Correlation between aggregated molecular cancer subtypes and selected clinical features

Rectum Adenocarcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

doi:10.7908/C19885Z8

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C19885Z8

Overview

Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 12 clinical features across 169 patients, 5 significant findings 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 8 subtypes that do not correlate to any clinical features.
4 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 correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'NEOPLASM.DISEASESTAGE' and 'PATHOLOGY.M.STAGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
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 'PATHOLOGY.M.STAGE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 5 significant findings detected.


Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES	RACE
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test
mRNA CNMF subtypes	0.923 (1.00)	0.826 (1.00)	0.463 (1.00)	0.131 (1.00)	0.417 (1.00)	0.323 (1.00)	0.952 (1.00)	0.613 (1.00)	0.637 (1.00)	0.417 (1.00)	0.159 (1.00)
mRNA cHierClus subtypes	0.484 (1.00)	0.542 (1.00)	0.124 (1.00)	0.17 (1.00)	0.157 (1.00)	0.253 (1.00)	0.58 (1.00)	0.345 (1.00)	1 (1.00)	0.185 (1.00)	0.213 (1.00)
Copy Number Ratio CNMF subtypes	0.639 (1.00)	0.103 (1.00)	0.143 (1.00)	0.688 (1.00)	0.15 (1.00)	0.809 (1.00)	0.375 (1.00)	0.015 (1.00)	0.69 (1.00)	0.487 (1.00)	0.0432 (1.00)	0.00395 (0.541)
METHLYATION CNMF	0.717 (1.00)	0.106 (1.00)	0.045 (1.00)	0.592 (1.00)	0.746 (1.00)	0.253 (1.00)	0.546 (1.00)	0.279 (1.00)	0.861 (1.00)	0.606 (1.00)	0.992 (1.00)	0.0583 (1.00)
RPPA CNMF subtypes	0.000507 (0.0705)	0.235 (1.00)	0.281 (1.00)	0.0239 (1.00)	0.187 (1.00)	0.0709 (1.00)	0.181 (1.00)	0.274 (1.00)	1 (1.00)	0.354 (1.00)	0.137 (1.00)	0.511 (1.00)
RPPA cHierClus subtypes	0.039 (1.00)	0.144 (1.00)	0.071 (1.00)	0.561 (1.00)	0.17 (1.00)	0.0284 (1.00)	0.266 (1.00)	0.358 (1.00)	0.772 (1.00)	0.0983 (1.00)	0.0615 (1.00)	0.53 (1.00)
RNAseq CNMF subtypes	0.321 (1.00)	0.323 (1.00)	0.00034 (0.0479)	0.496 (1.00)	0.0754 (1.00)	0.00029 (0.0412)	0.808 (1.00)	0.14 (1.00)	0.105 (1.00)	0.149 (1.00)	0.0908 (1.00)	0.0711 (1.00)
RNAseq cHierClus subtypes	0.277 (1.00)	0.275 (1.00)	0.00692 (0.927)	0.804 (1.00)	0.277 (1.00)	0.00449 (0.611)	0.654 (1.00)	0.00083 (0.115)	0.635 (1.00)	0.0326 (1.00)	0.298 (1.00)	0.727 (1.00)
MIRSEQ CNMF	0.306 (1.00)	0.977 (1.00)	0.0072 (0.958)	0.25 (1.00)	0.0234 (1.00)	0.00651 (0.879)	0.479 (1.00)	0.222 (1.00)	0.286 (1.00)	0.264 (1.00)	0.169 (1.00)	0.545 (1.00)
MIRSEQ CHIERARCHICAL	0.651 (1.00)	0.854 (1.00)	0.0213 (1.00)	0.629 (1.00)	0.312 (1.00)	0.00045 (0.063)	0.522 (1.00)	0.186 (1.00)	0.474 (1.00)	0.00828 (1.00)	0.228 (1.00)	0.465 (1.00)
MIRseq Mature CNMF subtypes	0.795 (1.00)	0.772 (1.00)	0.149 (1.00)	0.39 (1.00)	0.0993 (1.00)	0.505 (1.00)	0.413 (1.00)	1 (1.00)	1 (1.00)	0.653 (1.00)	0.786 (1.00)	0.0275 (1.00)
MIRseq Mature cHierClus subtypes	0.459 (1.00)	0.684 (1.00)	0.421 (1.00)	0.4 (1.00)	0.133 (1.00)	0.106 (1.00)	0.0976 (1.00)	1 (1.00)	1 (1.00)	0.12 (1.00)	0.531 (1.00)	0.0198 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	25	22	22

