Correlation between aggregated molecular cancer subtypes and selected clinical features
Rectum Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1FT8KGQ
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1FT8KGQ
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 11 clinical features across 169 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 9 subtypes that do not correlate to any clinical features.

  • 5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.

  • 5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.

  • CNMF clustering analysis on RPPA data identified 5 subtypes that do not correlate to any clinical features.

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

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'HISTOLOGICAL_TYPE'.

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

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.

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

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		PATHOLOGIC
STAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER RADIATION
THERAPY 		HISTOLOGICAL
TYPE 		RESIDUAL
TUMOR 		NUMBER
OF
LYMPH
NODES
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Kruskal-Wallis (anova)
mRNA CNMF subtypes 0.45
(0.795) 		0.763
(0.901) 		0.268
(0.633) 		0.637
(0.881) 		0.534
(0.839) 		0.0987
(0.467) 		0.712
(0.895) 		0.672
(0.881) 		0.388
(0.721) 		0.153
(0.533) 		0.233
(0.633)
mRNA cHierClus subtypes 0.589
(0.877) 		0.368
(0.716) 		0.25
(0.633) 		0.354
(0.709) 		0.29
(0.649) 		0.458
(0.795) 		0.354
(0.709) 		0.692
(0.881) 		0.0289
(0.424) 		0.374
(0.716) 		0.272
(0.633)
Copy Number Ratio CNMF subtypes 0.374
(0.716) 		0.227
(0.633) 		0.268
(0.633) 		0.546
(0.847) 		0.018
(0.399) 		0.629
(0.881) 		0.271
(0.633) 		0.827
(0.933) 		0.0738
(0.467) 		0.92
(0.944) 		0.0466
(0.467)
METHLYATION CNMF 0.922
(0.944) 		0.0845
(0.467) 		0.307
(0.654) 		0.91
(0.944) 		0.518
(0.839) 		0.273
(0.633) 		0.808
(0.928) 		0.988
(0.995) 		0.107
(0.472) 		0.0957
(0.467) 		0.723
(0.895)
RPPA CNMF subtypes 0.591
(0.877) 		0.0893
(0.467) 		0.0335
(0.442) 		0.797
(0.923) 		0.462
(0.795) 		0.169
(0.573) 		0.26
(0.633) 		0.209
(0.633) 		0.694
(0.881) 		0.0409
(0.45) 		0.15
(0.533)
RPPA cHierClus subtypes 0.744
(0.901) 		0.0698
(0.467) 		0.307
(0.654) 		0.9
(0.944) 		0.82
(0.933) 		0.528
(0.839) 		0.598
(0.877) 		0.759
(0.901) 		0.92
(0.944) 		0.265
(0.633) 		0.383
(0.721)
RNAseq CNMF subtypes 0.604
(0.877) 		0.124
(0.497) 		0.0513
(0.467) 		0.353
(0.709) 		0.246
(0.633) 		0.68
(0.881) 		0.49
(0.83) 		0.725
(0.895) 		0.103
(0.467) 		0.0403
(0.45) 		0.0801
(0.467)
RNAseq cHierClus subtypes 0.183
(0.592) 		0.464
(0.795) 		0.0212
(0.399) 		0.599
(0.877) 		0.1
(0.467) 		0.426
(0.781) 		0.204
(0.633) 		0.764
(0.901) 		5e-05
(0.0066) 		0.028
(0.424) 		0.0795
(0.467)
MIRSEQ CNMF 0.662
(0.881) 		0.944
(0.958) 		0.0065
(0.344) 		0.13
(0.497) 		0.127
(0.497) 		0.635
(0.881) 		0.901
(0.944) 		0.753
(0.901) 		0.0851
(0.467) 		0.0768
(0.467) 		0.574
(0.877)
MIRSEQ CHIERARCHICAL 0.87
(0.944) 		0.854
(0.944) 		0.0191
(0.399) 		0.665
(0.881) 		0.246
(0.633) 		0.286
(0.649) 		0.521
(0.839) 		0.883
(0.944) 		0.184
(0.592) 		0.00781
(0.344) 		0.234
(0.633)
MIRseq Mature CNMF subtypes 0.854
(0.944) 		0.683
(0.881) 		0.0143
(0.399) 		0.0734
(0.467) 		0.136
(0.497) 		0.613
(0.88) 		0.512
(0.839) 		0.439
(0.793) 		0.682
(0.881) 		0.51
(0.839) 		0.682
(0.881)
MIRseq Mature cHierClus subtypes 0.885
(0.944) 		0.779
(0.91) 		0.215
(0.633) 		0.0814
(0.467) 		0.0785
(0.467) 		0.135
(0.497) 		0.127
(0.497) 		0.883
(0.944) 		1
(1.00) 		0.3
(0.654) 		0.323
(0.678)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3 4 5
Number of samples 13 8 20 13 15
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.45 (logrank test), Q value = 0.79

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

nPatients nDeath Duration Range (Median), Month
ALL 65 10 1.0 - 52.0 (20.0)
subtype1 13 2 2.0 - 41.0 (21.0)
subtype2 8 0 1.0 - 46.0 (26.0)
subtype3 18 4 1.0 - 50.0 (13.7)
subtype4 12 1 1.0 - 50.0 (20.0)
subtype5 14 3 1.0 - 52.0 (26.0)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.763 (Kruskal-Wallis (anova)), Q value = 0.9

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

nPatients Mean (Std.Dev)
ALL 69 66.6 (10.7)
subtype1 13 62.5 (13.3)
subtype2 8 69.8 (11.5)
subtype3 20 67.0 (11.3)
subtype4 13 67.7 (8.4)
subtype5 15 67.1 (8.8)

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.268 (Fisher's exact test), Q value = 0.63

