Rectum Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 11 clinical features across 166 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 3 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 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.

- 3 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 3 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that do not correlate to any clinical features.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

- 5 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 correlate to 'PATHOLOGICSPREAD(M)'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.

Clinical Features | Statistical Tests | mRNA CNMF subtypes | mRNA cHierClus subtypes | Copy Number Ratio CNMF subtypes | METHLYATION CNMF | RPPA CNMF subtypes | RPPA cHierClus subtypes | RNAseq CNMF subtypes | RNAseq cHierClus subtypes | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL
Time to Death | logrank test | 0.274 (1.00) | 0.0075 (0.803) | 0.00743 (0.802) | 0.58 (1.00) | 0.438 (1.00) | 0.00276 (0.301) | 100 (1.00) | 100 (1.00) | 0.314 (1.00) | 0.774 (1.00)
AGE | ANOVA | 0.499 (1.00) | 0.394 (1.00) | 0.373 (1.00) | 0.291 (1.00) | 0.237 (1.00) | 0.0107 (1.00) | 0.155 (1.00) | 0.387 (1.00) | 0.892 (1.00) | 0.582 (1.00)
GENDER | Fisher's exact test | 0.953 (1.00) | 0.865 (1.00) | 0.568 (1.00) | 0.494 (1.00) | 0.184 (1.00) | 0.237 (1.00) | 0.107 (1.00) | 0.878 (1.00) | 0.461 (1.00) | 0.973 (1.00)
HISTOLOGICAL TYPE | Fisher's exact test | 0.365 (1.00) | 0.692 (1.00) | 0.0316 (1.00) | 0.274 (1.00) | 0.275 (1.00) | 0.424 (1.00) | 0.334 (1.00) | 0.184 (1.00) | 0.313 (1.00) | 0.037 (1.00)
PATHOLOGY T | Chi-square test | 0.0987 (1.00) | 0.245 (1.00) | 0.564 (1.00) | 0.524 (1.00) | 0.0394 (1.00) | 0.357 (1.00) | 0.506 (1.00) | 0.0694 (1.00) | 0.251 (1.00) | 0.726 (1.00)
PATHOLOGY N | Chi-square test | 0.66 (1.00) | 0.574 (1.00) | 0.104 (1.00) | 0.708 (1.00) | 0.151 (1.00) | 0.0478 (1.00) | 0.874 (1.00) | 0.197 (1.00) | 0.0172 (1.00) | 0.244 (1.00)
PATHOLOGICSPREAD(M) | Chi-square test | 0.407 (1.00) | 0.212 (1.00) | 0.0929 (1.00) | 0.261 (1.00) | 0.0644 (1.00) | 0.0151 (1.00) | 0.442 (1.00) | 0.172 (1.00) | 0.0461 (1.00) | 0.000442 (0.0487)
TUMOR STAGE | Chi-square test | 0.639 (1.00) | 0.504 (1.00) | 0.477 (1.00) | 0.0317 (1.00) | 0.455 (1.00) | 0.163 (1.00) | 0.494 (1.00) | 0.156 (1.00) | 0.117 (1.00) | 0.361 (1.00)
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.29 (1.00) | 1 (1.00) | 0.238 (1.00) | 0.857 (1.00) | 1 (1.00) | 0.453 (1.00) | 0.591 (1.00) | 0.172 (1.00) | 0.283 (1.00) | 0.298 (1.00)
COMPLETENESS OF RESECTION | Chi-square test | 0.534 (1.00) | 0.36 (1.00) | 0.862 (1.00) | 0.361 (1.00) | 0.403 (1.00) | 0.132 (1.00) | 0.221 (1.00) | 0.267 (1.00) | 0.607 (1.00) | 0.0617 (1.00)
NUMBER OF LYMPH NODES | ANOVA | 0.561 (1.00) | 0.546 (1.00) | 0.0886 (1.00) | 0.43 (1.00) | 0.426 (1.00) | 0.334 (1.00) | 0.541 (1.00) | 0.317 (1.00) | 0.752 (1.00) | 0.797 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 25 | 20 | 24

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.274 (logrank test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.499 (ANOVA), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.953 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.365 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0987 (Chi-square test), Q value = 1

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

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

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.66 (Chi-square test), Q value = 1

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

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

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

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

P value = 0.407 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.639 (Chi-square test), Q value = 1

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

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

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

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

P value = 0.29 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.534 (Chi-square test), Q value = 1