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.923 (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	62	10	1.0 - 52.0 (17.0)
subtype1	24	4	1.0 - 50.0 (16.0)
subtype2	19	2	1.0 - 50.0 (13.0)
subtype3	19	4	1.0 - 52.0 (20.0)

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 = 0.826 (Kruskal-Wallis (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	22	66.8 (10.1)
subtype3	22	68.8 (9.0)

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.463 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE III	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	18	3	20	2	10	4	12
subtype1	8	1	7	2	3	0	4
subtype2	7	1	8	0	2	2	2
subtype3	3	1	5	0	5	2	6

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.131 (Fisher's exact test), Q value = 1

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	5	15	45	4
subtype1	3	6	16	0
subtype2	2	5	15	0
subtype3	0	4	14	4

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.417 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1	N2
ALL	42	15	12
subtype1	16	5	4
subtype2	16	4	2
subtype3	10	6	6

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.323 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1
ALL	57	12
subtype1	21	4
subtype2	20	2
subtype3	16	6

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.952 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.613 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.637 (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	21
subtype3	0	22

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 = 0.417 (Fisher's exact 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	19	0	2
subtype3	16	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 = 0.159 (Kruskal-Wallis (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	69	2.2 (5.1)
subtype1	25	1.4 (2.3)
subtype2	22	2.0 (6.6)
subtype3	22	3.3 (5.8)

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5	6	7	8
Number of samples	6	13	13	9	8	9	8	3

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.484 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	62	10	1.0 - 52.0 (17.0)
subtype1	6	2	8.0 - 36.0 (16.0)
subtype2	9	1	2.0 - 46.0 (32.0)
subtype3	13	0	1.0 - 36.0 (13.6)
subtype4	7	2	1.9 - 48.0 (17.0)
subtype5	8	1	1.0 - 38.0 (30.5)
subtype6	8	1	1.0 - 50.0 (6.6)
subtype7	8	2	1.0 - 39.0 (18.5)
subtype8	3	1	1.0 - 52.0 (50.0)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.542 (Kruskal-Wallis (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	6	66.2 (15.5)
subtype2	13	65.5 (10.8)
subtype3	13	61.6 (13.8)
subtype4	9	66.9 (8.1)
subtype5	8	71.6 (7.3)
subtype6	9	70.4 (8.6)
subtype7	8	68.0 (9.2)
subtype8	3	65.3 (5.8)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.124 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE III	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	18	3	20	2	10	4	12
subtype1	2	1	2	0	0	0	1
subtype2	4	1	6	0	2	0	0
subtype3	5	0	4	1	2	0	1
subtype4	0	1	4	0	1	1	2
subtype5	1	0	1	0	4	0	2
subtype6	2	0	3	0	0	2	2
subtype7	3	0	0	0	1	1	3
subtype8	1	0	0	1	0	0	1

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.17 (Fisher's exact test), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	5	15	45	4
subtype1	1	1	4	0
subtype2	2	2	9	0
subtype3	1	4	8	0
subtype4	0	0	7	2
subtype5	0	2	6	0
subtype6	0	2	7	0
subtype7	0	4	3	1
subtype8	1	0	1	1

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.157 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1	N2
ALL	42	15	12
subtype1	5	0	1
subtype2	11	2	0
subtype3	9	3	1
subtype4	5	2	2
subtype5	3	4	1
subtype6	5	1	3
subtype7	3	3	2
subtype8	1	0	2

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.253 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1
ALL	57	12
subtype1	5	1
subtype2	13	0
subtype3	12	1
subtype4	7	2
subtype5	6	2
subtype6	7	2
subtype7	5	3
subtype8	2	1

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.58 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	31	38
subtype1	4	2
subtype2	5	8
subtype3	6	7
subtype4	3	6
subtype5	4	4
subtype6	3	6
subtype7	3	5
subtype8	3	0

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.345 (Fisher's exact test), Q value = 1

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

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

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

'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	6
subtype2	1	12
subtype3	0	13
subtype4	0	9
subtype5	0	8
subtype6	0	9
subtype7	0	8
subtype8	0	3

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 = 0.185 (Fisher's exact 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	6	0	0
subtype2	12	0	0
subtype3	12	0	1
subtype4	7	1	1
subtype5	6	0	2
subtype6	7	0	2
subtype7	5	0	3
subtype8	2	0	1

