Correlation between aggregated molecular cancer subtypes and selected clinical features

Colon Adenocarcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_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/C15Q4TSM

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 439 patients, 19 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 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.M.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 6 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'AGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE', 'PATHOLOGY.M.STAGE', and 'COMPLETENESS.OF.RESECTION'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE', 'PATHOLOGY.M.STAGE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to '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, 19 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	0.755 (1.00)	0.597 (1.00)	0.00283 (0.34)	0.683 (1.00)	0.00855 (0.958)	0.0327 (1.00)	0.0357 (1.00)	2e-05 (0.00268)		0.162 (1.00)	0.00264 (0.322)	0.455 (1.00)
mRNA cHierClus subtypes	0.477 (1.00)	0.0658 (1.00)	0.632 (1.00)	0.648 (1.00)	0.79 (1.00)	0.896 (1.00)	0.0813 (1.00)	1e-05 (0.00142)		0.548 (1.00)	0.765 (1.00)	1 (1.00)
Copy Number Ratio CNMF subtypes	0.946 (1.00)	0.321 (1.00)	0.00229 (0.282)	0.901 (1.00)	0.0027 (0.327)	0.00099 (0.123)	0.674 (1.00)	1e-05 (0.00142)	0.0814 (1.00)	0.013 (1.00)	0.00484 (0.552)	0.0532 (1.00)
METHLYATION CNMF	0.936 (1.00)	0.000746 (0.0932)	0.428 (1.00)	0.288 (1.00)	0.824 (1.00)	0.247 (1.00)	0.0617 (1.00)	0.0135 (1.00)	0.481 (1.00)	0.429 (1.00)	0.901 (1.00)	0.589 (1.00)
RPPA CNMF subtypes	0.83 (1.00)	0.88 (1.00)	0.396 (1.00)	0.646 (1.00)	0.528 (1.00)	0.0486 (1.00)	0.826 (1.00)	0.0357 (1.00)	0.497 (1.00)	0.00636 (0.719)	0.16 (1.00)	0.174 (1.00)
RPPA cHierClus subtypes	0.0961 (1.00)	0.446 (1.00)	0.0119 (1.00)	0.0757 (1.00)	0.182 (1.00)	0.193 (1.00)	0.599 (1.00)	0.447 (1.00)	0.281 (1.00)	0.0949 (1.00)	0.0472 (1.00)	0.262 (1.00)
RNAseq CNMF subtypes	0.02 (1.00)	0.00375 (0.439)	0.00386 (0.448)	0.466 (1.00)	0.00466 (0.536)	1e-05 (0.00142)	0.106 (1.00)	1e-05 (0.00142)	0.182 (1.00)	8e-05 (0.0105)	0.0145 (1.00)	0.989 (1.00)
RNAseq cHierClus subtypes	0.659 (1.00)	0.000248 (0.0317)	0.00346 (0.408)	0.823 (1.00)	0.0937 (1.00)	1e-05 (0.00142)	0.361 (1.00)	1e-05 (0.00142)	0.137 (1.00)	1e-05 (0.00142)	0.229 (1.00)	0.941 (1.00)
MIRSEQ CNMF	0.157 (1.00)	0.000566 (0.0713)	0.0509 (1.00)	0.386 (1.00)	0.292 (1.00)	1e-05 (0.00142)	0.446 (1.00)	0.101 (1.00)	0.0694 (1.00)	2e-05 (0.00268)	0.517 (1.00)	0.677 (1.00)
MIRSEQ CHIERARCHICAL	0.929 (1.00)	0.000101 (0.0131)	0.155 (1.00)	0.892 (1.00)	0.0807 (1.00)	2e-05 (0.00268)	0.479 (1.00)	0.31 (1.00)	0.0616 (1.00)	0.00028 (0.0356)	0.162 (1.00)	0.28 (1.00)
MIRseq Mature CNMF subtypes	0.131 (1.00)	0.135 (1.00)	0.562 (1.00)	0.175 (1.00)	0.0243 (1.00)	0.43 (1.00)	0.323 (1.00)	0.00011 (0.0142)	0.746 (1.00)	0.364 (1.00)	0.0409 (1.00)	0.688 (1.00)
MIRseq Mature cHierClus subtypes	0.296 (1.00)	0.476 (1.00)	0.143 (1.00)	0.832 (1.00)	0.0331 (1.00)	0.726 (1.00)	0.765 (1.00)	0.00313 (0.372)	0.594 (1.00)	0.289 (1.00)	0.0625 (1.00)	0.786 (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	38	61	30	24

