact test)

Table S77.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 39 44
subtype1 9 8
subtype2 8 9
subtype3 11 16
subtype4 11 11

Figure S70.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0132 (Chi-square test)

Table S78.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 11 60 8
subtype1 3 11 2
subtype2 3 13 0
subtype3 5 14 6
subtype4 0 22 0

Figure S71.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.439 (Chi-square test)

Table S79.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 5 19 54 5
subtype1 0 2 14 1
subtype2 1 4 12 0
subtype3 3 6 17 1
subtype4 1 7 11 3

Figure S72.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.872 (Chi-square test)

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

nPatients N0 N1 N2
ALL 52 17 14
subtype1 9 4 4
subtype2 12 3 2
subtype3 18 6 3
subtype4 13 4 5

Figure S73.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.28 (Chi-square test)

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

nPatients M0 M1 M1A
ALL 68 13 1
subtype1 13 4 0
subtype2 16 1 0
subtype3 23 2 1
subtype4 16 6 0

Figure S74.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.762 (Chi-square test)

Table S82.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 21 29 18 13
subtype1 2 7 4 3
subtype2 5 7 4 1
subtype3 8 9 6 3
subtype4 6 6 4 6

Figure S75.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.41 (Fisher's exact test)

Table S83.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 82
subtype1 1 16
subtype2 0 17
subtype3 0 27
subtype4 0 22

Figure S76.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.41 (Fisher's exact test)

Table S84.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 1 82
subtype1 1 16
subtype2 0 17
subtype3 0 27
subtype4 0 22

Figure S77.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S85.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 17 16 28 22
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.176 (logrank test)

Table S86.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 46 4 0.9 - 72.1 (8.2)
subtype1 9 1 0.9 - 38.9 (2.0)
subtype2 11 1 1.0 - 52.0 (12.0)
subtype3 14 0 0.9 - 61.9 (8.5)
subtype4 12 2 1.0 - 72.1 (1.5)

Figure S78.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.884 (ANOVA)

Table S87.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 83 67.6 (10.5)
subtype1 17 69.2 (9.1)
subtype2 16 66.5 (9.4)
subtype3 28 67.7 (13.0)
subtype4 22 67.0 (9.2)

Figure S79.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.265 (Fisher's exact test)

Table S88.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 11 71
subtype1 1 15
subtype2 1 15
subtype3 7 21
subtype4 2 20

Figure S80.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.924 (Fisher's exact test)

Table S89.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 39 44
subtype1 8 9
subtype2 8 8
subtype3 14 14
subtype4 9 13

Figure S81.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00215 (Chi-square test)

Table S90.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 11 60 8
subtype1 1 13 1
subtype2 1 15 0
subtype3 7 12 7
subtype4 2 20 0

Figure S82.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.71 (Chi-square test)

Table S91.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 5 19 54 5
subtype1 1 4 12 0
subtype2 2 4 9 1
subtype3 2 6 19 1
subtype4 0 5 14 3

Figure S83.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.969 (Chi-square test)

Table S92.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 52 17 14
subtype1 10 4 3
subtype2 10 4 2
subtype3 19 5 4
subtype4 13 4 5

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

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

P value = 0.852 (Chi-square test)

Table S93.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A
ALL 68 13 1
subtype1 14 3 0
subtype2 13 3 0
subtype3 23 3 1
subtype4 18 4 0

Figure S85.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.998 (Chi-square test)

Table S94.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 21 29 18 13
subtype1 4 6 4 3
subtype2 5 4 4 3
subtype3 7 11 5 4
subtype4 5 8 5 3

Figure S86.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 1 (Fisher's exact test)

Table S95.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 82
subtype1 0 17
subtype2 0 16
subtype3 1 27
subtype4 0 22

Figure S87.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S96.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 1 82
subtype1 0 17
subtype2 0 16
subtype3 1 27
subtype4 0 22

Figure S88.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S97.  Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 205 73 243
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.238 (logrank test)

Table S98.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 386 59 0.1 - 135.5 (8.0)
subtype1 191 31 0.1 - 135.5 (8.1)
subtype2 65 10 0.1 - 100.0 (7.0)
subtype3 130 18 0.2 - 72.1 (6.5)