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 = 0.213 (Kruskal-Wallis (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	69	2.2 (5.1)
subtype1	6	1.0 (2.4)
subtype2	13	0.4 (1.0)
subtype3	13	0.7 (1.3)
subtype4	9	3.3 (6.7)
subtype5	8	3.6 (6.7)
subtype6	9	5.3 (10.1)
subtype7	8	1.8 (2.0)
subtype8	3	3.3 (3.1)

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

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

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

Cluster Labels	1	2	3	4
Number of samples	66	32	31	36

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

P value = 0.639 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	153	22	0.4 - 129.3 (14.5)
subtype1	62	7	0.5 - 70.0 (13.6)
subtype2	29	3	0.4 - 129.3 (18.3)
subtype3	29	4	1.0 - 126.4 (15.8)
subtype4	33	8	0.5 - 66.0 (14.9)

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 = 0.103 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	165	64.5 (11.8)
subtype1	66	61.9 (12.4)
subtype2	32	66.6 (9.9)
subtype3	31	63.7 (11.7)
subtype4	36	67.9 (11.6)

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 'NEOPLASM.DISEASESTAGE'

P value = 0.143 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	30	8	40	2	2	5	9	23	15	18	9
subtype1	17	4	12	1	0	4	6	6	4	7	4
subtype2	4	1	7	0	1	1	1	8	4	3	2
subtype3	5	1	12	0	0	0	0	7	0	3	2
subtype4	4	2	9	1	1	0	2	2	7	5	1

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

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

P value = 0.688 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	29	112	14
subtype1	5	16	41	4
subtype2	2	3	24	3
subtype3	1	5	22	2
subtype4	1	5	25	5

Figure S26. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.15 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1	N2
ALL	85	45	33
subtype1	37	18	11
subtype2	12	10	10
subtype3	19	9	2
subtype4	17	8	10

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.809 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	M1A	MX
ALL	126	22	2	13
subtype1	52	8	1	4
subtype2	24	3	1	4
subtype3	24	5	0	1
subtype4	26	6	0	4

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.375 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	76	89
subtype1	33	33
subtype2	13	19
subtype3	17	14
subtype4	13	23

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

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

P value = 0.015 (Fisher's exact test), Q value = 1

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

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	146	13
subtype1	54	9
subtype2	30	0
subtype3	31	0
subtype4	31	4

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

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

P value = 0.69 (Fisher's exact 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	159
subtype1	3	63
subtype2	2	30
subtype3	0	31
subtype4	1	35

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 = 0.487 (Fisher's exact 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	123	2	12	4
subtype1	52	1	4	1
subtype2	25	0	1	0
subtype3	23	1	2	1
subtype4	23	0	5	2

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 = 0.0432 (Kruskal-Wallis (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	155	2.6 (5.3)
subtype1	61	1.9 (4.3)
subtype2	31	4.3 (6.7)
subtype3	29	1.0 (1.8)
subtype4	34	3.8 (7.0)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00395 (Fisher's exact test), Q value = 0.54

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	6	81
subtype1	1	0	36
subtype2	0	0	15
subtype3	0	5	13
subtype4	0	1	17

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S38. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	29	39	30

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.717 (logrank test), Q value = 1

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	93	12	0.2 - 129.3 (13.6)
subtype1	29	4	0.2 - 129.3 (15.0)
subtype2	38	5	0.5 - 60.0 (14.0)
subtype3	26	3	0.5 - 117.1 (9.3)

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.106 (Kruskal-Wallis (anova)), Q value = 1

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	98	62.9 (12.3)
subtype1	29	59.8 (9.4)
subtype2	39	63.4 (13.0)
subtype3	30	65.4 (13.5)

Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.045 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	6	20	2	2	4	9	13	11	6	9
subtype1	5	2	5	1	0	0	1	3	4	3	4
subtype2	5	0	7	1	0	1	8	6	5	1	2
subtype3	1	4	8	0	2	3	0	4	2	2	3

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.592 (Fisher's exact test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	4	13	69	11
subtype1	2	4	20	3
subtype2	1	7	24	6
subtype3	1	2	25	2

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

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.746 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	43	30	22
subtype1	13	8	7
subtype2	15	15	8
subtype3	15	7	7

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.253 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	70	10	2	14
subtype1	19	6	1	3
subtype2	28	1	1	7
subtype3	23	3	0	4

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.546 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	44	54
subtype1	12	17
subtype2	16	23
subtype3	16	14

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.279 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	90	6
subtype1	28	1
subtype2	36	1
subtype3	26	4

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.861 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	93
subtype1	2	27
subtype2	2	37
subtype3	1	29