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.755 (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	141	26	0.9 - 54.0 (23.0)
subtype1	34	7	0.9 - 46.6 (23.7)
subtype2	56	12	1.0 - 51.0 (22.0)
subtype3	28	4	1.0 - 54.0 (18.6)
subtype4	23	3	0.9 - 50.0 (27.0)

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 = 0.597 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	153	70.8 (11.5)
subtype1	38	72.4 (11.4)
subtype2	61	69.9 (11.7)
subtype3	30	72.0 (10.9)
subtype4	24	68.9 (12.3)

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 = 0.00283 (Fisher's exact test), Q value = 0.34

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

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	29	12	45	5	8	3	12	16	21	1
subtype1	6	1	14	3	3	1	3	1	5	0
subtype2	11	2	12	0	2	2	5	13	14	0
subtype3	7	6	9	2	1	0	3	0	1	1
subtype4	5	3	10	0	2	0	1	2	1	0

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 = 0.683 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	4	31	103	15
subtype1	1	7	24	6
subtype2	2	12	42	5
subtype3	0	7	19	4
subtype4	1	5	18	0

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 = 0.00855 (Fisher's exact test), Q value = 0.96

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

nPatients	N0	N1	N2
ALL	94	28	31
subtype1	25	5	8
subtype2	27	15	19
subtype3	24	5	1
subtype4	18	3	3

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 = 0.0327 (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	M1A
ALL	129	21	1
subtype1	32	5	0
subtype2	47	14	0
subtype3	28	1	1
subtype4	22	1	0

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 = 0.0357 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	75	78
subtype1	25	13
subtype2	31	30
subtype3	10	20
subtype4	9	15

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 = 2e-05 (Fisher's exact test), Q value = 0.0027

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	129	22
subtype1	25	13
subtype2	60	1
subtype3	22	7
subtype4	22	1

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

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

P value = 0.00264 (Kruskal-Wallis (anova)), Q value = 1

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	152	2.1 (4.5)
subtype1	38	2.7 (5.8)
subtype2	61	2.8 (4.8)
subtype3	29	0.7 (2.3)
subtype4	24	1.2 (2.6)

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.455 (Fisher's exact test), Q value = 0.32

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11
subtype1	0	6
subtype2	1	4
subtype3	0	0
subtype4	0	1

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'RACE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S12. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	46	73	34

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.477 (logrank test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	141	26	0.9 - 54.0 (23.0)
subtype1	42	8	0.9 - 53.0 (23.2)
subtype2	69	15	1.0 - 54.0 (21.0)
subtype3	30	3	0.9 - 39.0 (24.0)

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0658 (Kruskal-Wallis (anova)), Q value = 1

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	153	70.8 (11.5)
subtype1	46	73.4 (11.8)
subtype2	73	70.6 (10.6)
subtype3	34	67.5 (12.5)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.632 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	29	12	45	5	8	3	12	16	21	1
subtype1	9	3	14	4	3	1	5	1	5	0
subtype2	12	7	20	1	4	2	4	10	12	1
subtype3	8	2	11	0	1	0	3	5	4	0

Figure S13. 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 = 0.648 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	4	31	103	15
subtype1	1	10	28	7
subtype2	2	13	51	7
subtype3	1	8	24	1

Figure S14. 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 = 0.79 (Fisher's exact test), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	94	28	31
subtype1	31	7	8
subtype2	42	16	15
subtype3	21	5	8