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

nPatients STAGE I STAGE II STAGE IIA STAGE III STAGE IIIB STAGE IIIC STAGE IV
ALL 18 3 20 2 10 4 12
subtype1 4 1 5 0 2 0 1
subtype2 4 0 3 0 1 0 0
subtype3 6 2 3 2 3 0 4
subtype4 3 0 4 0 2 3 1
subtype5 1 0 5 0 2 1 6

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.637 (Fisher's exact test), Q value = 0.88

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

nPatients T1 T2 T3 T4
ALL 5 15 45 4
subtype1 2 2 9 0
subtype2 1 3 4 0
subtype3 2 5 11 2
subtype4 0 3 10 0
subtype5 0 2 11 2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.534 (Fisher's exact test), Q value = 0.84

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

nPatients N0 N1 N2
ALL 42 15 12
subtype1 10 2 1
subtype2 7 1 0
subtype3 11 6 3
subtype4 7 3 3
subtype5 7 3 5

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0987 (Fisher's exact test), Q value = 0.47

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

nPatients 0 1
ALL 57 12
subtype1 12 1
subtype2 8 0
subtype3 16 4
subtype4 12 1
subtype5 9 6

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.712 (Fisher's exact test), Q value = 0.89

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

nPatients FEMALE MALE
ALL 31 38
subtype1 4 9
subtype2 4 4
subtype3 11 9
subtype4 6 7
subtype5 6 9

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.672 (Fisher's exact test), Q value = 0.88

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

nPatients NO YES
ALL 43 10
subtype1 8 4
subtype2 6 1
subtype3 10 2
subtype4 8 2
subtype5 11 1

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.388 (Fisher's exact test), Q value = 0.72

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

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 58 7
subtype1 10 3
subtype2 7 1
subtype3 15 2
subtype4 11 1
subtype5 15 0

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

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.153 (Fisher's exact test), Q value = 0.53

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

nPatients R0 R1 R2
ALL 57 1 10
subtype1 13 0 0
subtype2 8 0 0
subtype3 14 1 5
subtype4 11 0 1
subtype5 11 0 4

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

'mRNA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.233 (Kruskal-Wallis (anova)), Q value = 0.63

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

nPatients Mean (Std.Dev)
ALL 69 2.2 (5.1)
subtype1 13 0.8 (1.8)
subtype2 8 0.4 (1.1)
subtype3 20 1.5 (2.3)
subtype4 13 3.5 (8.5)
subtype5 15 4.1 (6.9)

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels 1 2 3 4 5 6 7 8 9
Number of samples 6 10 7 9 9 8 9 8 3
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.589 (logrank test), Q value = 0.88

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

nPatients nDeath Duration Range (Median), Month
ALL 65 10 1.0 - 52.0 (20.0)
subtype1 6 2 8.0 - 39.5 (24.0)
subtype2 9 0 1.0 - 43.0 (17.9)
subtype3 7 0 1.0 - 36.0 (13.6)
subtype4 9 1 2.0 - 46.0 (25.0)
subtype5 7 2 1.9 - 48.0 (17.0)
subtype6 8 1 1.0 - 38.0 (30.5)
subtype7 8 1 1.0 - 50.0 (11.5)
subtype8 8 2 1.0 - 39.0 (20.0)
subtype9 3 1 1.0 - 52.0 (50.0)

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

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.368 (Kruskal-Wallis (anova)), Q value = 0.72

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

nPatients Mean (Std.Dev)
ALL 69 66.6 (10.7)
subtype1 6 66.2 (15.5)
subtype2 10 59.9 (14.5)
subtype3 7 61.9 (11.1)
subtype4 9 68.9 (9.5)
subtype5 9 66.9 (8.1)
subtype6 8 71.6 (7.3)
subtype7 9 70.4 (8.6)
subtype8 8 68.0 (9.2)
subtype9 3 65.3 (5.8)

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

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.25 (Fisher's exact test), Q value = 0.63

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE III STAGE IIIB STAGE IIIC STAGE IV
ALL 18 3 20 2 10 4 12
subtype1 2 1 2 0 0 0 1
subtype2 3 1 3 0 2 0 1
subtype3 3 0 2 1 1 0 0
subtype4 3 0 5 0 1 0 0
subtype5 0 1 4 0 1 1 2
subtype6 1 0 1 0 4 0 2
subtype7 2 0 3 0 0 2 2
subtype8 3 0 0 0 1 1 3
subtype9 1 0 0 1 0 0 1

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.354 (Fisher's exact test), Q value = 0.71

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

nPatients T1 T2 T3 T4
ALL 5 15 45 4
subtype1 1 1 4 0
subtype2 1 2 7 0
subtype3 1 2 4 0
subtype4 1 2 6 0
subtype5 0 0 7 2
subtype6 0 2 6 0
subtype7 0 2 7 0
subtype8 0 4 3 1
subtype9 1 0 1 1

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.29 (Fisher's exact test), Q value = 0.65

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

nPatients N0 N1 N2
ALL 42 15 12
subtype1 5 0 1
subtype2 7 2 1
subtype3 5 2 0
subtype4 8 1 0
subtype5 5 2 2
subtype6 3 4 1
subtype7 5 1 3
subtype8 3 3 2
subtype9 1 0 2

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.458 (Fisher's exact test), Q value = 0.79

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 57 12
subtype1 5 1
subtype2 9 1
subtype3 7 0
subtype4 9 0
subtype5 7 2
subtype6 6 2
subtype7 7 2
subtype8 5 3
subtype9 2 1

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.354 (Fisher's exact test), Q value = 0.71

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

nPatients FEMALE MALE
ALL 31 38
subtype1 4 2
subtype2 3 7
subtype3 5 2
subtype4 3 6
subtype5 3 6
subtype6 4 4
subtype7 3 6
subtype8 3 5
subtype9 3 0

