Correlation between aggregated molecular cancer subtypes and selected clinical features

Colorectal Adenocarcinoma (Primary solid tumor)

15 January 2014 | analyses__2014_01_15

Maintainer Information

Citation Information

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/C18S4ND7

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 599 patients, 23 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE', 'PATHOLOGY.N.STAGE', and 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.M.STAGE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.M.STAGE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.
4 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, 23 significant findings detected.


Clinical Features	Time to Death	AGE	PRIMARY SITE OF DISEASE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Chi-square test	Chi-square test	Chi-square test	Chi-square test	Fisher's exact test	Chi-square test	Fisher's exact test	Chi-square test	ANOVA
mRNA CNMF subtypes	0.0558 (1.00)	0.783 (1.00)	0.0248 (1.00)	0.0121 (1.00)	0.185 (1.00)	0.0247 (1.00)	0.0146 (1.00)	0.0611 (1.00)	7.24e-06 (0.00098)	0.396 (1.00)	0.0186 (1.00)	0.0369 (1.00)
mRNA cHierClus subtypes	0.0773 (1.00)	0.503 (1.00)	0.105 (1.00)	0.0999 (1.00)	0.414 (1.00)	0.234 (1.00)	0.165 (1.00)	0.185 (1.00)	1.91e-05 (0.00256)	1 (1.00)	0.108 (1.00)	0.151 (1.00)
Copy Number Ratio CNMF subtypes	0.964 (1.00)	0.494 (1.00)	7.21e-06 (0.00098)	0.167 (1.00)	0.499 (1.00)	0.00152 (0.185)	0.122 (1.00)	0.398 (1.00)	1.48e-11 (2.13e-09)	0.059 (1.00)	0.0733 (1.00)	0.0672 (1.00)
METHLYATION CNMF	0.972 (1.00)	0.00134 (0.165)	0.0029 (0.348)	0.617 (1.00)	0.409 (1.00)	0.375 (1.00)	0.583 (1.00)	0.0681 (1.00)	0.00408 (0.477)	0.068 (1.00)	0.195 (1.00)	0.445 (1.00)
RPPA CNMF subtypes	0.683 (1.00)	0.894 (1.00)	0.825 (1.00)	0.0427 (1.00)	0.334 (1.00)	0.0697 (1.00)	0.00346 (0.412)	0.714 (1.00)	0.26 (1.00)	0.732 (1.00)	0.04 (1.00)	0.757 (1.00)
RPPA cHierClus subtypes	0.0545 (1.00)	0.457 (1.00)	0.336 (1.00)	0.0152 (1.00)	0.135 (1.00)	0.055 (1.00)	0.00379 (0.447)	0.333 (1.00)	0.18 (1.00)	0.142 (1.00)	0.083 (1.00)	0.0972 (1.00)
RNAseq CNMF subtypes	0.675 (1.00)	0.00633 (0.734)	0.0242 (1.00)	0.0442 (1.00)	0.0587 (1.00)	0.323 (1.00)	6.17e-06 (0.000852)	0.363 (1.00)	0.000516 (0.065)	0.392 (1.00)	0.000125 (0.0165)	0.228 (1.00)
RNAseq cHierClus subtypes	0.939 (1.00)	0.000292 (0.0373)	0.0185 (1.00)	0.00111 (0.139)	0.329 (1.00)	0.0472 (1.00)	1.31e-09 (1.86e-07)	0.477 (1.00)	0.014 (1.00)	0.115 (1.00)	8.33e-10 (1.19e-07)	0.172 (1.00)
MIRSEQ CNMF	0.218 (1.00)	0.00287 (0.348)	0.0393 (1.00)	0.00118 (0.146)	0.0983 (1.00)	0.0161 (1.00)	4.2e-07 (5.84e-05)	0.548 (1.00)	0.000254 (0.0328)	0.0955 (1.00)	7.09e-06 (0.000972)	0.03 (1.00)
MIRSEQ CHIERARCHICAL	0.981 (1.00)	0.000312 (0.0396)	0.0623 (1.00)	4.68e-05 (0.00622)	0.166 (1.00)	0.0546 (1.00)	1.01e-08 (1.42e-06)	0.525 (1.00)	0.0622 (1.00)	0.239 (1.00)	4.92e-08 (6.89e-06)	0.678 (1.00)
MIRseq Mature CNMF subtypes	0.266 (1.00)	0.304 (1.00)	0.000179 (0.0233)	0.12 (1.00)	0.0231 (1.00)	0.0121 (1.00)	0.119 (1.00)	0.831 (1.00)	0.000132 (0.0173)	0.895 (1.00)	0.381 (1.00)	0.0465 (1.00)
MIRseq Mature cHierClus subtypes	0.506 (1.00)	0.764 (1.00)	0.0158 (1.00)	0.381 (1.00)	0.978 (1.00)	0.123 (1.00)	0.144 (1.00)	0.91 (1.00)	0.0699 (1.00)	0.847 (1.00)	0.0156 (1.00)	0.577 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	46	62	72	42