Figure S15. 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 = 0.896 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A
ALL	129	21	1
subtype1	40	5	0
subtype2	60	12	1
subtype3	29	4	0

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0813 (Fisher's exact test), Q value = 1

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	75	78
subtype1	28	18
subtype2	35	38
subtype3	12	22

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	129	22
subtype1	27	18
subtype2	69	4
subtype3	33	0

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.765 (Kruskal-Wallis (anova)), Q value = 1

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	152	2.1 (4.5)
subtype1	46	2.3 (5.3)
subtype2	72	2.2 (4.6)
subtype3	34	1.7 (2.7)

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

'mRNA cHierClus subtypes' versus 'RACE'

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

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11
subtype1	0	5
subtype2	1	5
subtype3	0	1

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

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

Table S23. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	36	151	191	52

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

P value = 0.946 (logrank test), Q value = 1

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	413	84	0.1 - 140.4 (20.0)
subtype1	33	4	0.4 - 65.0 (13.9)
subtype2	147	31	0.1 - 131.5 (21.0)
subtype3	181	38	0.1 - 140.4 (21.0)
subtype4	52	11	0.9 - 119.7 (16.8)

Figure S21. 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 = 0.321 (Kruskal-Wallis (anova)), Q value = 1

Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	429	67.2 (12.9)
subtype1	36	64.4 (13.6)
subtype2	151	66.9 (11.7)
subtype3	190	68.2 (13.9)
subtype4	52	66.4 (11.9)

Figure S22. 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 = 0.00229 (Fisher's exact test), Q value = 0.28

Table S26. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: '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	72	1	28	127	8	1	21	13	53	38	42	16	1
subtype1	7	0	2	13	0	0	2	2	2	1	3	3	0
subtype2	19	0	5	34	2	1	5	6	25	16	26	8	0
subtype3	35	1	15	70	6	0	9	3	20	16	8	3	1
subtype4	11	0	6	10	0	0	5	2	6	5	5	2	0

Figure S23. 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 = 0.901 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	76	294	48
subtype1	0	9	24	3
subtype2	5	23	108	15
subtype3	5	33	127	25
subtype4	1	11	35	5

Figure S24. 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 = 0.0027 (Fisher's exact test), Q value = 0.33

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	252	101	76
subtype1	23	8	5
subtype2	68	48	34
subtype3	131	32	28
subtype4	30	13	9

Figure S25. 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 = 0.00099 (Fisher's exact test), Q value = 0.12

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	320	50	7	1	45
subtype1	25	4	2	0	4
subtype2	103	31	3	0	12
subtype3	153	9	1	1	23
subtype4	39	6	1	0	6

Figure S26. 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 = 0.674 (Fisher's exact test), Q value = 1

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	203	227
subtype1	19	17
subtype2	66	85
subtype3	94	97
subtype4	24	28

Figure S27. 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 = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	373	55
subtype1	36	0
subtype2	147	3
subtype3	143	47
subtype4	47	5

Figure S28. 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 = 0.0814 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	427
subtype1	1	35
subtype2	1	150
subtype3	0	191
subtype4	1	51

Figure S29. 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 = 0.013 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	305	3	24	24
subtype1	26	0	2	5
subtype2	102	1	15	5
subtype3	142	2	3	12
subtype4	35	0	4	2

Figure S30. 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 = 0.00484 (Kruskal-Wallis (anova)), Q value = 0.55

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	406	2.0 (4.4)
subtype1	31	2.1 (3.4)
subtype2	146	2.6 (5.5)
subtype3	179	1.7 (3.9)
subtype4	50	1.8 (3.5)

Figure S31. 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 = 0.0532 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	41	211
subtype1	0	4	5	14
subtype2	0	1	11	75
subtype3	1	6	21	94
subtype4	0	0	4	28

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S36. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	113	91	71

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.936 (logrank test), Q value = 1

Table S37. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	271	57	0.1 - 140.4 (17.9)
subtype1	111	25	0.1 - 140.4 (22.0)
subtype2	89	18	0.1 - 135.7 (17.6)
subtype3	71	14	0.1 - 102.4 (15.6)