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

nPatients | R0 | R1 | R2
--- | --- | --- | ---
ALL | 57 | 1 | 10
subtype1 | 22 | 0 | 3
subtype2 | 17 | 0 | 2
subtype3 | 18 | 1 | 5

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

'mRNA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.561 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 68 | 2.2 (5.2)
subtype1 | 24 | 1.4 (2.4)
subtype2 | 20 | 2.2 (6.9)
subtype3 | 24 | 3.0 (5.6)

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 19 | 24 | 26

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0075 (logrank test), Q value = 0.8

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

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

Figure 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 = 0.394 (ANOVA), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.865 (Fisher's exact test), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.692 (Fisher's exact test), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.245 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

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

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.574 (Chi-square test), Q value = 1

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

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

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

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

P value = 0.212 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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

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

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.504 (Chi-square test), Q value = 1

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

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

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

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

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

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

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

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

'mRNA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.36 (Chi-square test), Q value = 1

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

nPatients | R0 | R1 | R2
--- | --- | --- | ---
ALL | 57 | 1 | 10
subtype1 | 14 | 1 | 4
subtype2 | 20 | 0 | 4
subtype3 | 23 | 0 | 2

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

'mRNA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.546 (ANOVA), Q value = 1

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 68 | 2.2 (5.2)
subtype1 | 19 | 3.3 (6.2)
subtype2 | 23 | 1.6 (2.6)
subtype3 | 26 | 2.0 (6.1)

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

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 7 | 32 | 46 | 55 | 22

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.00743 (logrank test), Q value = 0.8

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

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

Figure 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 'AGE'

P value = 0.373 (ANOVA), Q value = 1

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.568 (Chi-square test), Q value = 1

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0316 (Chi-square test), Q value = 1

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.564 (Chi-square test), Q value = 1

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

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

Figure S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.104 (Chi-square test), Q value = 1

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0929 (Chi-square test), Q value = 1

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.477 (Chi-square test), Q value = 1

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.238 (Chi-square test), Q value = 1

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.862 (Chi-square test), Q value = 1

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

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 121 | 2 | 12 | 3
subtype1 | 5 | 0 | 0 | 0
subtype2 | 24 | 1 | 4 | 0
subtype3 | 32 | 0 | 4 | 2
subtype4 | 43 | 1 | 3 | 1
subtype5 | 17 | 0 | 1 | 0

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0886 (ANOVA), Q value = 1

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 149 | 2.7 (5.4)
subtype1 | 6 | 3.0 (5.0)
subtype2 | 31 | 1.7 (2.3)
subtype3 | 40 | 3.1 (6.3)
subtype4 | 50 | 1.7 (4.5)
subtype5 | 22 | 5.2 (7.7)

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 27 | 38 | 30

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.58 (logrank test), Q value = 1

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 88 | 8 | 0.2 - 121.1 (6.2)
subtype1 | 27 | 2 | 0.2 - 121.1 (6.9)
subtype2 | 35 | 3 | 0.2 - 60.0 (7.4)
subtype3 | 26 | 3 | 0.3 - 72.1 (3.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.291 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 95 | 63.3 (12.3)
subtype1 | 27 | 60.3 (9.4)
subtype2 | 38 | 63.9 (12.8)
subtype3 | 30 | 65.3 (13.6)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.494 (Fisher's exact test), Q value = 1

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

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

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.274 (Fisher's exact test), Q value = 1

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

nPatients | RECTAL ADENOCARCINOMA | RECTAL MUCINOUS ADENOCARCINOMA
--- | --- | ---
ALL | 87 | 6
subtype1 | 26 | 1
subtype2 | 35 | 1
subtype3 | 26 | 4

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.524 (Chi-square test), Q value = 1

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 4 | 13 | 66 | 11
subtype1 | 2 | 4 | 18 | 3
subtype2 | 1 | 7 | 23 | 6
subtype3 | 1 | 2 | 25 | 2

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.708 (Chi-square test), Q value = 1

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

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

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.261 (Chi-square test), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | M1A | MX
--- | --- | --- | --- | ---
ALL | 67 | 10 | 2 | 14
subtype1 | 17 | 6 | 1 | 3
subtype2 | 27 | 1 | 1 | 7
subtype3 | 23 | 3 | 0 | 4

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.0317 (Chi-square test), Q value = 1

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 11 | 28 | 35 | 15
subtype1 | 5 | 7 | 7 | 7
subtype2 | 5 | 7 | 19 | 3
subtype3 | 1 | 14 | 9 | 5