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.606 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	66	2	2	5
subtype1	20	0	0	2
subtype2	26	1	0	1
subtype3	20	1	2	2

Figure S44. 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 = 0.992 (Kruskal-Wallis (anova)), Q value = 1

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	87	3.1 (5.7)
subtype1	25	2.4 (3.3)
subtype2	36	2.8 (4.9)
subtype3	26	4.2 (8.1)

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0583 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	77
subtype1	0	4	23
subtype2	0	1	36
subtype3	1	0	18

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S51. 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 = 0.000507 (logrank test), Q value = 0.07

Table S52. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	120	22	0.2 - 129.3 (15.2)
subtype1	28	8	0.7 - 47.1 (10.0)
subtype2	41	4	0.5 - 117.1 (18.3)
subtype3	51	10	0.2 - 129.3 (15.4)

Figure S47. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.235 (Kruskal-Wallis (anova)), Q value = 1

Table S53. 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	65.2 (10.9)
subtype3	54	64.1 (12.5)

Figure S48. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.281 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	21	7	31	1	1	6	8	19	10	16	5
subtype1	5	2	7	0	0	0	0	4	6	5	3
subtype2	7	4	11	0	1	2	4	7	2	3	0
subtype3	9	1	13	1	0	4	4	8	2	8	2

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0239 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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 S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.187 (Fisher's exact test), Q value = 1

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

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

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0709 (Fisher's exact test), Q value = 1

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

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 S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.181 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

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

Table S60. 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 S55. 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 = 0.354 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	97	2	12	3
subtype1	24	0	5	1
subtype2	36	1	1	1
subtype3	37	1	6	1

Figure S56. 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 = 0.137 (Kruskal-Wallis (anova)), Q value = 1

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	124	2.4 (4.9)
subtype1	32	3.3 (5.8)
subtype2	41	2.1 (5.2)
subtype3	51	2.2 (4.0)

Figure S57. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.511 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	65
subtype1	1	0	14
subtype2	0	1	25
subtype3	0	2	26

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S64. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	25	25	30	6	30	14

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.039 (logrank test), Q value = 1

Table S65. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	120	22	0.2 - 129.3 (15.2)
subtype1	20	6	1.0 - 36.0 (8.2)
subtype2	23	4	0.8 - 117.1 (15.9)
subtype3	28	6	0.2 - 62.0 (13.3)
subtype4	6	1	0.5 - 32.0 (19.0)
subtype5	29	3	0.7 - 70.0 (18.0)
subtype6	14	2	0.4 - 129.3 (12.5)

Figure S59. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.144 (Kruskal-Wallis (anova)), Q value = 1

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	130	65.6 (11.7)
subtype1	25	71.6 (12.0)
subtype2	25	65.3 (13.4)
subtype3	30	64.1 (9.2)
subtype4	6	65.2 (11.9)
subtype5	30	64.3 (10.6)
subtype6	14	61.2 (13.0)

Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.071 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	21	7	31	1	1	6	8	19	10	16	5
subtype1	4	1	4	0	0	2	0	3	3	8	0
subtype2	4	1	4	0	0	1	3	6	2	0	3
subtype3	5	2	6	0	0	2	2	4	0	6	1
subtype4	1	0	1	0	0	0	0	1	3	0	0
subtype5	6	3	10	0	1	1	1	3	1	2	0
subtype6	1	0	6	1	0	0	2	2	1	0	1

Figure S61. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.561 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	5	23	91	10
subtype1	1	3	19	2
subtype2	1	4	16	3
subtype3	2	7	18	3
subtype4	1	0	5	0
subtype5	0	7	23	0
subtype6	0	2	10	2

Figure S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.17 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	64	37	26
subtype1	9	8	8
subtype2	10	8	6
subtype3	14	10	5
subtype4	2	1	3
subtype5	21	5	3
subtype6	8	5	1

Figure S63. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0284 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	M1A	MX
ALL	98	18	2	10
subtype1	17	8	0	0
subtype2	20	1	2	1
subtype3	19	6	0	5
subtype4	6	0	0	0
subtype5	23	2	0	4
subtype6	13	1	0	0

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.266 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	60	70
subtype1	14	11
subtype2	8	17
subtype3	17	13
subtype4	4	2
subtype5	11	19
subtype6	6	8

Figure S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.358 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	117	10
subtype1	23	1
subtype2	21	3
subtype3	25	4
subtype4	5	1
subtype5	29	1
subtype6	14	0

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.772 (Fisher's exact test), Q value = 1

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	125
subtype1	0	25
subtype2	2	23
subtype3	2	28
subtype4	0	6
subtype5	1	29
subtype6	0	14