Figure S33. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000746 (Kruskal-Wallis (anova)), Q value = 0.093

Table S38. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	274	65.2 (13.1)
subtype1	113	66.0 (12.6)
subtype2	91	67.8 (12.9)
subtype3	70	60.3 (13.0)

Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.428 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: '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	14	88	5	1	7	10	41	23	20	15	1
subtype1	16	0	6	31	1	1	3	5	18	8	10	10	0
subtype2	19	1	3	29	3	0	4	2	12	8	4	3	1
subtype3	6	0	5	28	1	0	0	3	11	7	6	2	0

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.288 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	7	42	191	34
subtype1	3	19	79	12
subtype2	3	18	58	11
subtype3	1	5	54	11

Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.824 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	162	69	43
subtype1	63	31	18
subtype2	57	19	15
subtype3	42	19	10

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.247 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	190	28	6	1	45
subtype1	73	16	4	0	19
subtype2	63	5	1	1	18
subtype3	54	7	1	0	8

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0617 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	126	149
subtype1	46	67
subtype2	51	40
subtype3	29	42

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0135 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	242	33
subtype1	107	6
subtype2	76	15
subtype3	59	12

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.481 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	272
subtype1	2	111
subtype2	0	91
subtype3	1	70

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.429 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	176	2	4	23
subtype1	70	0	3	9
subtype2	59	2	1	10
subtype3	47	0	0	4

Figure S42. 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 = 0.901 (Kruskal-Wallis (anova)), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	252	1.9 (4.4)
subtype1	104	2.1 (5.7)
subtype2	83	1.6 (2.7)
subtype3	65	2.1 (3.8)

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.589 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	40	205
subtype1	0	4	16	82
subtype2	0	2	13	71
subtype3	1	5	11	52

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	80	128	27	96

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.83 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	319	65	0.1 - 140.4 (20.3)
subtype1	78	12	0.1 - 131.5 (21.0)
subtype2	121	29	0.1 - 140.4 (21.0)
subtype3	26	4	1.0 - 52.0 (23.0)
subtype4	94	20	0.1 - 135.7 (19.6)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.88 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	330	67.3 (13.0)
subtype1	80	66.8 (12.9)
subtype2	127	68.1 (12.3)
subtype3	27	66.9 (11.9)
subtype4	96	66.7 (14.3)

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

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

Table S52. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: '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	49	1	20	108	8	1	18	8	43	29	28	14	1
subtype1	10	0	6	23	3	0	2	3	14	10	5	4	0
subtype2	18	0	10	34	3	1	8	2	18	8	18	5	1
subtype3	3	1	1	12	0	0	2	0	3	3	1	1	0
subtype4	18	0	3	39	2	0	6	3	8	8	4	4	0

Figure S47. 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 = 0.646 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	6	53	233	38
subtype1	0	12	59	9
subtype2	2	21	90	15
subtype3	0	3	22	1
subtype4	4	17	62	13

Figure S48. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.528 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	196	80	55
subtype1	42	23	15
subtype2	72	32	24
subtype3	17	6	4
subtype4	65	19	12

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

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

P value = 0.0486 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	M1A	M1B	MX
ALL	259	36	6	1	26
subtype1	65	6	3	0	5
subtype2	92	23	0	1	11
subtype3	24	2	0	0	1
subtype4	78	5	3	0	9

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.826 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	155	176
subtype1	38	42
subtype2	56	72
subtype3	13	14
subtype4	48	48

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0357 (Fisher's exact test), Q value = 1

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	293	38
subtype1	68	12
subtype2	121	7
subtype3	23	4
subtype4	81	15

Figure S52. 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 = 0.497 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	3	328
subtype1	2	78
subtype2	1	127
subtype3	0	27
subtype4	0	96

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

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

P value = 0.00636 (Fisher's exact test), Q value = 0.72

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

nPatients	R0	R2	RX
ALL	241	16	16
subtype1	61	1	6
subtype2	78	13	6
subtype3	26	1	0
subtype4	76	1	4