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0558 (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	119	17	0.9 - 52.0 (4.0)
subtype1	26	1	1.0 - 30.0 (1.5)
subtype2	35	12	1.0 - 52.0 (10.9)
subtype3	32	2	0.9 - 49.9 (1.0)
subtype4	26	2	0.9 - 52.0 (13.0)

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 = 0.783 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	222	69.5 (11.4)
subtype1	46	70.7 (11.8)
subtype2	62	68.4 (8.9)
subtype3	72	69.5 (12.4)
subtype4	42	69.6 (12.7)

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

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

P value = 0.0248 (Fisher's exact test), Q value = 1

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

nPatients	COLON	RECTUM
ALL	152	68
subtype1	39	7
subtype2	37	25
subtype3	45	25
subtype4	31	11

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0121 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	47	15	65	5	10	3	22	20	32	1
subtype1	8	3	15	3	3	1	4	2	6	0
subtype2	9	2	14	0	2	2	5	11	16	0
subtype3	19	4	23	0	4	0	7	7	8	0
subtype4	11	6	13	2	1	0	6	0	2	1

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

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

P value = 0.185 (Chi-square test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	9	46	148	19
subtype1	1	9	30	6
subtype2	1	12	41	8
subtype3	5	16	51	0
subtype4	2	9	26	5

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

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

P value = 0.0247 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	136	43	43
subtype1	30	6	10
subtype2	28	17	17
subtype3	46	12	14
subtype4	32	8	2

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

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

P value = 0.0146 (Chi-square test), Q value = 1

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

nPatients	M0	M1	M1A
ALL	186	33	1
subtype1	39	6	0
subtype2	45	17	0
subtype3	63	8	0
subtype4	39	2	1

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0611 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	106	116
subtype1	28	18
subtype2	33	29
subtype3	30	42
subtype4	15	27

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 7.24e-06 (Chi-square test), Q value = 0.00098

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	129	22	58	7
subtype1	28	11	4	2
subtype2	36	1	25	0
subtype3	44	1	21	2
subtype4	21	9	8	3

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

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

P value = 0.396 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	1	221
subtype1	1	45
subtype2	0	62
subtype3	0	72
subtype4	0	42

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

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

P value = 0.0186 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2
ALL	185	2	29
subtype1	38	0	4
subtype2	45	2	15
subtype3	62	0	8
subtype4	40	0	2

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

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

P value = 0.0369 (ANOVA), Q value = 1

Table S13. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	221	2.2 (4.7)
subtype1	46	3.3 (7.0)
subtype2	62	2.9 (5.4)
subtype3	72	1.6 (2.7)
subtype4	41	0.8 (2.2)

Figure S12. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S14. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	45	51	66	60

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0773 (logrank test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	17	0.9 - 52.0 (4.0)
subtype1	13	1	0.9 - 25.7 (1.0)
subtype2	32	3	0.9 - 52.0 (12.0)
subtype3	38	3	1.0 - 41.0 (1.5)
subtype4	36	10	0.9 - 41.0 (10.7)

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.503 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	222	69.5 (11.4)
subtype1	45	69.0 (13.1)
subtype2	51	69.2 (10.1)
subtype3	66	71.2 (12.1)
subtype4	60	68.2 (10.3)

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

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

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

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	152	68
subtype1	32	12
subtype2	32	18
subtype3	52	14
subtype4	36	24

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0999 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	47	15	65	5	10	3	22	20	32	1
subtype1	9	3	17	0	1	0	6	3	6	0
subtype2	15	5	14	0	3	1	4	3	6	0
subtype3	14	5	20	4	5	1	7	2	6	1
subtype4	9	2	14	1	1	1	5	12	14	0

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.414 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	9	46	148	19
subtype1	1	9	34	1
subtype2	4	13	31	3
subtype3	2	14	41	9
subtype4	2	10	42	6

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.234 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	136	43	43
subtype1	29	10	6
subtype2	34	8	9
subtype3	44	12	10
subtype4	29	13	18

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 0.165 (Chi-square test), Q value = 1

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A
ALL	186	33	1
subtype1	38	6	0
subtype2	45	6	0
subtype3	58	6	1
subtype4	45	15	0

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.185 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	106	116
subtype1	21	24
subtype2	18	33
subtype3	34	32
subtype4	33	27

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.91e-05 (Chi-square test), Q value = 0.0026

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	129	22	58	7
subtype1	31	1	11	1
subtype2	30	2	14	2
subtype3	33	18	10	3
subtype4	35	1	23	1

