Rectum 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 10 clinical features across 166 patients, 18 significant findings detected with P value < 0.05.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

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

  • 5 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death' and 'HISTOLOGICAL.TYPE'.

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

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'GENDER' and 'PATHOLOGICSPREAD(M)'.

  • Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that correlate to 'Time to Death',  'AGE', and 'PATHOLOGICSPREAD(M)'.

  • 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 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on sequencing-based miR expression data identified 5 subtypes that correlate to 'Time to Death',  'PATHOLOGY.N',  'PATHOLOGICSPREAD(M)', and 'NEOADJUVANT.THERAPY'.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE',  'PATHOLOGICSPREAD(M)', 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 10 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 18 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER HISTOLOGICAL
TYPE 		PATHOLOGY
T 		PATHOLOGY
N 		PATHOLOGICSPREAD(M) TUMOR
STAGE 		RADIATIONS
RADIATION
REGIMENINDICATION 		NEOADJUVANT
THERAPY
Statistical Tests logrank test ANOVA Fisher's exact test Fisher's exact test Chi-square test Chi-square test Chi-square test Chi-square test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.108 0.499 0.953 0.365 0.0987 0.66 0.407 0.639 0.29 0.29
mRNA cHierClus subtypes 0.00176 0.394 0.865 0.692 0.245 0.574 0.212 0.504 1 1
CN CNMF 0.000187 0.373 0.568 0.0316 0.564 0.104 0.0929 0.477 0.238 0.942
METHLYATION CNMF 0.296 0.31 0.611 0.24 0.595 0.51 0.185 0.0187 0.323 0.47
RPPA CNMF subtypes 0.652 0.513 0.0423 0.255 0.2 0.114 0.0137 0.157 1 0.896
RPPA cHierClus subtypes 6.78e-05 0.0282 0.378 0.157 0.402 0.0918 0.0124 0.218 0.785 0.0675
RNAseq CNMF subtypes 0.206 0.478 0.586 0.00271 0.143 0.702 0.696 0.562 0.472 0.472
RNAseq cHierClus subtypes 0.165 0.283 0.87 2.93e-05 0.709 0.761 0.589 0.842 0.569 0.569
MIRseq CNMF subtypes 0.0293 0.892 0.461 0.313 0.251 0.0172 0.0461 0.117 0.283 0.00351
MIRseq cHierClus subtypes 0.474 0.582 0.973 0.037 0.726 0.244 0.000442 0.361 0.298 0.00116
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 25 20 24
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.108 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 39 4 0.9 - 52.0 (6.0)
subtype1 20 1 0.9 - 49.9 (13.3)
subtype2 7 0 1.0 - 12.7 (1.0)
subtype3 12 3 1.0 - 52.0 (1.5)

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 = 0.499 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 69 66.6 (10.7)
subtype1 25 64.6 (12.3)
subtype2 20 67.9 (9.9)
subtype3 24 67.7 (9.5)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.953 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 31 38
subtype1 12 13
subtype2 9 11
subtype3 10 14

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 = 0.365 (Fisher's exact test)

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

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 58 7
subtype1 19 3
subtype2 16 3
subtype3 23 1

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 = 0.0987 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 5 15 45 4
subtype1 3 6 16 0
subtype2 2 5 13 0
subtype3 0 4 16 4

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 = 0.66 (Chi-square test)

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

nPatients N0 N1 N2
ALL 42 15 12
subtype1 16 5 4
subtype2 14 4 2
subtype3 12 6 6

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 = 0.407 (Fisher's exact test)

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

nPatients M0 M1
ALL 57 12
subtype1 21 4
subtype2 18 2
subtype3 18 6

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 = 0.639 (Chi-square test)

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

nPatients I II III IV
ALL 18 23 16 10
subtype1 8 8 5 3
subtype2 7 7 4 2
subtype3 3 8 7 5