Figure S67. 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 = 0.0983 (Fisher's exact test), Q value = 1

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	97	2	12	3
subtype1	18	0	7	0
subtype2	21	1	0	1
subtype3	19	1	4	1
subtype4	5	0	0	0
subtype5	24	0	1	1
subtype6	10	0	0	0

Figure S68. 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 = 0.0615 (Kruskal-Wallis (anova)), Q value = 1

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	124	2.4 (4.9)
subtype1	25	3.9 (7.0)
subtype2	24	2.5 (4.0)
subtype3	29	2.0 (3.7)
subtype4	6	2.8 (2.9)
subtype5	28	1.8 (5.4)
subtype6	12	1.8 (3.4)

Figure S69. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.53 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	65
subtype1	0	1	1
subtype2	1	0	16
subtype3	0	1	12
subtype4	0	0	4
subtype5	0	0	19
subtype6	0	1	13

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S77. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	34	30	33	66

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.321 (logrank test), Q value = 1

Table S78. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	151	21	0.2 - 129.3 (14.0)
subtype1	32	5	0.4 - 129.3 (10.1)
subtype2	27	2	0.5 - 55.8 (14.5)
subtype3	32	7	0.2 - 126.4 (14.0)
subtype4	60	7	1.0 - 117.1 (18.5)

Figure S71. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.323 (Kruskal-Wallis (anova)), Q value = 1

Table S79. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	163	64.9 (11.6)
subtype1	34	63.3 (11.3)
subtype2	30	65.2 (12.3)
subtype3	33	62.5 (12.7)
subtype4	66	66.8 (10.7)

Figure S72. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00034 (Fisher's exact test), Q value = 0.048

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	30	8	38	2	2	6	9	23	14	17	9
subtype1	1	2	9	0	0	0	3	3	8	3	4
subtype2	6	0	4	1	1	2	3	5	0	3	3
subtype3	7	0	6	1	1	0	3	6	3	2	2
subtype4	16	6	19	0	0	4	0	9	3	9	0

Figure S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.496 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	29	110	14
subtype1	0	3	27	4
subtype2	1	7	19	3
subtype3	3	7	19	3
subtype4	5	12	45	4

Figure S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0754 (Fisher's exact test), Q value = 1

Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	83	45	32
subtype1	13	8	12
subtype2	13	12	5
subtype3	15	11	5
subtype4	42	14	10

Figure S75. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00029 (Fisher's exact test), Q value = 0.041

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	124	21	2	14
subtype1	25	4	2	2
subtype2	17	4	0	9
subtype3	25	4	0	3
subtype4	57	9	0	0

Figure S76. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.808 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	73	90
subtype1	16	18
subtype2	11	19
subtype3	15	18
subtype4	31	35

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.14 (Fisher's exact test), Q value = 1

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	144	13
subtype1	31	3
subtype2	23	4
subtype3	33	0
subtype4	57	6

Figure S78. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.105 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	6	157
subtype1	3	31
subtype2	2	28
subtype3	0	33
subtype4	1	65

Figure S79. 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 = 0.149 (Fisher's exact test), Q value = 1

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	121	2	12	4
subtype1	26	0	2	1
subtype2	18	1	1	0
subtype3	21	0	1	3
subtype4	56	1	8	0

Figure S80. 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 = 0.0908 (Kruskal-Wallis (anova)), Q value = 1

Table S88. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	152	2.7 (5.5)
subtype1	32	3.6 (5.8)
subtype2	26	3.7 (7.2)
subtype3	28	1.8 (2.4)
subtype4	66	2.2 (5.5)

Figure S81. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0711 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	81
subtype1	1	0	29
subtype2	0	0	23
subtype3	0	2	26
subtype4	0	1	3

Figure S82. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S90. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	56	35	41	31

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.277 (logrank test), Q value = 1

Table S91. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	151	21	0.2 - 129.3 (14.0)
subtype1	54	11	0.2 - 129.3 (13.0)
subtype2	32	1	0.5 - 55.8 (13.5)
subtype3	37	4	1.0 - 62.0 (14.0)
subtype4	28	5	1.0 - 117.1 (20.0)

Figure S83. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.275 (Kruskal-Wallis (anova)), Q value = 1

Table S92. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	163	64.9 (11.6)
subtype1	56	63.1 (12.2)
subtype2	35	64.0 (12.9)
subtype3	41	65.2 (10.7)
subtype4	31	68.8 (9.3)