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

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.692 (Fisher's exact test), Q value = 0.88

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 43 10
subtype1 4 2
subtype2 5 2
subtype3 5 1
subtype4 5 3
subtype5 6 0
subtype6 6 1
subtype7 5 1
subtype8 6 0
subtype9 1 0

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0289 (Fisher's exact test), Q value = 0.42

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

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 58 7
subtype1 6 0
subtype2 10 0
subtype3 4 1
subtype4 4 4
subtype5 9 0
subtype6 7 1
subtype7 8 1
subtype8 8 0
subtype9 2 0

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

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.374 (Fisher's exact test), Q value = 0.72

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

nPatients R0 R1 R2
ALL 57 1 10
subtype1 6 0 0
subtype2 9 0 1
subtype3 7 0 0
subtype4 8 0 0
subtype5 7 1 1
subtype6 6 0 2
subtype7 7 0 2
subtype8 5 0 3
subtype9 2 0 1

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

'mRNA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.272 (Kruskal-Wallis (anova)), Q value = 0.63

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

nPatients Mean (Std.Dev)
ALL 69 2.2 (5.1)
subtype1 6 1.0 (2.4)
subtype2 10 0.9 (1.5)
subtype3 7 0.4 (0.8)
subtype4 9 0.2 (0.7)
subtype5 9 3.3 (6.7)
subtype6 8 3.6 (6.7)
subtype7 9 5.3 (10.1)
subtype8 8 1.8 (2.0)
subtype9 3 3.3 (3.1)

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

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

Table S25.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 26 40 27 42 30
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.374 (logrank test), Q value = 0.72

Table S26.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 158 27 0.4 - 129.3 (21.0)
subtype1 26 3 1.0 - 129.3 (23.1)
subtype2 38 7 1.0 - 86.6 (25.1)
subtype3 25 5 0.4 - 47.4 (23.0)
subtype4 40 10 1.0 - 126.4 (24.3)
subtype5 29 2 1.0 - 62.0 (20.0)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.227 (Kruskal-Wallis (anova)), Q value = 0.63

Table S27.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 165 64.5 (11.8)
subtype1 26 61.2 (12.0)
subtype2 40 65.3 (12.7)
subtype3 27 66.3 (11.1)
subtype4 42 67.1 (10.3)
subtype5 30 60.9 (12.4)

Figure S24.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.268 (Fisher's exact test), Q value = 0.63

Table S28.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 30 8 40 2 1 4 7 25 14 18 7
subtype1 5 0 7 0 0 0 3 4 2 4 1
subtype2 4 3 13 0 0 1 0 10 2 4 2
subtype3 4 0 3 0 0 1 1 7 4 3 2
subtype4 7 2 11 1 1 2 2 2 4 6 1
subtype5 10 3 6 1 0 0 1 2 2 1 1

Figure S25.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.546 (Fisher's exact test), Q value = 0.85

Table S29.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 28 112 14
subtype1 1 6 18 1
subtype2 1 4 30 4
subtype3 2 3 17 4
subtype4 1 8 30 3
subtype5 4 7 17 2

Figure S26.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.018 (Fisher's exact test), Q value = 0.4

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 84 45 33
subtype1 12 10 4
subtype2 21 13 5
subtype3 7 11 8
subtype4 22 8 11
subtype5 22 3 5

Figure S27.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.629 (Fisher's exact test), Q value = 0.88

Table S31.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 125 24
subtype1 18 5
subtype2 32 6
subtype3 18 4
subtype4 31 7
subtype5 26 2

Figure S28.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.271 (Fisher's exact test), Q value = 0.63

Table S32.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 76 89
subtype1 12 14
subtype2 23 17
subtype3 10 17
subtype4 21 21
subtype5 10 20

Figure S29.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.827 (Fisher's exact test), Q value = 0.93

Table S33.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 112 20
subtype1 18 2
subtype2 28 5
subtype3 17 5
subtype4 26 5
subtype5 23 3

Figure S30.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0738 (Fisher's exact test), Q value = 0.47

Table S34.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 146 13
subtype1 23 2
subtype2 39 1
subtype3 25 0
subtype4 35 5
subtype5 24 5

Figure S31.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.92 (Fisher's exact test), Q value = 0.94

Table S35.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 123 2 12 4
subtype1 18 1 1 0
subtype2 30 1 4 2
subtype3 19 0 2 0
subtype4 30 0 4 1
subtype5 26 0 1 1

Figure S32.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0466 (Kruskal-Wallis (anova)), Q value = 0.47

Table S36.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 156 2.6 (5.3)
subtype1 23 1.6 (2.4)
subtype2 38 1.4 (2.4)
subtype3 26 3.6 (4.7)
subtype4 40 4.0 (7.8)
subtype5 29 2.1 (5.8)

Figure S33.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3 4 5
Number of samples 14 23 17 26 18
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.922 (logrank test), Q value = 0.94

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

nPatients nDeath Duration Range (Median), Month
ALL 95 17 0.4 - 129.3 (24.6)
subtype1 14 2 2.5 - 55.8 (22.5)
subtype2 23 3 10.8 - 47.4 (25.1)
subtype3 17 4 0.4 - 129.3 (31.0)
subtype4 25 5 1.0 - 117.1 (20.9)
subtype5 16 3 11.9 - 126.4 (27.8)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0845 (Kruskal-Wallis (anova)), Q value = 0.47

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

nPatients Mean (Std.Dev)
ALL 98 62.9 (12.3)
subtype1 14 56.4 (14.7)
subtype2 23 64.7 (11.2)
subtype3 17 62.5 (10.4)
subtype4 26 67.8 (11.7)
subtype5 18 59.1 (11.7)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.307 (Fisher's exact test), Q value = 0.65