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

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

P value = 0.29 (Fisher's exact test)

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

nPatients NO YES
ALL 1 68
subtype1 0 25
subtype2 1 19
subtype3 0 24

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.29 (Fisher's exact test)

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

nPatients NO YES
ALL 1 68
subtype1 0 25
subtype2 1 19
subtype3 0 24

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 19 24 26
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.00176 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 39 4 0.9 - 52.0 (6.0)
subtype1 10 2 1.0 - 38.0 (1.0)
subtype2 20 2 0.9 - 52.0 (13.7)
subtype3 9 0 1.0 - 17.0 (1.0)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.394 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 69 66.6 (10.7)
subtype1 19 68.4 (8.7)
subtype2 24 64.2 (12.8)
subtype3 26 67.5 (9.7)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.865 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 31 38
subtype1 8 11
subtype2 12 12
subtype3 11 15

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.692 (Fisher's exact test)

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 58 7
subtype1 18 1
subtype2 19 3
subtype3 21 3

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.245 (Chi-square test)

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 5 15 45 4
subtype1 0 3 13 3
subtype2 3 6 14 1
subtype3 2 6 18 0

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.574 (Chi-square test)

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

nPatients N0 N1 N2
ALL 42 15 12
subtype1 9 6 4
subtype2 15 4 5
subtype3 18 5 3

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

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

P value = 0.212 (Fisher's exact test)

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 57 12
subtype1 14 5
subtype2 19 5
subtype3 24 2

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

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.504 (Chi-square test)

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

nPatients I II III IV
ALL 18 23 16 10
subtype1 2 6 6 4
subtype2 8 7 4 4
subtype3 8 10 6 2

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

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

P value = 1 (Fisher's exact test)

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 68
subtype1 0 19
subtype2 0 24
subtype3 1 25

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 1 68
subtype1 0 19
subtype2 0 24
subtype3 1 25

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'CN CNMF'

Table S23.  Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels 1 2 3 4 5
Number of samples 7 32 46 55 22
'CN CNMF' versus 'Time to Death'

P value = 0.000187 (logrank test)

Table S24.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 125 11 0.2 - 121.1 (6.3)
subtype1 7 0 0.4 - 121.1 (9.4)
subtype2 24 4 1.0 - 51.5 (5.0)
subtype3 31 3 0.5 - 119.5 (6.3)
subtype4 46 1 0.2 - 70.0 (4.7)
subtype5 17 3 0.8 - 38.9 (13.0)

Figure S21.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.373 (ANOVA)

Table S25.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 162 64.7 (11.8)
subtype1 7 61.9 (9.9)
subtype2 32 64.4 (11.8)
subtype3 46 66.4 (12.5)
subtype4 55 62.7 (11.9)
subtype5 22 67.5 (10.3)

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

'CN CNMF' versus 'GENDER'

P value = 0.568 (Chi-square test)

Table S26.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 74 88
subtype1 1 6
subtype2 15 17
subtype3 22 24
subtype4 26 29
subtype5 10 12

Figure S23.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0316 (Chi-square test)

Table S27.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 143 13
subtype1 7 0
subtype2 32 0
subtype3 40 4
subtype4 44 9
subtype5 20 0

Figure S24.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.564 (Chi-square test)

Table S28.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 9 29 109 14
subtype1 1 0 5 1
subtype2 1 4 23 3
subtype3 1 8 31 6
subtype4 5 14 34 2
subtype5 1 3 16 2

Figure S25.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.104 (Chi-square test)

Table S29.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 83 45 32
subtype1 5 0 2
subtype2 14 12 5
subtype3 23 11 11
subtype4 34 15 6
subtype5 7 7 8

Figure S26.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0929 (Chi-square test)

Table S30.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A MX
ALL 123 22 2 13
subtype1 7 0 0 0
subtype2 22 9 0 0
subtype3 34 7 0 5
subtype4 45 4 1 4
subtype5 15 2 1 4

