Colon/Rectal Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 7 different clustering approaches and 11 clinical features across 585 patients, 14 significant findings detected with P value < 0.05.

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

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

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

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

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

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'PATHOLOGY.N' and 'NEOADJUVANT.THERAPY'.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 2 subtypes that 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 7 different clustering approaches and 11 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 14 significant findings detected.

Clinical Features | Statistical Tests | mRNA CNMF subtypes | mRNA cHierClus subtypes | METHLYATION CNMF | RNAseq CNMF subtypes | RNAseq cHierClus subtypes | MIRseq CNMF subtypes | MIRseq cHierClus subtypes
Time to Death | logrank test | 0.145 | 0.805 | 0.973 | 0.12 | 0.176 | 0.0911 | 0.738
AGE | t-test | 0.596 | 0.148 | 0.004 | 0.416 | 0.884 | 0.82 | 0.472
PRIMARY SITE OF DISEASE | Fisher's exact test | 0.0126 | 0.035 | 0.000825 | 0.111 | 0.265 | 0.965 | 0.76
GENDER | Fisher's exact test | 0.072 | 0.796 | 0.111 | 0.869 | 0.924 | 0.653 | 0.42
HISTOLOGICAL TYPE | Chi-square test | 6.39e-07 | 3.6e-08 | 0.00132 | 0.0132 | 0.00215 | 0.942 | 0.513
PATHOLOGY T | Chi-square test | 0.184 | 0.866 | 0.351 | 0.439 | 0.71 | 0.331 | 0.208
PATHOLOGY N | Chi-square test | 0.00595 | 0.65 | 0.347 | 0.872 | 0.969 | 0.0292 | 0.946
PATHOLOGICSPREAD(M) | Chi-square test | 0.00173 | 0.498 | 0.495 | 0.28 | 0.852 | 0.1 | 0.313
TUMOR STAGE | Chi-square test | 0.0119 | 0.633 | 0.268 | 0.762 | 0.998 | 0.179 | 0.692
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.393 | 0.496 | 0.0895 | 0.41 | 1 | 1 | 0.53
NEOADJUVANT THERAPY | Fisher's exact test | 0.127 | 0.712 | 0.302 | 0.41 | 1 | 0.00452 | 0.104

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 63 | 48 | 73 | 40

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.145 (logrank test)

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

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

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.596 (ANOVA)

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

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

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

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

P value = 0.0126 (Fisher's exact test)

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.072 (Fisher's exact test)

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

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

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.184 (Chi-square test)

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

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

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.00595 (Chi-square test)

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

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

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

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

P value = 0.00173 (Chi-square test)

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

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

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0119 (Chi-square test)

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

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

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

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

P value = 0.393 (Fisher's exact test)

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

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

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.127 (Fisher's exact test)

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 47 | 64 | 113

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.805 (logrank test)

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

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

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.148 (ANOVA)

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

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

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

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

P value = 0.035 (Fisher's exact test)

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.796 (Fisher's exact test)

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

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

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

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

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.866 (Chi-square test)

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

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

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.65 (Chi-square test)

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

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

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

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

P value = 0.498 (Chi-square test)

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

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

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

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.633 (Chi-square test)

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

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

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

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

P value = 0.496 (Fisher's exact test)

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

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

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

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.712 (Fisher's exact test)

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

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

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

Clustering Approach #3: 'METHLYATION CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 136 | 102 | 111

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.973 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 324 | 44 | 0.1 - 135.5 (7.0)
subtype1 | 126 | 18 | 0.1 - 135.5 (7.0)
subtype2 | 95 | 10 | 0.1 - 102.4 (6.0)
subtype3 | 103 | 16 | 0.1 - 129.1 (7.6)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.004 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 348 | 64.8 (13.0)
subtype1 | 136 | 64.9 (12.5)
subtype2 | 101 | 61.7 (13.1)
subtype3 | 111 | 67.6 (13.0)

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

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

P value = 0.000825 (Fisher's exact test)

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

nPatients | COLON | RECTUM
--- | --- | ---
ALL | 254 | 93
subtype1 | 89 | 47
subtype2 | 70 | 30
subtype3 | 95 | 16