Table S40.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 11 6 20 2 1 3 7 15 10 6 7
subtype1 1 1 2 0 0 0 2 1 2 2 1
subtype2 4 0 5 1 0 0 4 4 2 0 2
subtype3 2 0 5 0 0 0 0 5 1 0 3
subtype4 3 3 6 1 1 3 0 3 2 1 0
subtype5 1 2 2 0 0 0 1 2 3 3 1

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.91 (Fisher's exact test), Q value = 0.94

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

nPatients T1 T2 T3 T4
ALL 4 12 69 11
subtype1 1 1 10 2
subtype2 1 5 14 3
subtype3 0 2 13 1
subtype4 2 2 20 2
subtype5 0 2 12 3

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.518 (Fisher's exact test), Q value = 0.84

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

nPatients N0 N1 N2
ALL 42 30 22
subtype1 5 6 3
subtype2 10 9 4
subtype3 7 6 3
subtype4 15 4 6
subtype5 5 5 6

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

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.273 (Fisher's exact test), Q value = 0.63

Table S43.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 69 12
subtype1 8 3
subtype2 18 2
subtype3 12 3
subtype4 21 1
subtype5 10 3

Figure S39.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.808 (Fisher's exact test), Q value = 0.93

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

nPatients FEMALE MALE
ALL 44 54
subtype1 5 9
subtype2 9 14
subtype3 8 9
subtype4 12 14
subtype5 10 8

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.988 (Fisher's exact test), Q value = 1

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 69 11
subtype1 10 2
subtype2 18 3
subtype3 12 1
subtype4 17 3
subtype5 12 2

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.107 (Fisher's exact test), Q value = 0.47

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

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 90 6
subtype1 11 3
subtype2 20 1
subtype3 17 0
subtype4 24 2
subtype5 18 0

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

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0957 (Fisher's exact test), Q value = 0.47

Table S47.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 66 2 2 5
subtype1 7 1 1 1
subtype2 18 0 0 0
subtype3 12 0 0 0
subtype4 20 1 1 1
subtype5 9 0 0 3

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

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.723 (Kruskal-Wallis (anova)), Q value = 0.89

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

nPatients Mean (Std.Dev)
ALL 88 3.1 (5.7)
subtype1 10 4.8 (9.1)
subtype2 22 2.8 (5.0)
subtype3 17 1.8 (2.8)
subtype4 23 3.2 (6.6)
subtype5 16 3.7 (5.0)

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

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3 4 5
Number of samples 42 28 26 31 4
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.591 (logrank test), Q value = 0.88

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

nPatients nDeath Duration Range (Median), Month
ALL 126 27 0.4 - 129.3 (20.9)
subtype1 41 9 0.4 - 129.3 (21.0)
subtype2 28 8 2.5 - 117.1 (25.0)
subtype3 24 5 1.0 - 46.0 (17.5)
subtype4 29 5 1.0 - 126.4 (21.2)
subtype5 4 0 1.0 - 37.0 (26.2)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0893 (Kruskal-Wallis (anova)), Q value = 0.47

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

nPatients Mean (Std.Dev)
ALL 131 65.4 (11.8)
subtype1 42 64.7 (13.0)
subtype2 28 68.4 (9.2)
subtype3 26 68.2 (11.1)
subtype4 31 60.9 (12.0)
subtype5 4 69.8 (9.2)

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

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0335 (Fisher's exact test), Q value = 0.44

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

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 21 7 32 1 5 7 21 9 16 4
subtype1 8 0 8 1 3 4 6 0 9 1
subtype2 3 0 7 0 1 1 5 4 0 3
subtype3 5 3 6 0 0 0 3 3 6 0
subtype4 5 4 9 0 1 2 6 2 1 0
subtype5 0 0 2 0 0 0 1 0 0 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.797 (Fisher's exact test), Q value = 0.92

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

nPatients T1 T2 T3 T4
ALL 5 23 91 10
subtype1 3 10 25 3
subtype2 0 4 20 3
subtype3 2 3 19 2
subtype4 0 6 23 2
subtype5 0 0 4 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.462 (Fisher's exact test), Q value = 0.79

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

nPatients N0 N1 N2
ALL 64 37 26
subtype1 18 15 8
subtype2 11 7 9
subtype3 14 6 6
subtype4 19 8 3
subtype5 2 1 0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.169 (Fisher's exact test), Q value = 0.57

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

nPatients 0 1
ALL 98 20
subtype1 28 9
subtype2 21 4
subtype3 20 6
subtype4 26 1
subtype5 3 0

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.26 (Fisher's exact test), Q value = 0.63

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

nPatients FEMALE MALE
ALL 61 70
subtype1 24 18
subtype2 14 14
subtype3 12 14
subtype4 10 21
subtype5 1 3

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.209 (Fisher's exact test), Q value = 0.63

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

nPatients NO YES
ALL 88 16
subtype1 27 5
subtype2 24 1
subtype3 15 6
subtype4 19 4
subtype5 3 0

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.694 (Fisher's exact test), Q value = 0.88

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

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 118 10
subtype1 37 4
subtype2 26 2
subtype3 21 3
subtype4 30 1
subtype5 4 0

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

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0409 (Fisher's exact test), Q value = 0.45

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

nPatients R0 R1 R2 RX
ALL 98 2 12 3
subtype1 30 0 6 0
subtype2 22 1 0 0
subtype3 19 0 5 1
subtype4 24 1 1 1
subtype5 3 0 0 1

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

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.15 (Kruskal-Wallis (anova)), Q value = 0.53