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

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

P value = 0.16 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	311	2.0 (4.5)
subtype1	74	2.9 (6.8)
subtype2	120	2.0 (3.8)
subtype3	27	1.7 (3.3)
subtype4	90	1.2 (2.6)

Figure S55. 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 = 0.174 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	21	181
subtype1	1	5	7	43
subtype2	0	5	6	63
subtype3	0	0	2	6
subtype4	0	1	6	69

Figure S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S62. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	60	49	56	68	69	29

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0961 (logrank test), Q value = 1

Table S63. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	319	65	0.1 - 140.4 (20.3)
subtype1	59	8	0.1 - 131.5 (18.1)
subtype2	45	8	0.9 - 85.0 (24.0)
subtype3	55	18	0.1 - 119.9 (18.0)
subtype4	65	11	0.2 - 140.4 (22.4)
subtype5	67	10	0.1 - 135.7 (20.1)
subtype6	28	10	2.0 - 107.7 (18.0)

Figure S57. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.446 (Kruskal-Wallis (anova)), Q value = 1

Table S64. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	330	67.3 (13.0)
subtype1	60	66.2 (11.5)
subtype2	49	70.1 (12.9)
subtype3	55	67.5 (14.0)
subtype4	68	66.5 (12.4)
subtype5	69	66.1 (14.0)
subtype6	29	68.9 (13.1)

Figure S58. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0119 (Fisher's exact test), Q value = 1

Table S65. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: '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	49	1	20	108	8	1	18	8	43	29	28	14	1
subtype1	7	0	1	22	1	0	0	2	10	7	4	6	0
subtype2	10	0	7	7	1	0	6	1	5	4	7	1	0
subtype3	6	0	2	16	3	1	1	0	11	7	6	2	1
subtype4	7	1	6	20	1	0	7	2	10	4	6	2	0
subtype5	17	0	2	28	1	0	2	3	4	6	3	3	0
subtype6	2	0	2	15	1	0	2	0	3	1	2	0	0

Figure S59. 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 = 0.0757 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	6	53	233	38
subtype1	0	7	45	8
subtype2	0	13	33	3
subtype3	0	7	38	11
subtype4	2	8	51	6
subtype5	4	16	42	7
subtype6	0	2	24	3

Figure S60. 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 = 0.182 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	196	80	55
subtype1	32	15	13
subtype2	28	13	8
subtype3	28	15	13
subtype4	37	22	9
subtype5	51	10	8
subtype6	20	5	4

Figure S61. 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 = 0.193 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	259	36	6	1	26
subtype1	46	7	3	0	3
subtype2	40	8	0	0	1
subtype3	42	8	0	1	5
subtype4	51	8	0	0	9
subtype5	57	3	3	0	6
subtype6	23	2	0	0	2

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.599 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	155	176
subtype1	27	33
subtype2	25	24
subtype3	23	33
subtype4	34	34
subtype5	29	40
subtype6	17	12

Figure S63. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.447 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	293	38
subtype1	51	9
subtype2	46	3
subtype3	52	4
subtype4	61	7
subtype5	58	11
subtype6	25	4

Figure S64. 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 = 0.281 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	328
subtype1	2	58
subtype2	0	49
subtype3	1	55
subtype4	0	68
subtype5	0	69
subtype6	0	29

Figure S65. 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 = 0.0949 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R2	RX
ALL	241	16	16
subtype1	44	2	2
subtype2	40	5	1
subtype3	30	1	7
subtype4	49	5	2
subtype5	56	1	3
subtype6	22	2	1

Figure S66. 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 = 0.0472 (Kruskal-Wallis (anova)), Q value = 1

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	311	2.0 (4.5)
subtype1	56	3.2 (7.6)
subtype2	48	1.5 (2.3)
subtype3	51	2.8 (5.0)
subtype4	65	1.6 (3.0)
subtype5	64	1.2 (3.0)
subtype6	27	1.2 (2.5)