Figure S84. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00692 (Fisher's exact test), Q value = 0.93

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	30	8	38	2	2	6	9	23	14	17	9
subtype1	8	1	14	1	1	0	4	7	10	3	4
subtype2	4	1	5	1	1	2	5	6	0	3	5
subtype3	11	2	12	0	0	2	0	6	2	6	0
subtype4	7	4	7	0	0	2	0	4	2	5	0

Figure S85. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.804 (Fisher's exact test), Q value = 1

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	29	110	14
subtype1	3	9	37	6
subtype2	1	5	25	4
subtype3	4	8	28	1
subtype4	1	7	20	3

Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.277 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	83	45	32
subtype1	25	14	14
subtype2	14	15	6
subtype3	26	8	7
subtype4	18	8	5

Figure S87. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00449 (Fisher's exact test), Q value = 0.61

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	124	21	2	14
subtype1	42	6	1	5
subtype2	21	4	1	9
subtype3	35	6	0	0
subtype4	26	5	0	0

Figure S88. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.654 (Fisher's exact test), Q value = 1

Table S97. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	73	90
subtype1	22	34
subtype2	18	17
subtype3	20	21
subtype4	13	18

Figure S89. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00083 (Fisher's exact test), Q value = 0.11

Table S98. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	144	13
subtype1	55	1
subtype2	29	4
subtype3	29	8
subtype4	31	0

Figure S90. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.635 (Fisher's exact test), Q value = 1

Table S99. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	6	157
subtype1	3	53
subtype2	2	33
subtype3	1	40
subtype4	0	31

Figure S91. 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 = 0.0326 (Fisher's exact test), Q value = 1

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	121	2	12	4
subtype1	39	0	1	4
subtype2	22	1	1	0
subtype3	35	0	5	0
subtype4	25	1	5	0

Figure S92. 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 = 0.298 (Kruskal-Wallis (anova)), Q value = 1

Table S101. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	152	2.7 (5.5)
subtype1	49	2.3 (3.0)
subtype2	31	4.2 (8.1)
subtype3	41	2.4 (5.9)
subtype4	31	2.2 (4.7)

Figure S93. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.727 (Fisher's exact test), Q value = 1

Table S102. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	81
subtype1	1	2	50
subtype2	0	0	28
subtype3	0	0	3
subtype4	0	1	0

Figure S94. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S103. 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 = 0.306 (logrank test), Q value = 1

Table S104. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	131	21	0.2 - 129.3 (15.0)
subtype1	17	6	0.7 - 129.3 (14.9)
subtype2	38	6	0.5 - 60.0 (14.0)
subtype3	16	1	0.5 - 126.4 (15.0)
subtype4	10	1	0.2 - 117.1 (25.5)
subtype5	50	7	1.0 - 70.0 (16.5)

Figure S95. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.977 (Kruskal-Wallis (anova)), Q value = 1

Table S105. 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 S96. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0072 (Fisher's exact test), Q value = 0.96

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	28	8	33	2	1	6	5	20	13	15	7
subtype1	0	1	4	0	0	0	0	2	5	1	3
subtype2	6	1	6	1	1	2	5	9	4	3	2
subtype3	6	0	4	1	0	0	0	3	0	1	2
subtype4	1	1	3	0	0	1	0	1	0	2	0
subtype5	15	5	16	0	0	3	0	5	4	8	0

Figure S97. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.25 (Fisher's exact test), Q value = 1

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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 S98. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.0234 (Fisher's exact test), Q value = 1

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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 S99. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.00651 (Fisher's exact test), Q value = 0.88

Table S109. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

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 S100. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.479 (Fisher's exact test), Q value = 1

Table S110. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

Figure S101. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.222 (Fisher's exact test), Q value = 1

Table S111. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: '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 S102. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.286 (Fisher's exact test), Q value = 1

Table S112. 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 S103. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.264 (Fisher's exact test), Q value = 1

Table S113. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	102	2	11	4
subtype1	12	0	0	1
subtype2	23	1	1	1
subtype3	13	0	1	1
subtype4	7	0	2	1
subtype5	47	1	7	0

Figure S104. 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 = 0.169 (Kruskal-Wallis (anova)), Q value = 1

Table S114. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	132	2.6 (5.3)
subtype1	17	3.9 (5.7)
subtype2	37	2.8 (5.3)
subtype3	13	1.2 (2.2)
subtype4	9	3.2 (6.3)
subtype5	56	2.3 (5.6)

Figure S105. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.545 (Fisher's exact test), Q value = 1