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

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

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	221
subtype1	0	45
subtype2	0	51
subtype3	1	65
subtype4	0	60

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.108 (Chi-square test), Q value = 1

Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	185	2	29
subtype1	37	0	6
subtype2	44	0	7
subtype3	58	0	4
subtype4	46	2	12

Figure S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.151 (ANOVA), Q value = 1

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	221	2.2 (4.7)
subtype1	45	1.2 (2.1)
subtype2	50	1.5 (2.7)
subtype3	66	2.5 (5.9)
subtype4	60	3.0 (5.6)

Figure S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

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

Table S27. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	174	243	156	13

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

P value = 0.964 (logrank test), Q value = 1

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	470	76	0.1 - 140.4 (12.4)
subtype1	137	21	0.1 - 131.5 (13.0)
subtype2	206	33	0.1 - 140.4 (12.7)
subtype3	117	21	0.1 - 130.7 (12.0)
subtype4	10	1	0.4 - 47.1 (13.1)

Figure S25. 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 = 0.494 (ANOVA), Q value = 1

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	585	66.5 (12.7)
subtype1	174	67.0 (10.9)
subtype2	242	66.7 (14.1)
subtype3	156	66.0 (12.2)
subtype4	13	61.8 (11.0)

Figure S26. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 7.21e-06 (Fisher's exact test), Q value = 0.00098

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	423	159
subtype1	109	61
subtype2	203	40
subtype3	102	54
subtype4	9	4

Figure S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.167 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	101	1	41	157	10	3	27	22	75	50	59	25	1
subtype1	23	0	6	40	3	1	8	7	34	15	23	10	0
subtype2	47	1	21	78	6	0	10	8	26	17	13	8	1
subtype3	29	0	13	35	1	2	8	6	15	17	22	6	0
subtype4	2	0	1	4	0	0	1	1	0	1	1	1	0

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.499 (Chi-square test), Q value = 1

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	20	104	399	61
subtype1	8	23	122	21
subtype2	9	44	163	26
subtype3	3	33	107	13
subtype4	0	4	7	1

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.00152 (Chi-square test), Q value = 0.18

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	331	146	106
subtype1	80	60	33
subtype2	161	47	35
subtype3	83	36	36
subtype4	7	3	2

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 0.122 (Chi-square test), Q value = 1

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	441	72	9	1	54
subtype1	123	30	3	0	16
subtype2	194	17	2	1	24
subtype3	115	24	3	0	13
subtype4	9	1	1	0	1

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.398 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	272	314
subtype1	75	99
subtype2	122	121
subtype3	68	88
subtype4	7	6

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'GENDER'

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

P value = 1.48e-11 (Chi-square test), Q value = 2.1e-09

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	367	55	143	13
subtype1	106	3	61	0
subtype2	158	45	30	9
subtype3	94	7	48	4
subtype4	9	0	4	0

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.059 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	577
subtype1	3	171
subtype2	1	242
subtype3	4	152
subtype4	1	12

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0733 (Chi-square test), Q value = 1

Table S38. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	418	5	36	27
subtype1	123	1	15	5
subtype2	181	3	5	15
subtype3	104	1	15	6
subtype4	10	0	1	1

Figure S35. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0672 (ANOVA), Q value = 1

Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	552	2.2 (4.7)
subtype1	166	2.2 (3.5)
subtype2	228	1.7 (4.0)
subtype3	146	3.0 (6.6)
subtype4	12	1.4 (2.3)

Figure S36. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'METHLYATION CNMF'

Table S40. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	158	115	91

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.972 (logrank test), Q value = 1

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	348	59	0.1 - 140.4 (13.0)
subtype1	152	27	0.1 - 139.2 (14.0)
subtype2	108	20	0.1 - 140.4 (13.8)
subtype3	88	12	0.1 - 102.4 (12.0)

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00134 (ANOVA), Q value = 0.16

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	363	64.6 (12.9)
subtype1	158	65.1 (12.3)
subtype2	115	67.1 (12.8)
subtype3	90	60.6 (13.2)

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.0029 (Fisher's exact test), Q value = 0.35

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	269	93
subtype1	106	51
subtype2	98	17
subtype3	65	25

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.617 (Chi-square test), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	51	1	25	98	7	3	12	19	53	31	26	24	1
subtype1	19	0	9	41	2	2	6	10	25	12	12	13	0
subtype2	22	1	8	33	4	1	5	2	15	8	8	5	1
subtype3	10	0	8	24	1	0	1	7	13	11	6	6	0

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

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

P value = 0.409 (Chi-square test), Q value = 1

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	54	253	44
subtype1	4	24	112	17
subtype2	4	22	76	12
subtype3	3	8	65	15

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.375 (Chi-square test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	199	99	62
subtype1	81	48	25
subtype2	71	24	20
subtype3	47	27	17