Figure S27.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.477 (Chi-square test)

Table S31.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 30 50 50 25
subtype1 1 4 2 0
subtype2 4 9 9 7
subtype3 6 17 13 7
subtype4 16 15 16 7
subtype5 3 5 10 4

Figure S28.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.238 (Chi-square test)

Table S32.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 6 156
subtype1 1 6
subtype2 0 32
subtype3 1 45
subtype4 2 53
subtype5 2 20

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

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.942 (Chi-square test)

Table S33.  Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 24 138
subtype1 1 6
subtype2 4 28
subtype3 6 40
subtype4 10 45
subtype5 3 19

Figure S30.  Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S34.  Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 27 39 29
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.296 (logrank test)

Table S35.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 88 8 0.2 - 121.1 (6.2)
subtype1 27 2 0.2 - 121.1 (6.9)
subtype2 36 3 0.2 - 60.0 (7.5)
subtype3 25 3 0.3 - 72.1 (2.4)

Figure S31.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.31 (ANOVA)

Table S36.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 95 63.3 (12.3)
subtype1 27 60.3 (9.4)
subtype2 39 64.1 (12.7)
subtype3 29 65.1 (13.8)

Figure S32.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.611 (Fisher's exact test)

Table S37.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 42 53
subtype1 11 16
subtype2 16 23
subtype3 15 14

Figure S33.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.24 (Fisher's exact test)

Table S38.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 87 6
subtype1 26 1
subtype2 36 1
subtype3 25 4

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.595 (Chi-square test)

Table S39.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 4 13 66 11
subtype1 2 4 18 3
subtype2 1 7 24 6
subtype3 1 2 24 2

Figure S35.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.51 (Chi-square test)

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 41 30 21
subtype1 12 8 6
subtype2 14 16 8
subtype3 15 6 7

Figure S36.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.185 (Chi-square test)

Table S41.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A MX
ALL 67 10 2 14
subtype1 17 6 1 3
subtype2 27 1 1 8
subtype3 23 3 0 3

Figure S37.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.0187 (Chi-square test)

Table S42.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 11 28 35 15
subtype1 5 7 7 7
subtype2 5 7 20 3
subtype3 1 14 8 5

Figure S38.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.323 (Fisher's exact test)

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 5 90
subtype1 2 25
subtype2 3 36
subtype3 0 29

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.47 (Fisher's exact test)

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 23 72
subtype1 6 21
subtype2 12 27
subtype3 5 24

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S45.  Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 54 34 42
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.652 (logrank test)

Table S46.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 102 11 0.2 - 121.1 (6.2)
subtype1 40 5 0.2 - 121.1 (5.6)
subtype2 24 2 0.3 - 72.1 (3.6)
subtype3 38 4 0.2 - 70.0 (8.1)

Figure S41.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.513 (ANOVA)

Table S47.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 130 65.6 (11.7)
subtype1 54 64.8 (12.3)
subtype2 34 67.6 (11.5)
subtype3 42 64.9 (11.2)

Figure S42.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0423 (Fisher's exact test)

Table S48.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 60 70
subtype1 32 22
subtype2 12 22
subtype3 16 26

Figure S43.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.255 (Fisher's exact test)

Table S49.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 117 10
subtype1 48 5
subtype2 28 4
subtype3 41 1

Figure S44.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.2 (Chi-square test)

Table S50.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 5 23 91 10
subtype1 4 10 36 4
subtype2 1 4 23 5
subtype3 0 9 32 1

Figure S45.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.114 (Chi-square test)

Table S51.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 64 37 26
subtype1 24 19 10
subtype2 13 10 10
subtype3 27 8 6

Figure S46.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0137 (Chi-square test)

Table S52.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A MX
ALL 98 18 2 10
subtype1 39 10 0 5
subtype2 24 7 2 0
subtype3 35 1 0 5