Table S115. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	67
subtype1	1	1	15
subtype2	0	1	35
subtype3	0	1	10
subtype4	0	0	3
subtype5	0	0	4

Figure S106. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S116. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	24	63	56

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.651 (logrank test), Q value = 1

Table S117. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	131	21	0.2 - 129.3 (15.0)
subtype1	21	3	0.2 - 129.3 (20.9)
subtype2	58	10	0.5 - 126.4 (14.8)
subtype3	52	8	1.0 - 70.0 (15.0)

Figure S107. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.854 (Kruskal-Wallis (anova)), Q value = 1

Table S118. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

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

Figure S108. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0213 (Fisher's exact test), Q value = 1

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	28	8	33	2	1	6	5	20	13	15	7
subtype1	2	1	5	1	0	1	1	5	2	4	1
subtype2	11	2	14	1	1	2	4	10	7	1	6
subtype3	15	5	14	0	0	3	0	5	4	10	0

Figure S109. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.629 (Fisher's exact test), Q value = 1

Table S120. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	9	26	97	10
subtype1	1	2	19	2
subtype2	4	11	41	6
subtype3	4	13	37	2

Figure S110. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.312 (Fisher's exact test), Q value = 1

Table S121. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	74	38	28
subtype1	9	9	5
subtype2	30	18	13
subtype3	35	11	10

Figure S111. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.00045 (Fisher's exact test), Q value = 0.063

Table S122. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	107	18	2	14
subtype1	17	5	0	2
subtype2	44	3	2	12
subtype3	46	10	0	0

Figure S112. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.522 (Fisher's exact test), Q value = 1

Table S123. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

Figure S113. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.186 (Fisher's exact test), Q value = 1

Table S124. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

Figure S114. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.474 (Fisher's exact test), Q value = 1

Table S125. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	6	137
subtype1	1	23
subtype2	4	59
subtype3	1	55

Figure S115. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.00828 (Fisher's exact test), Q value = 1

Table S126. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

Figure S116. 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 = 0.228 (Kruskal-Wallis (anova)), Q value = 1

Table S127. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	132	2.6 (5.3)
subtype1	20	2.9 (4.5)
subtype2	56	2.8 (5.4)
subtype3	56	2.3 (5.6)

Figure S117. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.465 (Fisher's exact test), Q value = 1

Table S128. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	67
subtype1	0	0	13
subtype2	1	2	50
subtype3	0	1	4

Figure S118. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S129. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	9	14	15	13

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.795 (logrank test), Q value = 1

Table S130. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	48	9	0.2 - 129.3 (14.9)
subtype1	9	1	1.0 - 36.0 (13.0)
subtype2	13	3	0.4 - 129.3 (14.9)
subtype3	14	3	0.2 - 55.8 (11.6)
subtype4	12	2	0.7 - 57.2 (16.7)

Figure S119. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.772 (Kruskal-Wallis (anova)), Q value = 1

Table S131. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	51	64.5 (12.0)
subtype1	9	60.7 (10.4)
subtype2	14	65.8 (9.9)
subtype3	15	64.2 (11.6)
subtype4	13	66.3 (15.7)

Figure S120. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.149 (Fisher's exact test), Q value = 1

Table S132. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	6	1	12	1	2	3	7	5	4	6
subtype1	3	0	1	0	0	0	1	1	1	2
subtype2	0	1	6	0	0	0	0	2	1	3
subtype3	1	0	2	1	1	3	3	0	1	0
subtype4	2	0	3	0	1	0	3	2	1	1

Figure S121. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.39 (Fisher's exact test), Q value = 1

Table S133. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	2	7	37	4
subtype1	1	2	5	1
subtype2	0	0	11	2
subtype3	1	3	11	0
subtype4	0	2	10	1

Figure S122. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0993 (Fisher's exact test), Q value = 1

Table S134. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	20	17	12
subtype1	4	3	2
subtype2	7	1	5
subtype3	4	9	1
subtype4	5	4	4

Figure S123. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.505 (Fisher's exact test), Q value = 1

Table S135. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	31	7	2	9
subtype1	6	2	1	0
subtype2	8	2	1	2
subtype3	7	2	0	5
subtype4	10	1	0	2

Figure S124. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.413 (Fisher's exact test), Q value = 1

Table S136. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	20	31
subtype1	3	6
subtype2	8	6
subtype3	4	11
subtype4	5	8

Figure S125. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

Table S137. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	49	1
subtype1	9	0
subtype2	13	0
subtype3	14	1
subtype4	13	0