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 0.583 (Chi-square test), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	255	38	8	1	55
subtype1	104	20	5	0	27
subtype2	85	10	1	1	15
subtype3	66	8	2	0	13

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0681 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	163	201
subtype1	68	90
subtype2	61	54
subtype3	34	57

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00408 (Chi-square test), Q value = 0.48

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	236	33	87	6
subtype1	100	6	48	3
subtype2	83	15	15	2
subtype3	53	12	24	1

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.068 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	8	356
subtype1	4	154
subtype2	0	115
subtype3	4	87

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.195 (Chi-square test), Q value = 1

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	232	4	6	27
subtype1	101	1	2	10
subtype2	69	2	4	13
subtype3	62	1	0	4

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.445 (ANOVA), Q value = 1

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	330	2.2 (4.8)
subtype1	145	2.2 (5.2)
subtype2	103	1.9 (3.8)
subtype3	82	2.8 (5.3)

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S53. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	100	179	42	140

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.683 (logrank test), Q value = 1

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	380	63	0.1 - 140.4 (12.9)
subtype1	77	10	0.2 - 91.8 (9.1)
subtype2	142	27	0.1 - 140.4 (13.6)
subtype3	35	3	0.1 - 61.9 (15.0)
subtype4	126	23	0.1 - 135.7 (13.0)

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.894 (ANOVA), Q value = 1

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	460	66.8 (12.7)
subtype1	100	67.0 (12.7)
subtype2	178	67.1 (12.5)
subtype3	42	67.3 (10.6)
subtype4	140	66.1 (13.5)

Figure S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.825 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	331	130
subtype1	70	30
subtype2	126	53
subtype3	32	10
subtype4	103	37

Figure S51. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0427 (Chi-square test), Q value = 1

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	70	1	32	134	9	2	25	16	61	39	43	19	1
subtype1	10	0	11	23	3	0	3	4	18	10	9	7	0
subtype2	27	0	12	43	4	1	12	7	26	10	26	7	1
subtype3	8	1	3	17	0	0	2	0	3	5	1	1	0
subtype4	25	0	6	51	2	1	8	5	14	14	7	4	0

Figure S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

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

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	76	324	48
subtype1	0	13	72	14
subtype2	6	31	123	19
subtype3	0	8	32	1
subtype4	5	24	97	14

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.0697 (Chi-square test), Q value = 1

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	260	117	81
subtype1	47	33	19
subtype2	95	50	34
subtype3	29	6	6
subtype4	89	28	22

Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 0.00346 (Chi-square test), Q value = 0.41

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	357	54	8	1	36
subtype1	78	12	5	0	3
subtype2	130	32	0	1	15
subtype3	37	2	0	0	3
subtype4	112	8	3	0	15

Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.714 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	215	246
subtype1	42	58
subtype2	88	91
subtype3	19	23
subtype4	66	74

Figure S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.26 (Chi-square test), Q value = 1

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	293	38	117	10
subtype1	61	9	24	4
subtype2	119	7	48	4
subtype3	27	5	9	1
subtype4	86	17	36	1

Figure S57. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.732 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	8	453
subtype1	3	97
subtype2	3	176
subtype3	0	42
subtype4	2	138

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.04 (Chi-square test), Q value = 1

Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	336	2	28	19
subtype1	73	0	7	7
subtype2	113	1	18	6
subtype3	38	0	1	1
subtype4	112	1	2	5

Figure S59. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.757 (ANOVA), Q value = 1

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	435	2.1 (4.6)
subtype1	93	2.3 (3.8)
subtype2	169	2.3 (4.4)
subtype3	41	1.5 (3.1)
subtype4	132	2.0 (5.6)

Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S66. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	79	250	94	38

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0545 (logrank test), Q value = 1

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	380	63	0.1 - 140.4 (12.9)
subtype1	72	8	0.1 - 130.7 (10.9)
subtype2	195	31	0.1 - 140.4 (13.3)
subtype3	85	14	0.1 - 135.7 (13.0)
subtype4	28	10	0.5 - 107.7 (14.0)

Figure S61. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.457 (ANOVA), Q value = 1

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	460	66.8 (12.7)
subtype1	79	66.7 (11.6)
subtype2	249	67.5 (12.7)
subtype3	94	65.1 (13.4)
subtype4	38	66.4 (12.7)

Figure S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.336 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	331	130
subtype1	57	22
subtype2	177	73
subtype3	65	29
subtype4	32	6

Figure S63. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0152 (Chi-square test), Q value = 1

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	70	1	32	134	9	2	25	16	61	39	43	19	1
subtype1	12	0	2	23	1	0	1	4	14	8	4	9	0
subtype2	34	1	22	63	6	1	19	8	33	17	33	7	1
subtype3	22	0	5	33	1	1	4	4	8	7	4	3	0
subtype4	2	0	3	15	1	0	1	0	6	7	2	0	0