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

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

P value = 0.857 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 90
subtype1 | 2 | 25
subtype2 | 2 | 36
subtype3 | 1 | 29

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

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.361 (Chi-square test), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 64 | 2 | 2 | 3
subtype1 | 19 | 0 | 0 | 0
subtype2 | 25 | 1 | 0 | 1
subtype3 | 20 | 1 | 2 | 2

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

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.43 (ANOVA), Q value = 1

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 82 | 3.2 (5.8)
subtype1 | 23 | 2.2 (3.0)
subtype2 | 34 | 3.0 (4.9)
subtype3 | 25 | 4.4 (8.3)

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 33 | 43 | 54

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.438 (logrank test), Q value = 1

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 102 | 11 | 0.2 - 121.1 (6.2)
subtype1 | 23 | 1 | 0.3 - 47.1 (1.0)
subtype2 | 37 | 3 | 0.2 - 72.1 (7.6)
subtype3 | 42 | 7 | 0.2 - 121.1 (7.3)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.237 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 130 | 65.6 (11.7)
subtype1 | 33 | 68.5 (11.2)
subtype2 | 43 | 65.1 (10.9)
subtype3 | 54 | 64.1 (12.5)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.184 (Fisher's exact test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 60 | 70
subtype1 | 14 | 19
subtype2 | 16 | 27
subtype3 | 30 | 24

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.275 (Fisher's exact test), Q value = 1

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

nPatients | RECTAL ADENOCARCINOMA | RECTAL MUCINOUS ADENOCARCINOMA
--- | --- | ---
ALL | 117 | 10
subtype1 | 28 | 3
subtype2 | 42 | 1
subtype3 | 47 | 6

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0394 (Chi-square test), Q value = 1

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 5 | 23 | 91 | 10
subtype1 | 2 | 3 | 21 | 6
subtype2 | 0 | 9 | 34 | 0
subtype3 | 3 | 11 | 36 | 4

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.151 (Chi-square test), Q value = 1

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 64 | 37 | 26
subtype1 | 14 | 7 | 11
subtype2 | 25 | 11 | 6
subtype3 | 25 | 19 | 9

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

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

P value = 0.0644 (Chi-square test), Q value = 1

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | M1A | MX
--- | --- | --- | --- | ---
ALL | 98 | 18 | 2 | 10
subtype1 | 24 | 6 | 2 | 0
subtype2 | 34 | 3 | 0 | 5
subtype3 | 40 | 9 | 0 | 5

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

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.455 (Chi-square test), Q value = 1

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 21 | 40 | 42 | 19
subtype1 | 5 | 9 | 9 | 7
subtype2 | 7 | 16 | 15 | 2
subtype3 | 9 | 15 | 18 | 10

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

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

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

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

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 125
subtype1 | 1 | 32
subtype2 | 2 | 41
subtype3 | 2 | 52

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

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.403 (Chi-square test), Q value = 1

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

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 97 | 2 | 12 | 2
subtype1 | 24 | 0 | 5 | 1
subtype2 | 36 | 1 | 1 | 1
subtype3 | 37 | 1 | 6 | 0

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

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.426 (ANOVA), Q value = 1

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 122 | 2.5 (4.9)
subtype1 | 30 | 3.5 (6.0)
subtype2 | 41 | 2.1 (5.2)
subtype3 | 51 | 2.2 (4.0)

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

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 6 | 19 | 33 | 26 | 46

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00276 (logrank test), Q value = 0.3

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 102 | 11 | 0.2 - 121.1 (6.2)
subtype1 | 4 | 1 | 0.5 - 24.1 (5.2)
subtype2 | 17 | 0 | 0.2 - 72.1 (5.9)
subtype3 | 28 | 2 | 0.7 - 70.0 (9.0)
subtype4 | 11 | 1 | 0.9 - 15.8 (1.0)
subtype5 | 42 | 7 | 0.2 - 121.1 (6.6)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0107 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 130 | 65.6 (11.7)
subtype1 | 6 | 65.2 (11.9)
subtype2 | 19 | 61.5 (12.4)
subtype3 | 33 | 66.6 (11.2)
subtype4 | 26 | 72.0 (12.0)
subtype5 | 46 | 62.9 (10.4)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.237 (Chi-square test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 60 | 70
subtype1 | 4 | 2
subtype2 | 7 | 12
subtype3 | 11 | 22
subtype4 | 15 | 11
subtype5 | 23 | 23

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.424 (Chi-square test), Q value = 1