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.00171 (ANOVA)

Table S99.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 521 66.8 (12.7)
subtype1 205 65.8 (12.7)
subtype2 73 63.2 (13.6)
subtype3 243 68.7 (12.1)

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.565 (Fisher's exact test)

Table S100.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 377 140
subtype1 145 59
subtype2 56 16
subtype3 176 65

Figure S91.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.471 (Fisher's exact test)

Table S101.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 249 272
subtype1 99 106
subtype2 30 43
subtype3 120 123

Figure S92.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0981 (Chi-square test)

Table S102.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 327 48 127 10
subtype1 132 13 58 1
subtype2 45 11 14 2
subtype3 150 24 55 7

Figure S93.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0706 (Chi-square test)

Table S103.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 19 90 361 47
subtype1 8 35 145 16
subtype2 1 5 55 11
subtype3 10 50 161 20

Figure S94.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0069 (Chi-square test)

Table S104.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 296 127 94
subtype1 117 58 29
subtype2 29 22 19
subtype3 150 47 46

Figure S95.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 1.42e-07 (Chi-square test)

Table S105.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 393 67 9 1 42
subtype1 146 17 6 1 31
subtype2 49 11 2 0 10
subtype3 198 39 1 0 1

Figure S96.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0189 (Chi-square test)

Table S106.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 90 188 149 75
subtype1 37 74 65 26
subtype2 3 23 27 12
subtype3 50 91 57 37

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.128 (Fisher's exact test)

Table S107.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 513
subtype1 3 202
subtype2 3 70
subtype3 2 241

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 4.93e-07 (Fisher's exact test)

Table S108.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 71 450
subtype1 43 162
subtype2 15 58
subtype3 13 230

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S109.  Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 45 220 256
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.598 (logrank test)

Table S110.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 386 59 0.1 - 135.5 (8.0)
subtype1 32 4 0.1 - 129.1 (4.5)
subtype2 120 17 0.9 - 72.1 (10.5)
subtype3 234 38 0.1 - 135.5 (8.0)

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.00388 (ANOVA)

Table S111.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 521 66.8 (12.7)
subtype1 45 64.1 (13.2)
subtype2 220 68.9 (12.2)
subtype3 256 65.5 (12.8)

Figure S101.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.0614 (Fisher's exact test)

Table S112.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 377 140
subtype1 26 19
subtype2 164 54
subtype3 187 67

Figure S102.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.683 (Fisher's exact test)

Table S113.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 249 272
subtype1 24 21
subtype2 106 114
subtype3 119 137

Figure S103.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0326 (Chi-square test)

Table S114.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 327 48 127 10
subtype1 24 2 16 3
subtype2 137 25 46 5
subtype3 166 21 65 2

Figure S104.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.15 (Chi-square test)

Table S115.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients T0+T1 T2 T3 T4
ALL 19 90 361 47
subtype1 1 5 38 1
subtype2 9 47 143 19
subtype3 9 38 180 27

Figure S105.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.199 (Chi-square test)

Table S116.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 296 127 94
subtype1 18 14 12
subtype2 132 49 38
subtype3 146 64 44

Figure S106.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 3.56e-08 (Chi-square test)

Table S117.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A M1B MX
ALL 393 67 9 1 42
subtype1 29 12 1 0 3
subtype2 181 33 2 0 0
subtype3 183 22 6 1 39

Figure S107.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0275 (Chi-square test)

Table S118.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients I II III IV
ALL 90 188 149 75
subtype1 5 11 15 13
subtype2 46 77 56 30
subtype3 39 100 78 32

Figure S108.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGE'

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

P value = 0.46 (Fisher's exact test)

Table S119.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 513
subtype1 1 44
subtype2 2 218
subtype3 5 251

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 8.37e-05 (Fisher's exact test)

Table S120.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 71 450
subtype1 8 37
subtype2 14 206
subtype3 49 207

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #11: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = COADREAD.mergedcluster.txt

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

  • Number of patients = 588

  • Number of clustering approaches = 10

  • Number of selected clinical features = 11

  • 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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