Figure S67. 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 = 0.262 (Fisher's exact test), Q value = 1

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	21	181
subtype1	0	3	5	43
subtype2	0	0	1	14
subtype3	1	4	4	41
subtype4	0	1	7	19
subtype5	0	1	3	50
subtype6	0	2	1	14

Figure S68. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S75. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	35	114	72	90	119

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.02 (logrank test), Q value = 1

Table S76. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	413	85	0.1 - 140.4 (20.3)
subtype1	34	1	7.0 - 107.7 (27.1)
subtype2	111	30	0.1 - 140.4 (20.6)
subtype3	71	16	0.1 - 100.0 (13.6)
subtype4	86	15	0.7 - 135.7 (20.9)
subtype5	111	23	0.9 - 83.8 (22.0)

Figure S69. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00375 (Kruskal-Wallis (anova)), Q value = 0.44

Table S77. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	429	67.2 (12.9)
subtype1	35	65.7 (12.6)
subtype2	114	65.3 (12.4)
subtype3	72	64.1 (12.9)
subtype4	89	69.3 (13.6)
subtype5	119	69.7 (12.5)

Figure S70. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00386 (Fisher's exact test), Q value = 0.45

Table S78. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: '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	71	1	27	130	9	1	21	13	53	36	42	16	1
subtype1	6	0	2	10	0	0	4	3	4	2	1	3	0
subtype2	18	0	4	31	0	1	3	4	17	8	15	8	0
subtype3	7	0	3	25	1	0	2	2	10	9	8	3	0
subtype4	21	1	10	32	4	0	3	0	11	3	2	1	1
subtype5	19	0	8	32	4	0	9	4	11	14	16	1	0

Figure S71. 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 = 0.466 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	75	294	49
subtype1	1	6	25	3
subtype2	2	24	75	13
subtype3	1	6	51	14
subtype4	3	18	60	8
subtype5	4	21	83	11

Figure S72. 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 = 0.00466 (Fisher's exact test), Q value = 0.54

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	254	101	74
subtype1	22	10	3
subtype2	62	35	16
subtype3	37	18	17
subtype4	68	13	9
subtype5	65	25	29

Figure S73. 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 = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S81. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	323	50	7	1	42
subtype1	29	2	1	0	3
subtype2	73	19	4	0	17
subtype3	50	10	1	0	9
subtype4	72	3	0	1	13
subtype5	99	16	1	0	0

Figure S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	202	228
subtype1	11	24
subtype2	53	61
subtype3	30	42
subtype4	42	48
subtype5	66	53

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	372	56
subtype1	33	2
subtype2	112	2
subtype3	59	13
subtype4	62	28
subtype5	106	11

Figure S76. 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 = 0.182 (Fisher's exact test), Q value = 1

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	427
subtype1	1	34
subtype2	1	113
subtype3	1	71
subtype4	0	90
subtype5	0	119

Figure S77. 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 = 8e-05 (Fisher's exact test), Q value = 0.01

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	305	3	23	24
subtype1	27	1	1	2
subtype2	68	0	8	7
subtype3	50	0	1	6
subtype4	60	1	0	9
subtype5	100	1	13	0

Figure S78. 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 = 0.0145 (Kruskal-Wallis (anova)), Q value = 1

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	406	2.0 (4.4)
subtype1	35	1.3 (2.8)
subtype2	106	1.9 (4.1)
subtype3	66	3.2 (7.4)
subtype4	81	1.0 (2.5)
subtype5	118	2.3 (3.9)

Figure S79. 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 = 0.989 (Fisher's exact test), Q value = 1

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	36	213
subtype1	0	0	2	18
subtype2	0	4	12	72
subtype3	1	3	10	54
subtype4	0	4	9	51
subtype5	0	0	3	18

Figure S80. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S88. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	74	97	88	132	39

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.659 (logrank test), Q value = 1