Figure S126. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

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

Table S138. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	4	47
subtype1	1	8
subtype2	1	13
subtype3	1	14
subtype4	1	12

Figure S127. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.653 (Fisher's exact test), Q value = 1

Table S139. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	31	1	2	2
subtype1	6	0	1	0
subtype2	10	0	1	0
subtype3	6	1	0	1
subtype4	9	0	0	1

Figure S128. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.786 (Kruskal-Wallis (anova)), Q value = 1

Table S140. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	45	3.3 (5.8)
subtype1	8	2.1 (2.9)
subtype2	14	3.4 (5.9)
subtype3	11	3.5 (7.9)
subtype4	12	3.9 (5.2)

Figure S129. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.0275 (Fisher's exact test), Q value = 1

Table S141. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	44
subtype1	2	6
subtype2	0	12
subtype3	0	15
subtype4	0	11

Figure S130. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S142. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	10	13	18	5	5

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.459 (logrank test), Q value = 1

Table S143. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	48	9	0.2 - 129.3 (14.9)
subtype1	9	1	0.2 - 24.1 (6.0)
subtype2	13	2	0.7 - 129.3 (14.5)
subtype3	16	4	0.5 - 55.8 (19.1)
subtype4	5	1	7.1 - 36.0 (13.0)
subtype5	5	1	1.0 - 47.1 (17.0)

Figure S131. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.684 (Kruskal-Wallis (anova)), Q value = 1

Table S144. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	51	64.5 (12.0)
subtype1	10	60.6 (13.6)
subtype2	13	65.5 (12.1)
subtype3	18	66.0 (11.6)
subtype4	5	67.8 (15.2)
subtype5	5	61.4 (7.4)

Figure S132. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.421 (Fisher's exact test), Q value = 1

Table S145. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	6	1	12	1	2	3	7	5	4	6
subtype1	1	0	2	0	0	0	0	1	3	2
subtype2	1	1	4	0	0	0	2	2	0	2
subtype3	1	0	3	1	2	3	4	1	1	1
subtype4	3	0	1	0	0	0	1	0	0	0
subtype5	0	0	2	0	0	0	0	1	0	1

Figure S133. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.4 (Fisher's exact test), Q value = 1

Table S146. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	2	7	37	4
subtype1	0	1	9	0
subtype2	0	1	10	2
subtype3	1	3	13	1
subtype4	1	2	2	0
subtype5	0	0	3	1

Figure S134. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.133 (Fisher's exact test), Q value = 1

Table S147. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	20	17	12
subtype1	3	4	2
subtype2	7	2	4
subtype3	4	10	4
subtype4	4	1	0
subtype5	2	0	2

Figure S135. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.106 (Fisher's exact test), Q value = 1

Table S148. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	MX
ALL	31	7	2	9
subtype1	4	5	0	1
subtype2	9	0	1	3
subtype3	11	2	0	4
subtype4	4	0	0	1
subtype5	3	0	1	0

Figure S136. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0976 (Fisher's exact test), Q value = 1

Table S149. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	20	31
subtype1	3	7
subtype2	9	4
subtype3	4	14
subtype4	2	3
subtype5	2	3

Figure S137. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

Table S150. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	49	1
subtype1	10	0
subtype2	12	0
subtype3	17	1
subtype4	5	0
subtype5	5	0

Figure S138. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

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

Table S151. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	4	47
subtype1	1	9
subtype2	1	12
subtype3	2	16
subtype4	0	5
subtype5	0	5

Figure S139. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.12 (Fisher's exact test), Q value = 1

Table S152. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	31	1	2	2
subtype1	4	0	2	1
subtype2	9	0	0	0
subtype3	11	1	0	0
subtype4	4	0	0	1
subtype5	3	0	0	0

Figure S140. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.531 (Kruskal-Wallis (anova)), Q value = 1

Table S153. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	45	3.3 (5.8)
subtype1	6	2.5 (2.6)
subtype2	13	3.5 (6.1)
subtype3	17	4.4 (7.3)
subtype4	4	0.2 (0.5)
subtype5	5	2.8 (3.8)

Figure S141. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0198 (Fisher's exact test), Q value = 1

Table S154. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	44
subtype1	0	8
subtype2	0	11
subtype3	0	17
subtype4	2	3
subtype5	0	5

Figure S142. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Methods & Data

Input

Cluster data file = READ-TP.mergedcluster.txt
Clinical data file = READ-TP.merged_data.txt
Number of patients = 169
Number of clustering approaches = 12
Number of selected clinical features = 12
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

Download Results

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

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[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] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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