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.111 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 193 | 156
subtype1 | 77 | 59
subtype2 | 63 | 39
subtype3 | 53 | 58

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00132 (Chi-square test)

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

nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA | RECTAL ADENOCARCINOMA | RECTAL MUCINOUS ADENOCARCINOMA
--- | --- | --- | --- | ---
ALL | 224 | 30 | 87 | 6
subtype1 | 85 | 4 | 45 | 2
subtype2 | 57 | 13 | 28 | 2
subtype3 | 82 | 13 | 14 | 2

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.351 (Chi-square test)

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

nPatients | T0+T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 11 | 51 | 243 | 42
subtype1 | 4 | 21 | 94 | 17
subtype2 | 3 | 10 | 71 | 17
subtype3 | 4 | 20 | 78 | 8

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.347 (Chi-square test)

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 193 | 93 | 58
subtype1 | 67 | 42 | 23
subtype2 | 56 | 28 | 17
subtype3 | 70 | 23 | 18

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.495 (Chi-square test)

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

nPatients | M0 | M1 | M1A | M1B | MX
--- | --- | --- | --- | --- | ---
ALL | 246 | 37 | 8 | 1 | 50
subtype1 | 92 | 20 | 4 | 0 | 19
subtype2 | 74 | 8 | 3 | 0 | 14
subtype3 | 80 | 9 | 1 | 1 | 17

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.268 (Chi-square test)

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 48 | 126 | 106 | 49
subtype1 | 15 | 44 | 46 | 24
subtype2 | 12 | 38 | 32 | 12
subtype3 | 21 | 44 | 28 | 13

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

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

P value = 0.0895 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 341 | 8
subtype1 | 132 | 4
subtype2 | 98 | 4
subtype3 | 111 | 0

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.302 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 275 | 74
subtype1 | 109 | 27
subtype2 | 75 | 27
subtype3 | 91 | 20

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

Clustering Approach #4: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 17 | 17 | 27 | 22

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.12 (logrank test)

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

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

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.416 (ANOVA)

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

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

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

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

P value = 0.111 (Fisher's exact test)

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

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

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.869 (Fisher's exact test)

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

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

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0132 (Chi-square test)

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

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

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.439 (Chi-square test)

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

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

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.872 (Chi-square test)

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

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

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

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

P value = 0.28 (Chi-square test)

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

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

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

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.762 (Chi-square test)

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

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

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

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

P value = 0.41 (Fisher's exact test)

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

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

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.41 (Fisher's exact test)

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

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

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

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S49. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 17 | 16 | 28 | 22

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.176 (logrank test)

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

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

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.884 (ANOVA)

Table S51. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

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

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

P value = 0.265 (Fisher's exact test)

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

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

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.924 (Fisher's exact test)

Table S53. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

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

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00215 (Chi-square test)

Table S54. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.71 (Chi-square test)

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

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

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.969 (Chi-square test)

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

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

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

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

P value = 0.852 (Chi-square test)

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

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

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

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.998 (Chi-square test)

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

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

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

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

P value = 1 (Fisher's exact test)

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

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

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

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

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

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

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

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S61. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 54 | 114 | 87

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0911 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 141 | 20 | 0.9 - 72.1 (9.1)
subtype1 | 32 | 8 | 0.9 - 56.4 (11.8)
subtype2 | 76 | 10 | 0.9 - 72.1 (14.1)
subtype3 | 33 | 2 | 0.9 - 38.9 (1.0)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.82 (ANOVA)

Table S63. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 255 | 68.6 (12.2)
subtype1 | 54 | 68.1 (11.0)
subtype2 | 114 | 68.4 (12.6)
subtype3 | 87 | 69.3 (12.4)

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

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

P value = 0.965 (Fisher's exact test)

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

nPatients | COLON | RECTUM
--- | --- | ---
ALL | 186 | 67
subtype1 | 40 | 13
subtype2 | 83 | 31
subtype3 | 63 | 23

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.653 (Fisher's exact test)

Table S65. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 128 | 127
subtype1 | 24 | 30
subtype2 | 59 | 55
subtype3 | 45 | 42