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

nPatients | RECTAL ADENOCARCINOMA | RECTAL MUCINOUS ADENOCARCINOMA
--- | --- | ---
ALL | 117 | 10
subtype1 | 5 | 1
subtype2 | 16 | 3
subtype3 | 32 | 1
subtype4 | 23 | 1
subtype5 | 41 | 4

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.357 (Chi-square test), Q value = 1

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 5 | 23 | 91 | 10
subtype1 | 1 | 0 | 5 | 0
subtype2 | 0 | 2 | 14 | 3
subtype3 | 1 | 9 | 23 | 0
subtype4 | 1 | 3 | 20 | 2
subtype5 | 2 | 9 | 29 | 5

Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0478 (Chi-square test), Q value = 1

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 64 | 37 | 26
subtype1 | 2 | 1 | 3
subtype2 | 6 | 7 | 6
subtype3 | 23 | 6 | 3
subtype4 | 10 | 8 | 8
subtype5 | 23 | 15 | 6

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

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

P value = 0.0151 (Chi-square test), Q value = 1

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | M1A | MX
--- | --- | --- | --- | ---
ALL | 98 | 18 | 2 | 10
subtype1 | 6 | 0 | 0 | 0
subtype2 | 15 | 1 | 2 | 1
subtype3 | 26 | 2 | 0 | 4
subtype4 | 18 | 8 | 0 | 0
subtype5 | 33 | 7 | 0 | 5

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

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.163 (Chi-square test), Q value = 1

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 21 | 40 | 42 | 19
subtype1 | 1 | 1 | 4 | 0
subtype2 | 1 | 4 | 10 | 3
subtype3 | 9 | 13 | 7 | 2
subtype4 | 4 | 6 | 8 | 6
subtype5 | 6 | 16 | 13 | 8

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

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

P value = 0.453 (Chi-square test), Q value = 1

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

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 125
subtype1 | 0 | 6
subtype2 | 2 | 17
subtype3 | 1 | 32
subtype4 | 0 | 26
subtype5 | 2 | 44

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

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.132 (Chi-square test), Q value = 1

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

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 97 | 2 | 12 | 2
subtype1 | 5 | 0 | 0 | 0
subtype2 | 17 | 1 | 0 | 1
subtype3 | 27 | 0 | 1 | 1
subtype4 | 18 | 0 | 7 | 0
subtype5 | 30 | 1 | 4 | 0

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

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.334 (ANOVA), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 122 | 2.5 (4.9)
subtype1 | 6 | 2.8 (2.9)
subtype2 | 18 | 3.3 (4.3)
subtype3 | 31 | 1.6 (5.1)
subtype4 | 24 | 4.0 (7.1)
subtype5 | 43 | 1.8 (3.5)

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

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7
--- | --- | --- | --- | --- | --- | --- | ---
Number of samples | 10 | 12 | 7 | 7 | 4 | 7 | 4

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 49 | 2 | 0.2 - 121.1 (5.0)
subtype1 | 10 | 0 | 0.7 - 19.5 (6.1)
subtype2 | 10 | 0 | 1.0 - 47.7 (11.0)
subtype3 | 7 | 0 | 0.2 - 10.0 (5.0)
subtype4 | 7 | 2 | 0.5 - 60.0 (9.0)
subtype5 | 4 | 0 | 0.3 - 1.1 (0.6)
subtype6 | 7 | 0 | 0.7 - 119.5 (4.0)
subtype7 | 4 | 0 | 0.5 - 121.1 (0.7)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.155 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 51 | 63.3 (12.0)
subtype1 | 10 | 63.3 (12.6)
subtype2 | 12 | 64.7 (12.8)
subtype3 | 7 | 61.9 (11.5)
subtype4 | 7 | 72.4 (13.3)
subtype5 | 4 | 54.2 (5.1)
subtype6 | 7 | 56.6 (9.0)
subtype7 | 4 | 67.0 (7.9)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.107 (Chi-square test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 23 | 28
subtype1 | 4 | 6
subtype2 | 6 | 6
subtype3 | 2 | 5
subtype4 | 6 | 1
subtype5 | 1 | 3
subtype6 | 1 | 6
subtype7 | 3 | 1

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.334 (Chi-square test), Q value = 1

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

nPatients | RECTAL ADENOCARCINOMA | RECTAL MUCINOUS ADENOCARCINOMA
--- | --- | ---
ALL | 47 | 2
subtype1 | 10 | 0
subtype2 | 9 | 1
subtype3 | 7 | 0
subtype4 | 7 | 0
subtype5 | 3 | 1
subtype6 | 7 | 0
subtype7 | 4 | 0