Figure S47.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.157 (Chi-square test)

Table S53.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 21 40 42 19
subtype1 9 14 18 10
subtype2 4 9 10 8
subtype3 8 17 14 1

Figure S48.  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 = 1 (Fisher's exact test)

Table S54.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 5 125
subtype1 2 52
subtype2 1 33
subtype3 2 40

Figure S49.  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 = 0.896 (Fisher's exact test)

Table S55.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 17 113
subtype1 8 46
subtype2 4 30
subtype3 5 37

Figure S50.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S56.  Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 6 26 20 42 36
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 6.78e-05 (logrank test)

Table S57.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 102 11 0.2 - 121.1 (6.2)
subtype1 4 1 0.5 - 24.1 (5.2)
subtype2 11 1 0.9 - 15.8 (1.0)
subtype3 18 1 0.3 - 72.1 (5.5)
subtype4 38 6 0.2 - 119.5 (6.6)
subtype5 31 2 0.2 - 121.1 (8.0)

Figure S51.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0282 (ANOVA)

Table S58.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 130 65.6 (11.7)
subtype1 6 65.2 (11.9)
subtype2 26 72.0 (12.0)
subtype3 20 62.6 (11.0)
subtype4 42 63.4 (10.4)
subtype5 36 65.2 (12.2)

Figure S52.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.378 (Chi-square test)

Table S59.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 60 70
subtype1 4 2
subtype2 15 11
subtype3 8 12
subtype4 20 22
subtype5 13 23

Figure S53.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.157 (Chi-square test)

Table S60.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 117 10
subtype1 5 1
subtype2 23 1
subtype3 17 3
subtype4 36 5
subtype5 36 0

Figure S54.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.402 (Chi-square test)

Table S61.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 5 23 91 10
subtype1 1 0 5 0
subtype2 1 3 20 2
subtype3 0 3 13 3
subtype4 2 8 27 5
subtype5 1 9 26 0

Figure S55.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0918 (Chi-square test)

Table S62.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 64 37 26
subtype1 2 1 3
subtype2 10 8 8
subtype3 6 8 5
subtype4 22 13 6
subtype5 24 7 4

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

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

P value = 0.0124 (Chi-square test)

Table S63.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A MX
ALL 98 18 2 10
subtype1 6 0 0 0
subtype2 18 8 0 0
subtype3 15 1 2 1
subtype4 30 7 0 5
subtype5 29 2 0 4

Figure S57.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.218 (Chi-square test)

Table S64.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 21 40 42 19
subtype1 1 1 4 0
subtype2 4 6 8 6
subtype3 2 3 10 3
subtype4 6 15 11 8
subtype5 8 15 9 2

Figure S58.  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 = 0.785 (Chi-square test)

Table S65.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 5 125
subtype1 0 6
subtype2 0 26
subtype3 1 19
subtype4 2 40
subtype5 2 34

Figure S59.  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 = 0.0675 (Chi-square test)

Table S66.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 17 113
subtype1 2 4
subtype2 0 26
subtype3 5 15
subtype4 6 36
subtype5 4 32

Figure S60.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S67.  Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 18 17 17 20
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.206 (logrank test)

Table S68.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 43 4 0.9 - 72.1 (10.6)
subtype1 11 1 1.0 - 52.0 (12.0)
subtype2 9 1 1.0 - 64.0 (1.0)
subtype3 11 2 0.9 - 72.1 (12.0)
subtype4 12 0 0.9 - 61.9 (8.5)

Figure S61.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.478 (ANOVA)

Table S69.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 72 66.8 (10.2)
subtype1 18 66.4 (9.5)
subtype2 17 65.5 (10.6)
subtype3 17 70.1 (9.2)
subtype4 20 65.2 (11.4)

Figure S62.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.586 (Fisher's exact test)

Table S70.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 33 39
subtype1 10 8
subtype2 6 11
subtype3 9 8
subtype4 8 12