Figure S64. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.135 (Chi-square test), Q value = 1

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	76	324	48
subtype1	0	13	54	11
subtype2	6	40	177	26
subtype3	4	22	61	7
subtype4	1	1	32	4

Figure S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.055 (Chi-square test), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	260	117	81
subtype1	40	20	18
subtype2	133	73	43
subtype3	66	16	11
subtype4	21	8	9

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

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

P value = 0.00379 (Chi-square test), Q value = 0.45

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	357	54	8	1	36
subtype1	60	8	5	0	4
subtype2	189	40	0	1	20
subtype3	76	4	3	0	10
subtype4	32	2	0	0	2

Figure S67. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.333 (Fisher's exact test), Q value = 1

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	215	246
subtype1	37	42
subtype2	118	132
subtype3	38	56
subtype4	22	16

Figure S68. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.18 (Chi-square test), Q value = 1

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	293	38	117	10
subtype1	47	10	19	3
subtype2	163	14	64	6
subtype3	56	9	29	0
subtype4	27	5	5	1

Figure S69. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.142 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	8	453
subtype1	4	75
subtype2	3	247
subtype3	1	93
subtype4	0	38

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.083 (Chi-square test), Q value = 1

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	336	2	28	19
subtype1	62	1	1	3
subtype2	169	1	23	11
subtype3	77	0	1	3
subtype4	28	0	3	2

Figure S71. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0972 (ANOVA), Q value = 1

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	435	2.1 (4.6)
subtype1	74	3.1 (6.9)
subtype2	236	2.1 (4.1)
subtype3	89	1.3 (3.7)
subtype4	36	2.0 (3.2)

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S79. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	121	125	131	194

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.675 (logrank test), Q value = 1

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	453	72	0.1 - 140.4 (12.1)
subtype1	115	20	0.1 - 131.5 (11.1)
subtype2	101	16	0.5 - 135.7 (14.0)
subtype3	118	23	0.1 - 140.4 (13.4)
subtype4	119	13	0.9 - 83.8 (10.3)

Figure S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00633 (ANOVA), Q value = 0.73

Table S81. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	570	66.5 (12.8)
subtype1	121	65.4 (13.1)
subtype2	124	66.8 (13.8)
subtype3	131	63.9 (12.3)
subtype4	194	68.7 (11.9)

Figure S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0242 (Fisher's exact test), Q value = 1

Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	414	153
subtype1	95	26
subtype2	99	24
subtype3	88	43
subtype4	132	60

Figure S75. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0442 (Chi-square test), Q value = 1

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	96	1	39	157	11	3	28	20	72	49	57	22	1
subtype1	10	0	8	40	2	0	2	5	14	17	13	6	0
subtype2	26	1	11	35	2	1	8	5	15	4	7	7	1
subtype3	22	0	8	29	2	2	5	5	21	10	12	8	0
subtype4	38	0	12	53	5	0	13	5	22	18	25	1	0

Figure S76. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0587 (Chi-square test), Q value = 1

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	19	99	388	63
subtype1	1	11	89	20
subtype2	3	27	82	12
subtype3	6	24	85	15
subtype4	9	37	132	16

Figure S77. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.323 (Chi-square test), Q value = 1

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	324	139	104
subtype1	61	29	30
subtype2	79	29	17
subtype3	74	34	20
subtype4	110	47	37

Figure S78. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 6.17e-06 (Chi-square test), Q value = 0.00085

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	431	68	8	1	54
subtype1	86	16	2	0	14
subtype2	93	11	0	1	19
subtype3	88	15	5	0	21
subtype4	164	26	1	0	0

Figure S79. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.363 (Fisher's exact test), Q value = 1

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	269	302
subtype1	50	71
subtype2	58	67
subtype3	61	70
subtype4	100	94

Figure S80. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000516 (Chi-square test), Q value = 0.065

Table S88. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	359	54	138	12
subtype1	79	16	23	3
subtype2	79	19	19	4
subtype3	86	2	43	0
subtype4	115	17	53	5

Figure S81. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.392 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	562
subtype1	3	118
subtype2	3	122
subtype3	2	129
subtype4	1	193

Figure S82. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.000125 (Chi-square test), Q value = 0.017

Table S90. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	409	5	34	28
subtype1	88	1	2	9
subtype2	81	1	3	9
subtype3	75	2	6	10
subtype4	165	1	23	0

Figure S83. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.228 (ANOVA), Q value = 1

Table S91. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	536	2.2 (4.8)
subtype1	111	3.1 (6.6)
subtype2	116	1.9 (4.2)
subtype3	117	2.0 (4.0)
subtype4	192	2.2 (4.4)