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.506 (Chi-square test), Q value = 1

Table S78. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 3 | 10 | 31 | 7
subtype1 | 0 | 1 | 8 | 1
subtype2 | 0 | 4 | 6 | 2
subtype3 | 2 | 2 | 3 | 0
subtype4 | 1 | 1 | 3 | 2
subtype5 | 0 | 1 | 3 | 0
subtype6 | 0 | 1 | 5 | 1
subtype7 | 0 | 0 | 3 | 1

Figure S71. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.874 (Chi-square test), Q value = 1

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 26 | 10 | 13
subtype1 | 3 | 3 | 3
subtype2 | 6 | 3 | 3
subtype3 | 5 | 1 | 0
subtype4 | 3 | 1 | 3
subtype5 | 3 | 0 | 1
subtype6 | 4 | 1 | 2
subtype7 | 2 | 1 | 1

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

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

P value = 0.442 (Chi-square test), Q value = 1

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | M1A | MX
--- | --- | --- | --- | ---
ALL | 37 | 4 | 1 | 9
subtype1 | 6 | 2 | 1 | 1
subtype2 | 7 | 0 | 0 | 5
subtype3 | 6 | 0 | 0 | 1
subtype4 | 4 | 1 | 0 | 2
subtype5 | 4 | 0 | 0 | 0
subtype6 | 6 | 1 | 0 | 0
subtype7 | 4 | 0 | 0 | 0

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

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.494 (Chi-square test), Q value = 1

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 11 | 14 | 16 | 7
subtype1 | 1 | 2 | 2 | 3
subtype2 | 4 | 2 | 4 | 2
subtype3 | 4 | 1 | 1 | 0
subtype4 | 1 | 2 | 3 | 1
subtype5 | 0 | 2 | 2 | 0
subtype6 | 1 | 3 | 2 | 1
subtype7 | 0 | 2 | 2 | 0

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

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

P value = 0.591 (Chi-square test), Q value = 1

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 48
subtype1 | 1 | 9
subtype2 | 2 | 10
subtype3 | 0 | 7
subtype4 | 0 | 7
subtype5 | 0 | 4
subtype6 | 0 | 7
subtype7 | 0 | 4

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

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.221 (Chi-square test), Q value = 1

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

nPatients | R0 | R1 | RX
--- | --- | --- | ---
ALL | 35 | 1 | 3
subtype1 | 8 | 0 | 0
subtype2 | 7 | 1 | 0
subtype3 | 5 | 0 | 0
subtype4 | 3 | 0 | 2
subtype5 | 3 | 0 | 1
subtype6 | 6 | 0 | 0
subtype7 | 3 | 0 | 0

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

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.541 (ANOVA), Q value = 1

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 42 | 3.3 (6.0)
subtype1 | 10 | 2.2 (2.6)
subtype2 | 10 | 6.6 (10.5)
subtype3 | 5 | 0.2 (0.4)
subtype4 | 3 | 4.7 (3.5)
subtype5 | 3 | 2.7 (4.6)
subtype6 | 7 | 3.1 (4.7)
subtype7 | 4 | 1.8 (2.1)

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 19 | 12 | 20

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 49 | 2 | 0.2 - 121.1 (5.0)
subtype1 | 19 | 1 | 0.3 - 121.1 (0.8)
subtype2 | 10 | 0 | 1.0 - 47.7 (11.0)
subtype3 | 20 | 1 | 0.2 - 119.5 (5.0)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.387 (ANOVA), Q value = 1

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 51 | 63.3 (12.0)
subtype1 | 19 | 61.4 (12.4)
subtype2 | 12 | 67.4 (9.4)
subtype3 | 20 | 62.7 (12.8)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.878 (Fisher's exact test), Q value = 1

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 23 | 28
subtype1 | 8 | 11
subtype2 | 5 | 7
subtype3 | 10 | 10

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.184 (Fisher's exact test), Q value = 1

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

nPatients | RECTAL ADENOCARCINOMA | RECTAL MUCINOUS ADENOCARCINOMA
--- | --- | ---
ALL | 47 | 2
subtype1 | 17 | 2
subtype2 | 10 | 0
subtype3 | 20 | 0

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0694 (Chi-square test), Q value = 1

Table S90. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 3 | 10 | 31 | 7
subtype1 | 0 | 1 | 14 | 4
subtype2 | 0 | 5 | 6 | 1
subtype3 | 3 | 4 | 11 | 2