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

nPatients Mean (Std.Dev)
ALL 126 2.4 (4.9)
subtype1 40 2.5 (4.4)
subtype2 28 3.5 (6.0)
subtype3 25 2.7 (6.3)
subtype4 30 1.2 (2.8)
subtype5 3 0.3 (0.6)

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

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

Cluster Labels 1 2 3 4 5 6
Number of samples 20 21 25 19 30 16
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.744 (logrank test), Q value = 0.9

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

nPatients nDeath Duration Range (Median), Month
ALL 126 27 0.4 - 129.3 (20.9)
subtype1 18 4 1.0 - 39.5 (18.5)
subtype2 21 6 1.0 - 117.1 (25.0)
subtype3 24 4 7.2 - 126.4 (25.5)
subtype4 19 4 4.0 - 48.0 (21.2)
subtype5 28 6 1.0 - 62.0 (18.9)
subtype6 16 3 0.4 - 129.3 (20.3)

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0698 (Kruskal-Wallis (anova)), Q value = 0.47

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

nPatients Mean (Std.Dev)
ALL 131 65.4 (11.8)
subtype1 20 70.8 (12.5)
subtype2 21 69.0 (9.3)
subtype3 25 61.9 (12.8)
subtype4 19 64.7 (10.1)
subtype5 30 62.0 (12.2)
subtype6 16 66.9 (10.7)

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

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.307 (Fisher's exact test), Q value = 0.65

Table S64.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 21 7 32 1 5 7 21 9 16 4
subtype1 3 1 4 0 2 0 2 3 5 0
subtype2 3 0 5 0 1 1 5 2 0 2
subtype3 3 1 8 0 0 2 6 2 1 1
subtype4 4 2 5 0 0 0 2 1 4 0
subtype5 6 3 6 0 2 3 2 0 6 0
subtype6 2 0 4 1 0 1 4 1 0 1

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.9 (Fisher's exact test), Q value = 0.94

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

nPatients T1 T2 T3 T4
ALL 5 23 91 10
subtype1 1 2 15 2
subtype2 0 4 15 2
subtype3 0 4 19 1
subtype4 1 3 15 0
subtype5 2 7 18 3
subtype6 1 3 9 2

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.82 (Fisher's exact test), Q value = 0.93

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

nPatients N0 N1 N2
ALL 64 37 26
subtype1 8 5 7
subtype2 9 7 5
subtype3 13 6 5
subtype4 11 4 3
subtype5 15 9 5
subtype6 8 6 1

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.528 (Fisher's exact test), Q value = 0.84

Table S67.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 98 20
subtype1 15 5
subtype2 17 2
subtype3 21 2
subtype4 12 4
subtype5 22 6
subtype6 11 1

Figure S61.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.598 (Fisher's exact test), Q value = 0.88

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

nPatients FEMALE MALE
ALL 61 70
subtype1 11 9
subtype2 12 9
subtype3 10 15
subtype4 6 13
subtype5 14 16
subtype6 8 8

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

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.759 (Fisher's exact test), Q value = 0.9

Table S69.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 88 16
subtype1 11 2
subtype2 17 1
subtype3 16 5
subtype4 15 3
subtype5 20 3
subtype6 9 2

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.92 (Fisher's exact test), Q value = 0.94

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

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 118 10
subtype1 18 1
subtype2 20 1
subtype3 23 2
subtype4 17 1
subtype5 25 4
subtype6 15 1

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

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.265 (Fisher's exact test), Q value = 0.63

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

nPatients R0 R1 R2 RX
ALL 98 2 12 3
subtype1 15 0 5 0
subtype2 19 1 0 0
subtype3 18 0 1 1
subtype4 13 0 2 1
subtype5 21 1 4 1
subtype6 12 0 0 0

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

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.383 (Kruskal-Wallis (anova)), Q value = 0.72

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

nPatients Mean (Std.Dev)
ALL 126 2.4 (4.9)
subtype1 20 4.3 (7.7)
subtype2 21 3.4 (6.7)
subtype3 24 1.7 (2.4)
subtype4 18 1.2 (2.6)
subtype5 29 2.0 (3.8)
subtype6 14 1.6 (3.2)

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4 5
Number of samples 32 14 34 28 58
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.604 (logrank test), Q value = 0.88

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

nPatients nDeath Duration Range (Median), Month
ALL 159 26 0.4 - 129.3 (21.0)
subtype1 32 8 0.4 - 129.3 (24.6)
subtype2 14 1 2.5 - 55.8 (27.2)
subtype3 34 3 6.0 - 62.0 (21.1)
subtype4 27 7 1.0 - 126.4 (26.4)
subtype5 52 7 1.0 - 117.1 (17.5)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.124 (Kruskal-Wallis (anova)), Q value = 0.5

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

nPatients Mean (Std.Dev)
ALL 166 64.7 (11.7)
subtype1 32 63.4 (11.7)
subtype2 14 68.9 (9.8)
subtype3 34 61.1 (13.1)
subtype4 28 63.5 (13.3)
subtype5 58 67.0 (9.8)

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

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0513 (Fisher's exact test), Q value = 0.47

Table S76.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 30 8 40 2 1 5 7 25 14 17 7
subtype1 1 2 7 0 0 0 2 4 7 3 3
subtype2 4 0 2 1 0 0 1 4 0 0 1
subtype3 8 1 9 0 0 1 3 3 2 2 1
subtype4 5 1 6 1 1 0 1 5 2 2 2
subtype5 12 4 16 0 0 4 0 9 3 10 0

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.353 (Fisher's exact test), Q value = 0.71

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

nPatients T1 T2 T3 T4
ALL 9 28 113 14
subtype1 0 2 24 5
subtype2 0 4 8 2
subtype3 3 8 22 1
subtype4 2 5 18 2
subtype5 4 9 41 4

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.246 (Fisher's exact test), Q value = 0.63