Figure S84. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S92. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	244	140	187

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.939 (logrank test), Q value = 1

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	453	72	0.1 - 140.4 (12.1)
subtype1	142	19	0.9 - 83.8 (10.3)
subtype2	130	23	0.1 - 135.7 (13.3)
subtype3	181	30	0.1 - 140.4 (12.7)

Figure S85. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000292 (ANOVA), Q value = 0.037

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	570	66.5 (12.8)
subtype1	244	68.9 (11.9)
subtype2	139	65.1 (13.8)
subtype3	187	64.3 (12.5)

Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0185 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	414	153
subtype1	171	71
subtype2	114	25
subtype3	129	57

Figure S87. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00111 (Chi-square test), Q value = 0.14

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	96	1	39	157	11	3	28	20	72	49	57	22	1
subtype1	49	0	19	67	6	0	16	5	25	20	34	1	0
subtype2	25	1	11	38	3	1	8	6	20	8	8	6	1
subtype3	22	0	9	52	2	2	4	9	27	21	15	15	0

Figure S88. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.329 (Chi-square test), Q value = 1

Table S97. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	19	99	388	63
subtype1	10	49	164	21
subtype2	5	24	95	15
subtype3	4	26	129	27

Figure S89. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.0472 (Chi-square test), Q value = 1

Table S98. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	324	139	104
subtype1	146	51	47
subtype2	85	39	16
subtype3	93	49	41

Figure S90. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 1.31e-09 (Chi-square test), Q value = 1.9e-07

Table S99. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	431	68	8	1	54
subtype1	205	35	1	0	0
subtype2	99	11	1	1	25
subtype3	127	22	6	0	29

Figure S91. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.477 (Fisher's exact test), Q value = 1

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	269	302
subtype1	122	122
subtype2	62	78
subtype3	85	102

Figure S92. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.014 (Chi-square test), Q value = 1

Table S101. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	359	54	138	12
subtype1	146	24	60	8
subtype2	94	20	23	2
subtype3	119	10	55	2

Figure S93. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.115 (Fisher's exact test), Q value = 1

Table S102. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	562
subtype1	1	243
subtype2	3	137
subtype3	5	182

Figure S94. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 8.33e-10 (Chi-square test), Q value = 1.2e-07

Table S103. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	409	5	34	28
subtype1	206	2	30	0
subtype2	83	3	1	13
subtype3	120	0	3	15

Figure S95. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.172 (ANOVA), Q value = 1

Table S104. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	536	2.2 (4.8)
subtype1	242	2.2 (4.7)
subtype2	125	1.7 (3.8)
subtype3	169	2.7 (5.6)

Figure S96. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S105. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	212	96	241

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.218 (logrank test), Q value = 1

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	429	72	0.1 - 140.4 (12.1)
subtype1	201	37	0.1 - 140.4 (14.0)
subtype2	93	16	0.1 - 100.0 (10.3)
subtype3	135	19	0.9 - 83.8 (8.0)

Figure S97. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00287 (ANOVA), Q value = 0.35

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	548	66.8 (12.7)
subtype1	211	65.9 (12.6)
subtype2	96	64.0 (13.8)
subtype3	241	68.8 (12.1)

Figure S98. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.0393 (Fisher's exact test), Q value = 1

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	405	140
subtype1	150	61
subtype2	80	15
subtype3	175	64

Figure S99. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00118 (Chi-square test), Q value = 0.15

Table S109. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	94	1	41	148	10	2	28	17	68	45	56	23	1
subtype1	32	1	10	64	4	2	9	10	32	13	16	11	1
subtype2	9	0	8	23	0	0	3	3	15	14	7	8	0
subtype3	53	0	23	61	6	0	16	4	21	18	33	4	0

Figure S100. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0983 (Chi-square test), Q value = 1

Table S110. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	20	95	376	56
subtype1	8	33	151	18
subtype2	2	10	69	15
subtype3	10	52	156	23

Figure S101. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.0161 (Chi-square test), Q value = 1

Table S111. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	313	133	99
subtype1	121	58	30
subtype2	43	29	23
subtype3	149	46	46

Figure S102. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 4.2e-07 (Chi-square test), Q value = 5.8e-05

Table S112. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	412	68	9	1	50
subtype1	149	19	6	1	33
subtype2	65	12	2	0	15
subtype3	198	37	1	0	2

Figure S103. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.548 (Fisher's exact test), Q value = 1

Table S113. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	257	292
subtype1	102	110
subtype2	40	56
subtype3	115	126

Figure S104. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.000254 (Chi-square test), Q value = 0.033

Table S114. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	351	53	127	10
subtype1	140	10	60	1
subtype2	61	19	13	2
subtype3	150	24	54	7