Figure S82. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.197 (Chi-square test), Q value = 1

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

nPatients | N0 | N1 | N2
--- | --- | --- | ---
ALL | 26 | 10 | 13
subtype1 | 6 | 4 | 8
subtype2 | 7 | 3 | 2
subtype3 | 13 | 3 | 3

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

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

P value = 0.172 (Chi-square test), Q value = 1

Table S92. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1 | M1A | MX
--- | --- | --- | --- | ---
ALL | 37 | 4 | 1 | 9
subtype1 | 15 | 2 | 1 | 1
subtype2 | 7 | 0 | 0 | 5
subtype3 | 15 | 2 | 0 | 3

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

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.156 (Chi-square test), Q value = 1

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

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 11 | 14 | 16 | 7
subtype1 | 0 | 6 | 7 | 4
subtype2 | 5 | 2 | 4 | 1
subtype3 | 6 | 6 | 5 | 2

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

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

P value = 0.172 (Fisher's exact test), Q value = 1

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 48
subtype1 | 1 | 18
subtype2 | 2 | 10
subtype3 | 0 | 20

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

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.267 (Chi-square test), Q value = 1

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

nPatients | R0 | R1 | RX
--- | --- | --- | ---
ALL | 35 | 1 | 3
subtype1 | 15 | 0 | 1
subtype2 | 7 | 1 | 0
subtype3 | 13 | 0 | 2

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

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.317 (ANOVA), Q value = 1

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 42 | 3.3 (6.0)
subtype1 | 18 | 4.7 (7.2)
subtype2 | 10 | 3.6 (6.7)
subtype3 | 14 | 1.4 (2.7)

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

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 18 | 42 | 17 | 10 | 56

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.314 (logrank test), Q value = 1

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

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

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.892 (ANOVA), Q value = 1

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

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

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.461 (Chi-square test), Q value = 1

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

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

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.313 (Chi-square test), Q value = 1

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

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

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

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.251 (Chi-square test), Q value = 1

Table S102. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

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

Figure S93. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.0172 (Chi-square test), Q value = 1

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

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

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

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0461 (Chi-square test), Q value = 1

Table S104. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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

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

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.117 (Chi-square test), Q value = 1

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

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

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

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.283 (Chi-square test), Q value = 1

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.607 (Chi-square test), Q value = 1

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 102 | 2 | 11 | 3
subtype1 | 12 | 0 | 0 | 1
subtype2 | 23 | 1 | 1 | 1
subtype3 | 13 | 0 | 1 | 1
subtype4 | 7 | 0 | 2 | 0
subtype5 | 47 | 1 | 7 | 0

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

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.752 (ANOVA), Q value = 1

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 129 | 2.6 (5.3)
subtype1 | 17 | 3.9 (5.7)
subtype2 | 37 | 2.8 (5.3)
subtype3 | 11 | 1.4 (2.4)
subtype4 | 9 | 3.2 (6.3)
subtype5 | 55 | 2.3 (5.6)

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 20 | 64 | 59

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.774 (logrank test), Q value = 1

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.582 (ANOVA), Q value = 1

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.973 (Fisher's exact test), Q value = 1

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.037 (Fisher's exact test), Q value = 1

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.726 (Chi-square test), Q value = 1

Table S114. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

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

Figure S104. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.244 (Chi-square test), Q value = 1

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000442 (Chi-square test), Q value = 0.049

Table S116. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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

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

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.361 (Chi-square test), Q value = 1

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.298 (Fisher's exact test), Q value = 1

Table S118. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0617 (Chi-square test), Q value = 1

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients | R0 | R1 | R2 | RX
--- | --- | --- | --- | ---
ALL | 102 | 2 | 11 | 3
subtype1 | 13 | 0 | 2 | 0
subtype2 | 53 | 1 | 9 | 0
subtype3 | 36 | 1 | 0 | 3

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.797 (ANOVA), Q value = 1

Table S120. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 129 | 2.6 (5.3)
subtype1 | 15 | 1.9 (2.5)
subtype2 | 63 | 2.6 (5.7)
subtype3 | 51 | 3.0 (5.6)

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

Methods & Data
Input

- Cluster data file = READ-TP.mergedcluster.txt

- Clinical data file = READ-TP.clin.merged.picked.txt

- Number of patients = 166

- Number of clustering approaches = 10

- Number of selected clinical features = 11

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Fisher's exact test

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

Chi-square test

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

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

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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] 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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