Figure S63.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00271 (Fisher's exact test)

Table S71.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 60 8
subtype1 17 0
subtype2 14 2
subtype3 17 0
subtype4 12 6

Figure S64.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.143 (Chi-square test)

Table S72.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 5 15 48 4
subtype1 2 3 12 1
subtype2 0 1 14 2
subtype3 0 7 9 1
subtype4 3 4 13 0

Figure S65.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.702 (Chi-square test)

Table S73.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 44 16 12
subtype1 9 6 3
subtype2 9 5 3
subtype3 12 2 3
subtype4 14 3 3

Figure S66.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.696 (Fisher's exact test)

Table S74.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 61 11
subtype1 16 2
subtype2 13 4
subtype3 14 3
subtype4 18 2

Figure S67.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.562 (Chi-square test)

Table S75.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 18 25 18 10
subtype1 5 4 7 2
subtype2 1 8 4 3
subtype3 5 6 3 3
subtype4 7 7 4 2

Figure S68.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.472 (Fisher's exact test)

Table S76.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 71
subtype1 0 18
subtype2 1 16
subtype3 0 17
subtype4 0 20

Figure S69.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.472 (Fisher's exact test)

Table S77.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 1 71
subtype1 0 18
subtype2 1 16
subtype3 0 17
subtype4 0 20

Figure S70.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S78.  Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 17 24 31
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.165 (logrank test)

Table S79.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 43 4 0.9 - 72.1 (10.6)
subtype1 11 1 1.0 - 52.0 (10.6)
subtype2 13 0 0.9 - 61.9 (12.7)
subtype3 19 3 0.9 - 72.1 (1.9)

Figure S71.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.283 (ANOVA)

Table S80.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 72 66.8 (10.2)
subtype1 17 65.1 (8.0)
subtype2 24 65.0 (12.5)
subtype3 31 69.0 (9.3)

Figure S72.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.87 (Fisher's exact test)

Table S81.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 33 39
subtype1 7 10
subtype2 12 12
subtype3 14 17

Figure S73.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.93e-05 (Fisher's exact test)

Table S82.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 60 8
subtype1 16 0
subtype2 13 8
subtype3 31 0

Figure S74.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.709 (Chi-square test)

Table S83.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 5 15 48 4
subtype1 2 3 11 1
subtype2 2 5 17 0
subtype3 1 7 20 3

Figure S75.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.761 (Chi-square test)

Table S84.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 44 16 12
subtype1 9 4 4
subtype2 17 4 3
subtype3 18 8 5

Figure S76.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.589 (Fisher's exact test)

Table S85.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1
ALL 61 11
subtype1 14 3
subtype2 22 2
subtype3 25 6

Figure S77.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.842 (Chi-square test)

Table S86.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 18 25 18 10
subtype1 5 4 5 3
subtype2 7 10 5 2
subtype3 6 11 8 5

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

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

P value = 0.569 (Fisher's exact test)

Table S87.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 71
subtype1 0 17
subtype2 1 23
subtype3 0 31

Figure S79.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.569 (Fisher's exact test)

Table S88.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 1 71
subtype1 0 17
subtype2 1 23
subtype3 0 31

Figure S80.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S89.  Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 18 42 17 10 56
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0293 (logrank test)

Table S90.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 106 10 0.2 - 121.1 (7.0)
subtype1 16 3 0.3 - 121.1 (8.7)
subtype2 38 5 0.5 - 60.0 (7.3)
subtype3 15 0 0.5 - 119.5 (6.0)
subtype4 10 0 0.2 - 72.1 (1.5)
subtype5 27 2 0.9 - 70.0 (1.9)

Figure S81.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.892 (ANOVA)

Table S91.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 143 65.4 (11.5)
subtype1 18 65.2 (9.4)
subtype2 42 64.1 (12.6)
subtype3 17 65.3 (14.5)
subtype4 10 64.8 (7.4)
subtype5 56 66.6 (11.1)