Figure S105. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.0955 (Fisher's exact test), Q value = 1

Table S115. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	540
subtype1	3	209
subtype2	4	92
subtype3	2	239

Figure S106. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 7.09e-06 (Chi-square test), Q value = 0.00097

Table S116. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	382	5	36	26
subtype1	119	2	5	17
subtype2	66	0	2	7
subtype3	197	3	29	2

Figure S107. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.03 (ANOVA), Q value = 1

Table S117. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	514	2.2 (4.7)
subtype1	189	1.8 (3.5)
subtype2	89	3.3 (7.1)
subtype3	236	2.1 (4.4)

Figure S108. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S118. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	38	253	258

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.981 (logrank test), Q value = 1

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	429	72	0.1 - 140.4 (12.1)
subtype1	33	5	0.1 - 131.5 (6.0)
subtype2	241	43	0.1 - 140.4 (13.4)
subtype3	155	24	0.5 - 119.7 (10.6)

Figure S109. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000312 (ANOVA), Q value = 0.04

Table S120. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	548	66.8 (12.7)
subtype1	38	60.9 (13.5)
subtype2	252	65.8 (12.9)
subtype3	258	68.8 (12.0)

Figure S110. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.0623 (Fisher's exact test), Q value = 1

Table S121. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	405	140
subtype1	22	16
subtype2	191	60
subtype3	192	64

Figure S111. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 4.68e-05 (Chi-square test), Q value = 0.0062

Table S122. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	94	1	41	148	10	2	28	17	68	45	56	23	1
subtype1	3	1	2	6	0	0	0	2	6	4	8	3	0
subtype2	34	0	16	77	4	2	10	11	35	24	14	15	1
subtype3	57	0	23	65	6	0	18	4	27	17	34	5	0

Figure S112. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.166 (Chi-square test), Q value = 1

Table S123. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	20	95	376	56
subtype1	1	3	29	4
subtype2	5	40	179	28
subtype3	14	52	168	24

Figure S113. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.0546 (Chi-square test), Q value = 1

Table S124. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	313	133	99
subtype1	13	13	11
subtype2	144	64	42
subtype3	156	56	46

Figure S114. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 1.01e-08 (Chi-square test), Q value = 1.4e-06

Table S125. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	412	68	9	1	50
subtype1	21	10	1	0	5
subtype2	179	20	6	1	42
subtype3	212	38	2	0	3

Figure S115. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.525 (Fisher's exact test), Q value = 1

Table S126. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	257	292
subtype1	18	20
subtype2	112	141
subtype3	127	131

Figure S116. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.0622 (Chi-square test), Q value = 1

Table S127. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	351	53	127	10
subtype1	20	2	15	1
subtype2	166	25	59	1
subtype3	165	26	53	8

Figure S117. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.239 (Fisher's exact test), Q value = 1

Table S128. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	540
subtype1	1	37
subtype2	6	247
subtype3	2	256

Figure S118. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 4.92e-08 (Chi-square test), Q value = 6.9e-06

Table S129. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	382	5	36	26
subtype1	19	1	5	1
subtype2	153	2	3	23
subtype3	210	2	28	2

Figure S119. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.678 (ANOVA), Q value = 1

Table S130. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	514	2.2 (4.7)
subtype1	33	2.9 (3.7)
subtype2	228	2.2 (5.0)
subtype3	253	2.1 (4.6)

Figure S120. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S131. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	101	109	85

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

P value = 0.266 (logrank test), Q value = 1

Table S132. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	282	49	0.1 - 140.4 (13.0)
subtype1	93	18	0.1 - 140.4 (13.5)
subtype2	105	17	0.1 - 135.7 (14.0)
subtype3	84	14	0.2 - 129.3 (10.6)

Figure S121. 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 = 0.304 (ANOVA), Q value = 1

Table S133. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	294	65.2 (13.0)
subtype1	101	65.3 (12.7)
subtype2	108	66.4 (13.8)
subtype3	85	63.5 (12.2)

Figure S122. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.000179 (Fisher's exact test), Q value = 0.023

Table S134. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	220	73
subtype1	62	39
subtype2	93	15
subtype3	65	19

Figure S123. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.12 (Chi-square test), Q value = 1

Table S135. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	41	1	17	85	5	2	10	13	43	27	19	18	1
subtype1	16	0	3	28	2	0	5	7	13	11	6	5	0
subtype2	19	1	8	32	2	2	5	4	18	4	5	4	1
subtype3	6	0	6	25	1	0	0	2	12	12	8	9	0

Figure S124. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0231 (Chi-square test), Q value = 1

Table S136. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	9	43	208	33
subtype1	6	16	69	10
subtype2	2	22	75	9
subtype3	1	5	64	14

Figure S125. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.0121 (Chi-square test), Q value = 1