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

nPatients N0 N1 N2
ALL 84 45 33
subtype1 10 8 12
subtype2 8 5 1
subtype3 19 9 5
subtype4 14 9 4
subtype5 33 14 11

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.68 (Fisher's exact test), Q value = 0.88

Table S79.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 126 23
subtype1 23 6
subtype2 8 0
subtype3 26 3
subtype4 21 4
subtype5 48 10

Figure S72.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.49 (Fisher's exact test), Q value = 0.83

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

nPatients FEMALE MALE
ALL 75 91
subtype1 17 15
subtype2 6 8
subtype3 11 23
subtype4 14 14
subtype5 27 31

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.725 (Fisher's exact test), Q value = 0.89

Table S81.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 112 21
subtype1 25 4
subtype2 10 2
subtype3 27 4
subtype4 19 2
subtype5 31 9

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.103 (Fisher's exact test), Q value = 0.47

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

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 147 13
subtype1 29 3
subtype2 12 0
subtype3 27 6
subtype4 28 0
subtype5 51 4

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

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0403 (Fisher's exact test), Q value = 0.45

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

nPatients R0 R1 R2 RX
ALL 123 2 12 5
subtype1 23 0 2 2
subtype2 7 1 0 0
subtype3 29 0 0 1
subtype4 17 0 1 2
subtype5 47 1 9 0

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

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0801 (Kruskal-Wallis (anova)), Q value = 0.47

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

nPatients Mean (Std.Dev)
ALL 156 2.7 (5.5)
subtype1 30 4.0 (5.9)
subtype2 13 2.2 (5.7)
subtype3 31 2.5 (5.9)
subtype4 24 1.6 (2.3)
subtype5 58 2.7 (5.9)

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3 4 5 6 7
Number of samples 27 36 12 20 33 18 20
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.183 (logrank test), Q value = 0.59

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

nPatients nDeath Duration Range (Median), Month
ALL 159 26 0.4 - 129.3 (21.0)
subtype1 27 8 0.4 - 129.3 (25.1)
subtype2 35 2 2.5 - 55.8 (26.0)
subtype3 12 4 10.0 - 43.2 (20.6)
subtype4 20 3 1.0 - 78.1 (21.3)
subtype5 29 6 1.0 - 117.1 (20.0)
subtype6 18 1 1.0 - 62.0 (20.0)
subtype7 18 2 1.0 - 52.0 (16.5)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.464 (Kruskal-Wallis (anova)), Q value = 0.79

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

nPatients Mean (Std.Dev)
ALL 166 64.7 (11.7)
subtype1 27 62.9 (10.2)
subtype2 36 63.6 (13.0)
subtype3 12 60.8 (13.3)
subtype4 20 62.9 (14.5)
subtype5 33 68.3 (9.4)
subtype6 18 63.5 (12.4)
subtype7 20 68.2 (8.4)

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0212 (Fisher's exact test), Q value = 0.4

Table S88.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 30 8 40 2 1 5 7 25 14 17 7
subtype1 1 0 9 0 1 0 0 5 5 3 1
subtype2 5 1 6 1 0 1 3 7 0 3 3
subtype3 1 1 2 0 0 0 2 1 3 0 1
subtype4 6 0 4 1 0 0 2 2 2 0 2
subtype5 7 4 8 0 0 2 0 4 2 6 0
subtype6 6 2 7 0 0 1 0 2 0 0 0
subtype7 4 0 4 0 0 1 0 4 2 5 0

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.599 (Fisher's exact test), Q value = 0.88

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

nPatients T1 T2 T3 T4
ALL 9 28 113 14
subtype1 0 1 21 3
subtype2 2 5 25 4
subtype3 0 2 9 1
subtype4 3 5 10 2
subtype5 1 7 22 3
subtype6 2 4 12 0
subtype7 1 4 14 1

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.1 (Fisher's exact test), Q value = 0.47

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

nPatients N0 N1 N2
ALL 84 45 33
subtype1 11 5 9
subtype2 15 15 6
subtype3 4 4 3
subtype4 11 5 3
subtype5 19 8 6
subtype6 15 3 0
subtype7 9 5 6

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.426 (Fisher's exact test), Q value = 0.78

Table S91.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 126 23
subtype1 21 4
subtype2 22 5
subtype3 8 1
subtype4 15 2
subtype5 27 6
subtype6 18 0
subtype7 15 5

Figure S83.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.204 (Fisher's exact test), Q value = 0.63

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

nPatients FEMALE MALE
ALL 75 91
subtype1 13 14
subtype2 19 17
subtype3 1 11
subtype4 10 10
subtype5 14 19
subtype6 8 10
subtype7 10 10

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

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.764 (Fisher's exact test), Q value = 0.9

Table S93.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 112 21
subtype1 19 2
subtype2 28 5
subtype3 10 2
subtype4 15 1
subtype5 18 6
subtype6 12 3
subtype7 10 2

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 5e-05 (Fisher's exact test), Q value = 0.0066

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

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 147 13
subtype1 27 0
subtype2 30 4
subtype3 11 1
subtype4 20 0
subtype5 31 1
subtype6 9 7
subtype7 19 0

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

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.028 (Fisher's exact test), Q value = 0.42

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

nPatients R0 R1 R2 RX
ALL 123 2 12 5
subtype1 19 0 1 1
subtype2 23 1 1 0
subtype3 9 0 0 1
subtype4 13 0 0 3
subtype5 26 1 6 0
subtype6 17 0 0 0
subtype7 16 0 4 0

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

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0795 (Kruskal-Wallis (anova)), Q value = 0.47