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

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.942 (Chi-square test)

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

nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA | RECTAL ADENOCARCINOMA | RECTAL MUCINOUS ADENOCARCINOMA
--- | --- | --- | --- | ---
ALL | 158 | 26 | 56 | 8
subtype1 | 36 | 4 | 11 | 1
subtype2 | 68 | 14 | 25 | 4
subtype3 | 54 | 8 | 20 | 3

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.331 (Chi-square test)

Table S67. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients | T0+T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 11 | 52 | 169 | 21
subtype1 | 2 | 9 | 34 | 9
subtype2 | 5 | 24 | 76 | 8
subtype3 | 4 | 19 | 59 | 4

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0292 (Chi-square test)

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 152 | 55 | 47
subtype1 | 24 | 20 | 9
subtype2 | 71 | 20 | 23
subtype3 | 57 | 15 | 15

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

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

P value = 0.1 (Chi-square test)

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

nPatients | M0 | M1 | M1A
--- | --- | --- | ---
ALL | 208 | 41 | 2
subtype1 | 37 | 14 | 1
subtype2 | 99 | 14 | 0
subtype3 | 72 | 13 | 1

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

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.179 (Chi-square test)

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 52 | 90 | 64 | 38
subtype1 | 9 | 13 | 15 | 12
subtype2 | 26 | 39 | 32 | 13
subtype3 | 17 | 38 | 17 | 13

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

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

P value = 1 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 253 | 2
subtype1 | 54 | 0
subtype2 | 113 | 1
subtype3 | 86 | 1

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

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00452 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 239 | 16
subtype1 | 46 | 8
subtype2 | 107 | 7
subtype3 | 86 | 1

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

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S73. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 80 | 175

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.738 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 141 | 20 | 0.9 - 72.1 (9.1)
subtype1 | 36 | 2 | 0.9 - 35.0 (1.0)
subtype2 | 105 | 18 | 0.9 - 72.1 (13.0)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.472 (t-test)

Table S75. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 255 | 68.6 (12.2)
subtype1 | 80 | 67.8 (12.7)
subtype2 | 175 | 69.0 (12.0)

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

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

P value = 0.76 (Fisher's exact test)

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

nPatients | COLON | RECTUM
--- | --- | ---
ALL | 186 | 67
subtype1 | 57 | 22
subtype2 | 129 | 45

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.42 (Fisher's exact test)

Table S77. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 128 | 127
subtype1 | 37 | 43
subtype2 | 91 | 84

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

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.513 (Chi-square test)

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

nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA | RECTAL ADENOCARCINOMA | RECTAL MUCINOUS ADENOCARCINOMA
--- | --- | --- | --- | ---
ALL | 158 | 26 | 56 | 8
subtype1 | 50 | 6 | 19 | 1
subtype2 | 108 | 20 | 37 | 7

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.208 (Chi-square test)

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

nPatients | T0+T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 11 | 52 | 169 | 21
subtype1 | 5 | 19 | 52 | 3
subtype2 | 6 | 33 | 117 | 18

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.946 (Chi-square test)

Table S80. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 152 | 55 | 47
subtype1 | 49 | 17 | 14
subtype2 | 103 | 38 | 33

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

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

P value = 0.313 (Chi-square test)

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

nPatients | M0 | M1 | M1A
--- | --- | --- | ---
ALL | 208 | 41 | 2
subtype1 | 69 | 9 | 1
subtype2 | 139 | 32 | 1

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

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.692 (Chi-square test)

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 52 | 90 | 64 | 38
subtype1 | 17 | 30 | 22 | 9
subtype2 | 35 | 60 | 42 | 29

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

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

P value = 0.53 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 253 | 2
subtype1 | 79 | 1
subtype2 | 174 | 1

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

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.104 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 239 | 16
subtype1 | 78 | 2
subtype2 | 161 | 14

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

Methods & Data
Input

- Cluster data file = COADREAD.mergedcluster.txt

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

- Number of patients = 585

- Number of clustering approaches = 7

- Number of selected clinical features = 11

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Fisher's exact test

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

Chi-square test

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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[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)
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[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
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