Table S137. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	162	78	51
subtype1	53	28	19
subtype2	69	30	9
subtype3	40	20	23

Figure S126. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 0.119 (Chi-square test), Q value = 1

Table S138. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	204	27	7	1	50
subtype1	76	7	3	0	14
subtype2	73	7	1	1	25
subtype3	55	13	3	0	11

Figure S127. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.831 (Fisher's exact test), Q value = 1

Table S139. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	130	165
subtype1	42	59
subtype2	49	60
subtype3	39	46

Figure S128. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GENDER'

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

P value = 0.000132 (Chi-square test), Q value = 0.017

Table S140. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	191	29	71	2
subtype1	61	1	38	1
subtype2	76	17	15	0
subtype3	54	11	18	1

Figure S129. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.895 (Fisher's exact test), Q value = 1

Table S141. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	288
subtype1	3	98
subtype2	2	107
subtype3	2	83

Figure S130. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.381 (Chi-square test), Q value = 1

Table S142. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	173	2	4	24
subtype1	54	0	2	9
subtype2	63	2	1	11
subtype3	56	0	1	4

Figure S131. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0465 (ANOVA), Q value = 1

Table S143. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	264	2.2 (4.8)
subtype1	88	2.1 (3.5)
subtype2	97	1.4 (3.5)
subtype3	79	3.2 (6.9)

Figure S132. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S144. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	34	105	115	41

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

P value = 0.506 (logrank test), Q value = 1

Table S145. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	282	49	0.1 - 140.4 (13.0)
subtype1	32	3	0.1 - 139.2 (13.8)
subtype2	103	16	0.1 - 129.3 (11.0)
subtype3	108	23	0.1 - 140.4 (14.0)
subtype4	39	7	0.2 - 131.5 (15.0)

Figure S133. 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 = 0.764 (ANOVA), Q value = 1

Table S146. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	294	65.2 (13.0)
subtype1	34	65.5 (12.2)
subtype2	105	64.3 (12.8)
subtype3	114	66.1 (13.5)
subtype4	41	64.8 (12.9)

Figure S134. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0158 (Fisher's exact test), Q value = 1

Table S147. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	220	73
subtype1	21	13
subtype2	85	19
subtype3	89	25
subtype4	25	16

Figure S135. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.381 (Chi-square test), Q value = 1

Table S148. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	41	1	17	85	5	2	10	13	43	27	19	18	1
subtype1	5	0	2	8	1	0	2	1	6	3	4	1	0
subtype2	15	1	9	28	1	0	2	3	16	13	4	8	0
subtype3	15	0	4	43	3	1	6	7	15	7	5	4	1
subtype4	6	0	2	6	0	1	0	2	6	4	6	5	0

Figure S136. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.978 (Chi-square test), Q value = 1

Table S149. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	9	43	208	33
subtype1	1	5	24	4
subtype2	2	15	74	13
subtype3	4	18	83	10
subtype4	2	5	27	6

Figure S137. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

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

P value = 0.123 (Chi-square test), Q value = 1

Table S150. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	162	78	51
subtype1	18	10	5
subtype2	57	27	20
subtype3	72	28	14
subtype4	15	13	12

Figure S138. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

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

P value = 0.144 (Chi-square test), Q value = 1

Table S151. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	204	27	7	1	50
subtype1	23	4	0	0	7
subtype2	73	8	3	0	20
subtype3	85	7	1	1	18
subtype4	23	8	3	0	5

Figure S139. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.91 (Fisher's exact test), Q value = 1

Table S152. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	130	165
subtype1	15	19
subtype2	49	56
subtype3	48	67
subtype4	18	23

Figure S140. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

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

P value = 0.0699 (Chi-square test), Q value = 1

Table S153. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	191	29	71	2
subtype1	19	2	13	0
subtype2	70	15	18	1
subtype3	79	10	25	0
subtype4	23	2	15	1

Figure S141. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

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

P value = 0.847 (Fisher's exact test), Q value = 1

Table S154. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	288
subtype1	1	33
subtype2	3	102
subtype3	3	112
subtype4	0	41

Figure S142. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0156 (Chi-square test), Q value = 1

Table S155. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	173	2	4	24
subtype1	21	0	0	5
subtype2	73	0	0	8
subtype3	58	2	1	9
subtype4	21	0	3	2

Figure S143. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.577 (ANOVA), Q value = 1

Table S156. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	264	2.2 (4.8)
subtype1	26	3.2 (9.9)
subtype2	96	2.3 (4.1)
subtype3	106	1.8 (4.0)
subtype4	36	2.5 (3.1)

Figure S144. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Cluster data file = COADREAD-TP.mergedcluster.txt
Clinical data file = COADREAD-TP.merged_data.txt
Number of patients = 599
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

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

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] 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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