Table S89. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	413	85	0.1 - 140.4 (20.3)
subtype1	74	15	0.3 - 91.8 (16.7)
subtype2	95	20	0.1 - 140.4 (20.6)
subtype3	86	21	0.1 - 135.7 (16.2)
subtype4	122	22	0.9 - 83.8 (24.0)
subtype5	36	7	1.0 - 54.0 (23.7)

Figure S81. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000248 (Kruskal-Wallis (anova)), Q value = 0.032

Table S90. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	429	67.2 (12.9)
subtype1	74	65.5 (13.2)
subtype2	97	65.7 (11.6)
subtype3	87	64.5 (14.5)
subtype4	132	68.6 (12.4)
subtype5	39	74.8 (10.5)

Figure S82. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00346 (Fisher's exact test), Q value = 0.41

Table S91. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: '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	71	1	27	130	9	1	21	13	53	36	42	16	1
subtype1	8	0	3	23	1	0	2	2	15	6	6	6	0
subtype2	15	0	7	30	1	1	3	3	14	4	7	8	0
subtype3	18	1	4	29	1	0	4	3	9	10	5	1	1
subtype4	26	0	8	34	2	0	8	5	10	16	22	1	0
subtype5	4	0	5	14	4	0	4	0	5	0	2	0	0

Figure S83. 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 = 0.823 (Fisher's exact test), Q value = 1

Table S92. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	75	294	49
subtype1	1	9	54	10
subtype2	2	17	66	12
subtype3	3	16	58	10
subtype4	4	29	88	11
subtype5	1	4	28	6

Figure S84. 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 = 0.0937 (Fisher's exact test), Q value = 1

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	254	101	74
subtype1	36	26	12
subtype2	62	23	11
subtype3	55	17	16
subtype4	73	29	30
subtype5	28	6	5

Figure S85. 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 = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	323	50	7	1	42
subtype1	51	9	3	0	11
subtype2	65	11	3	0	17
subtype3	64	6	0	1	14
subtype4	107	22	1	0	0
subtype5	36	2	0	0	0

Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.361 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	202	228
subtype1	33	41
subtype2	43	54
subtype3	38	50
subtype4	64	68
subtype5	24	15

Figure S87. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	372	56
subtype1	68	6
subtype2	91	6
subtype3	68	20
subtype4	122	8
subtype5	23	16

Figure S88. 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 = 0.137 (Fisher's exact test), Q value = 1

Table S97. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	427
subtype1	2	72
subtype2	1	96
subtype3	0	88
subtype4	0	132
subtype5	0	39

Figure S89. 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 = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S98. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	305	3	23	24
subtype1	53	0	0	5
subtype2	53	0	2	10
subtype3	54	2	1	9
subtype4	110	1	19	0
subtype5	35	0	1	0

Figure S90. 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 = 0.229 (Kruskal-Wallis (anova)), Q value = 1

Table S99. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	406	2.0 (4.4)
subtype1	70	2.5 (6.6)
subtype2	87	1.5 (2.9)
subtype3	80	1.8 (3.4)
subtype4	130	2.3 (4.4)
subtype5	39	1.6 (4.3)

Figure S91. 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 = 0.941 (Fisher's exact test), Q value = 1

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	36	213
subtype1	0	2	11	58
subtype2	0	4	11	68
subtype3	1	5	10	67
subtype4	0	0	4	15
subtype5	0	0	0	5

Figure S92. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S101. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	160	66	180

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.157 (logrank test), Q value = 1

Table S102. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	389	80	0.1 - 140.4 (21.0)
subtype1	158	30	0.1 - 140.4 (20.4)
subtype2	66	14	0.1 - 100.0 (13.4)
subtype3	165	36	0.9 - 83.8 (24.0)

Figure S93. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.000566 (Kruskal-Wallis (anova)), Q value = 0.071

Table S103. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	405	67.3 (13.0)
subtype1	159	66.5 (12.4)
subtype2	66	62.6 (14.4)
subtype3	180	69.9 (12.5)

Figure S94. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0509 (Fisher's exact test), Q value = 1