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

nPatients Mean (Std.Dev)
ALL 156 2.7 (5.5)
subtype1 26 2.8 (3.4)
subtype2 33 3.9 (7.9)
subtype3 11 2.5 (3.3)
subtype4 15 1.6 (2.6)
subtype5 33 2.3 (4.8)
subtype6 18 0.2 (0.5)
subtype7 20 4.2 (7.8)

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

Clustering Approach #9: 'MIRSEQ CNMF'

Table S97.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 24 56 63
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.662 (logrank test), Q value = 0.88

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

nPatients nDeath Duration Range (Median), Month
ALL 136 25 0.4 - 129.3 (24.1)
subtype1 24 7 0.4 - 129.3 (29.3)
subtype2 55 10 1.0 - 126.4 (25.1)
subtype3 57 8 1.0 - 117.1 (19.0)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.944 (Kruskal-Wallis (anova)), Q value = 0.96

Table S99.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 143 65.4 (11.5)
subtype1 24 65.7 (8.5)
subtype2 56 64.5 (13.2)
subtype3 63 66.1 (11.0)

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.0065 (Fisher's exact test), Q value = 0.34

Table S100.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 28 8 32 2 5 4 21 13 15 6
subtype1 2 1 6 0 0 0 1 5 2 3
subtype2 11 1 10 2 1 4 12 4 3 3
subtype3 15 6 16 0 4 0 8 4 10 0

Figure S91.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.13 (Fisher's exact test), Q value = 0.5

Table S101.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 26 96 10
subtype1 2 0 17 3
subtype2 4 12 36 4
subtype3 3 14 43 3

Figure S92.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.127 (Fisher's exact test), Q value = 0.5

Table S102.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 73 38 28
subtype1 10 4 7
subtype2 25 21 9
subtype3 38 13 12

Figure S93.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.635 (Fisher's exact test), Q value = 0.88

Table S103.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 106 20
subtype1 14 4
subtype2 39 6
subtype3 53 10

Figure S94.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.901 (Fisher's exact test), Q value = 0.94

Table S104.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 66 77
subtype1 12 12
subtype2 26 30
subtype3 28 35

Figure S95.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.753 (Fisher's exact test), Q value = 0.9

Table S105.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 94 18
subtype1 18 3
subtype2 39 6
subtype3 37 9

Figure S96.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0851 (Fisher's exact test), Q value = 0.47

Table S106.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 127 10
subtype1 21 2
subtype2 54 1
subtype3 52 7

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0768 (Fisher's exact test), Q value = 0.47

Table S107.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 102 2 11 4
subtype1 17 0 1 1
subtype2 33 1 1 3
subtype3 52 1 9 0

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.574 (Kruskal-Wallis (anova)), Q value = 0.88

Table S108.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 133 2.6 (5.3)
subtype1 23 3.0 (5.2)
subtype2 47 2.4 (4.9)
subtype3 63 2.5 (5.7)

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S109.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 24 63 56
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.87 (logrank test), Q value = 0.94

Table S110.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 136 25 0.4 - 129.3 (24.1)
subtype1 21 5 0.4 - 129.3 (32.0)
subtype2 62 12 1.0 - 126.4 (25.1)
subtype3 53 8 1.0 - 62.0 (17.0)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.854 (Kruskal-Wallis (anova)), Q value = 0.94

Table S111.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 143 65.4 (11.5)
subtype1 24 64.5 (11.6)
subtype2 63 65.4 (12.2)
subtype3 56 65.8 (10.8)

Figure S101.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.0191 (Fisher's exact test), Q value = 0.4

Table S112.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 28 8 32 2 5 4 21 13 15 6
subtype1 2 1 4 1 1 1 5 2 4 1
subtype2 11 2 14 1 1 3 11 7 1 5
subtype3 15 5 14 0 3 0 5 4 10 0

Figure S102.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.665 (Fisher's exact test), Q value = 0.88

Table S113.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 9 26 96 10
subtype1 1 2 18 2
subtype2 4 11 41 6
subtype3 4 13 37 2

Figure S103.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.246 (Fisher's exact test), Q value = 0.63

Table S114.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 73 38 28
subtype1 8 9 5
subtype2 30 18 13
subtype3 35 11 10

Figure S104.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.286 (Fisher's exact test), Q value = 0.65

Table S115.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 106 20
subtype1 16 5
subtype2 44 5
subtype3 46 10

Figure S105.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.521 (Fisher's exact test), Q value = 0.84

Table S116.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 66 77
subtype1 9 15
subtype2 32 31
subtype3 25 31

Figure S106.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.883 (Fisher's exact test), Q value = 0.94

Table S117.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 94 18
subtype1 13 3
subtype2 45 9
subtype3 36 6

Figure S107.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.184 (Fisher's exact test), Q value = 0.59

Table S118.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 127 10
subtype1 21 3
subtype2 59 2
subtype3 47 5

Figure S108.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.00781 (Fisher's exact test), Q value = 0.34

Table S119.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 102 2 11 4
subtype1 15 0 4 1
subtype2 40 1 0 3
subtype3 47 1 7 0

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.234 (Kruskal-Wallis (anova)), Q value = 0.63

Table S120.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 133 2.6 (5.3)
subtype1 20 2.9 (4.5)
subtype2 57 2.7 (5.3)
subtype3 56 2.3 (5.6)

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S121.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 17 15 13 6
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.854 (logrank test), Q value = 0.94

Table S122.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 51 12 0.4 - 129.3 (26.4)
subtype1 17 4 0.4 - 129.3 (36.5)
subtype2 15 4 2.5 - 55.8 (25.1)
subtype3 13 3 7.2 - 57.2 (21.0)
subtype4 6 1 11.0 - 52.0 (31.0)

Figure S111.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.683 (Kruskal-Wallis (anova)), Q value = 0.88