Figure S82.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.461 (Chi-square test)

Table S92.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 66 77
subtype1 8 10
subtype2 18 24
subtype3 11 6
subtype4 3 7
subtype5 26 30

Figure S83.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.313 (Chi-square test)

Table S93.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 127 10
subtype1 15 2
subtype2 40 1
subtype3 17 0
subtype4 9 1
subtype5 46 6

Figure S84.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.251 (Chi-square test)

Table S94.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 9 26 97 10
subtype1 0 0 14 3
subtype2 2 9 29 2
subtype3 3 3 9 2
subtype4 0 2 8 0
subtype5 4 12 37 3

Figure S85.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0172 (Chi-square test)

Table S95.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 74 38 28
subtype1 5 4 7
subtype2 16 19 7
subtype3 11 4 2
subtype4 6 1 2
subtype5 36 10 10

Figure S86.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0461 (Chi-square test)

Table S96.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A MX
ALL 107 18 2 14
subtype1 11 2 1 3
subtype2 28 4 0 9
subtype3 13 2 1 1
subtype4 7 2 0 1
subtype5 48 8 0 0

Figure S87.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.117 (Chi-square test)

Table S97.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 28 44 43 20
subtype1 0 5 6 3
subtype2 6 9 20 5
subtype3 6 5 3 3
subtype4 1 4 2 2
subtype5 15 21 12 7

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.283 (Chi-square test)

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

nPatients NO YES
ALL 6 137
subtype1 2 16
subtype2 3 39
subtype3 0 17
subtype4 0 10
subtype5 1 55

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

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00351 (Chi-square test)

Table S99.  Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 16 127
subtype1 4 14
subtype2 10 32
subtype3 1 16
subtype4 0 10
subtype5 1 55

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S100.  Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 20 64 59
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.474 (logrank test)

Table S101.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 106 10 0.2 - 121.1 (7.0)
subtype1 18 1 0.2 - 64.0 (3.4)
subtype2 35 3 0.9 - 72.1 (10.4)
subtype3 53 6 0.2 - 121.1 (7.4)

Figure S91.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.582 (ANOVA)

Table S102.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 143 65.4 (11.5)
subtype1 20 62.9 (13.5)
subtype2 64 65.8 (10.4)
subtype3 59 65.8 (12.0)

Figure S92.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.973 (Fisher's exact test)

Table S103.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 66 77
subtype1 9 11
subtype2 29 35
subtype3 28 31

Figure S93.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.037 (Fisher's exact test)

Table S104.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 127 10
subtype1 19 1
subtype2 52 8
subtype3 56 1

Figure S94.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.726 (Chi-square test)

Table S105.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 9 26 97 10
subtype1 1 2 16 1
subtype2 5 14 42 3
subtype3 3 10 39 6

Figure S95.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.244 (Chi-square test)

Table S106.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2
ALL 74 38 28
subtype1 7 9 3
subtype2 38 14 12
subtype3 29 15 13

Figure S96.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.000442 (Chi-square test)

Table S107.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 M1A MX
ALL 107 18 2 14
subtype1 13 5 0 2
subtype2 53 11 0 0
subtype3 41 2 2 12

Figure S97.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.361 (Chi-square test)

Table S108.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients I II III IV
ALL 28 44 43 20
subtype1 2 5 7 5
subtype2 17 20 16 9
subtype3 9 19 20 6

Figure S98.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.298 (Fisher's exact test)

Table S109.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 6 137
subtype1 1 19
subtype2 1 63
subtype3 4 55

Figure S99.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00116 (Fisher's exact test)

Table S110.  Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 16 127
subtype1 3 17
subtype2 1 63
subtype3 12 47

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = READ.mergedcluster.txt

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

  • Number of patients = 166

  • Number of clustering approaches = 10

  • 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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