Table S123.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 51 64.5 (12.0)
subtype1 17 65.6 (9.3)
subtype2 15 64.2 (11.6)
subtype3 13 66.3 (15.7)
subtype4 6 58.7 (11.7)

Figure S112.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0143 (Fisher's exact test), Q value = 0.4

Table S124.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 6 1 11 1 3 8 5 4 6
subtype1 0 1 6 0 0 0 3 2 3
subtype2 1 0 2 0 3 4 0 1 0
subtype3 2 0 3 1 0 3 2 1 1
subtype4 3 0 0 0 0 1 0 0 2

Figure S113.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0734 (Fisher's exact test), Q value = 0.47

Table S125.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 2 7 36 4
subtype1 0 0 13 2
subtype2 1 3 11 0
subtype3 0 2 10 1
subtype4 1 2 2 1

Figure S114.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.136 (Fisher's exact test), Q value = 0.5

Table S126.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 19 17 12
subtype1 7 2 6
subtype2 4 9 1
subtype3 5 4 4
subtype4 3 2 1

Figure S115.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.613 (Fisher's exact test), Q value = 0.88

Table S127.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 30 9
subtype1 9 4
subtype2 7 2
subtype3 10 1
subtype4 4 2

Figure S116.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.512 (Fisher's exact test), Q value = 0.84

Table S128.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 20 31
subtype1 9 8
subtype2 4 11
subtype3 5 8
subtype4 2 4

Figure S117.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.439 (Fisher's exact test), Q value = 0.79

Table S129.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 37 5
subtype1 12 3
subtype2 10 0
subtype3 9 2
subtype4 6 0

Figure S118.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.682 (Fisher's exact test), Q value = 0.88

Table S130.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 49 1
subtype1 16 0
subtype2 14 1
subtype3 13 0
subtype4 6 0

Figure S119.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.51 (Fisher's exact test), Q value = 0.84

Table S131.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 31 1 2 2
subtype1 11 0 2 0
subtype2 6 1 0 1
subtype3 9 0 0 1
subtype4 5 0 0 0

Figure S120.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.682 (Kruskal-Wallis (anova)), Q value = 0.88

Table S132.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 46 3.2 (5.7)
subtype1 17 3.3 (5.4)
subtype2 12 3.2 (7.6)
subtype3 12 3.9 (5.2)
subtype4 5 1.6 (3.0)

Figure S121.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S133.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 12 12 16 6 5
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.885 (logrank test), Q value = 0.94

Table S134.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 51 12 0.4 - 129.3 (26.4)
subtype1 12 2 0.4 - 48.2 (22.8)
subtype2 12 3 2.5 - 129.3 (33.2)
subtype3 16 5 15.8 - 55.8 (24.8)
subtype4 6 1 7.2 - 52.0 (28.5)
subtype5 5 1 17.0 - 49.5 (32.2)

Figure S122.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.779 (Kruskal-Wallis (anova)), Q value = 0.91

Table S135.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 51 64.5 (12.0)
subtype1 12 62.2 (13.3)
subtype2 12 64.5 (12.0)
subtype3 16 65.4 (11.9)
subtype4 6 69.5 (14.2)
subtype5 5 61.4 (7.4)

Figure S123.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.215 (Fisher's exact test), Q value = 0.63

Table S136.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 6 1 11 1 3 8 5 4 6
subtype1 1 0 2 0 0 1 1 3 2
subtype2 0 1 4 0 0 2 2 0 2
subtype3 1 0 2 1 3 4 1 1 1
subtype4 4 0 1 0 0 1 0 0 0
subtype5 0 0 2 0 0 0 1 0 1

Figure S124.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0814 (Fisher's exact test), Q value = 0.47

Table S137.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 2 7 36 4
subtype1 0 1 10 0
subtype2 0 0 10 2
subtype3 1 3 11 1
subtype4 1 3 2 0
subtype5 0 0 3 1

Figure S125.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0785 (Fisher's exact test), Q value = 0.47

Table S138.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients N0 N1 N2
ALL 19 17 12
subtype1 3 5 2
subtype2 6 2 4
subtype3 3 9 4
subtype4 5 1 0
subtype5 2 0 2

Figure S126.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.135 (Fisher's exact test), Q value = 0.5

Table S139.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 30 9
subtype1 4 5
subtype2 8 1
subtype3 10 2
subtype4 5 0
subtype5 3 1

Figure S127.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.127 (Fisher's exact test), Q value = 0.5

Table S140.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 20 31
subtype1 4 8
subtype2 8 4
subtype3 3 13
subtype4 3 3
subtype5 2 3

Figure S128.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.883 (Fisher's exact test), Q value = 0.94

Table S141.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients NO YES
ALL 37 5
subtype1 7 1
subtype2 8 2
subtype3 11 2
subtype4 6 0
subtype5 5 0

Figure S129.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

Table S142.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 49 1
subtype1 12 0
subtype2 11 0
subtype3 15 1
subtype4 6 0
subtype5 5 0

Figure S130.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.3 (Fisher's exact test), Q value = 0.65

Table S143.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients R0 R1 R2 RX
ALL 31 1 2 2
subtype1 5 0 2 1
subtype2 8 0 0 0
subtype3 10 1 0 0
subtype4 5 0 0 1
subtype5 3 0 0 0

Figure S131.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.323 (Kruskal-Wallis (anova)), Q value = 0.68

Table S144.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 46 3.2 (5.7)
subtype1 8 1.9 (2.5)
subtype2 12 3.8 (6.2)
subtype3 16 4.6 (7.5)
subtype4 5 0.2 (0.4)
subtype5 5 2.8 (3.8)

Figure S132.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/READ-TP/22541962/READ-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/READ-TP/22507018/READ-TP.merged_data.txt

  • Number of patients = 169

  • Number of clustering approaches = 12

  • 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

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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