Table S104. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: '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	66	1	28	120	8	1	21	12	49	32	42	16	1
subtype1	24	1	7	55	2	1	7	5	24	11	11	7	1
subtype2	7	0	3	21	0	0	2	3	10	8	4	4	0
subtype3	35	0	18	44	6	0	12	4	15	13	27	5	0

Figure S95. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.386 (Fisher's exact test), Q value = 1

Table S105. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	69	279	46
subtype1	5	24	115	15
subtype2	0	9	46	11
subtype3	6	36	118	20

Figure S96. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.292 (Fisher's exact test), Q value = 1

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	239	95	71
subtype1	98	39	22
subtype2	33	19	14
subtype3	108	37	35

Figure S97. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0014

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	305	50	7	1	36
subtype1	113	12	5	1	26
subtype2	48	8	0	0	9
subtype3	144	30	2	0	1

Figure S98. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.446 (Fisher's exact test), Q value = 1

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	215
subtype1	74	86
subtype2	27	39
subtype3	90	90

Figure S99. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.101 (Fisher's exact test), Q value = 1

Table S109. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	351	53
subtype1	145	15
subtype2	53	13
subtype3	153	25

Figure S100. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0694 (Fisher's exact test), Q value = 1

Table S110. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	403
subtype1	0	160
subtype2	2	64
subtype3	1	179

Figure S101. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 2e-05 (Fisher's exact test), Q value = 0.0027

Table S111. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	282	3	25	22
subtype1	94	1	2	15
subtype2	43	0	0	5
subtype3	145	2	23	2

Figure S102. 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 = 0.517 (Kruskal-Wallis (anova)), Q value = 1

Table S112. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	382	2.0 (4.5)
subtype1	146	1.6 (3.0)
subtype2	61	3.2 (8.0)
subtype3	175	2.0 (3.8)

Figure S103. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.677 (Fisher's exact test), Q value = 1

Table S113. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	23	213
subtype1	0	7	12	132
subtype2	1	3	7	51
subtype3	0	1	4	30

Figure S104. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S114. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	136	50	79	141

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.929 (logrank test), Q value = 1

Table S115. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	389	80	0.1 - 140.4 (21.0)
subtype1	133	29	0.1 - 140.4 (20.1)
subtype2	50	9	0.1 - 100.0 (15.0)
subtype3	74	15	0.1 - 139.2 (18.0)
subtype4	132	27	0.9 - 131.5 (27.0)

Figure S105. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000101 (Kruskal-Wallis (anova)), Q value = 0.013

Table S116. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	405	67.3 (13.0)
subtype1	135	66.3 (13.3)
subtype2	50	63.2 (13.1)
subtype3	79	64.9 (13.1)
subtype4	141	71.2 (11.9)

Figure S106. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.155 (Fisher's exact test), Q value = 1

Table S117. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: '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	66	1	28	120	8	1	21	12	49	32	42	16	1
subtype1	20	0	7	49	4	1	6	4	15	10	9	6	1
subtype2	6	0	2	19	0	0	1	3	8	2	3	4	0
subtype3	11	1	4	19	0	0	4	3	12	8	14	1	0
subtype4	29	0	15	33	4	0	10	2	14	12	16	5	0

Figure S107. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.892 (Fisher's exact test), Q value = 1

Table S118. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	69	279	46
subtype1	4	21	94	17
subtype2	1	6	39	4
subtype3	2	13	56	7
subtype4	4	29	90	18

Figure S108. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.0807 (Fisher's exact test), Q value = 1

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	239	95	71
subtype1	90	26	19
subtype2	27	17	6
subtype3	38	21	20
subtype4	84	31	26

Figure S109. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 2e-05 (Fisher's exact test), Q value = 0.0027

Table S120. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	305	50	7	1	36
subtype1	97	11	3	1	21
subtype2	36	6	1	0	7
subtype3	55	14	1	0	8
subtype4	117	19	2	0	0

Figure S110. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S121. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	215
subtype1	57	79
subtype2	23	27
subtype3	40	39
subtype4	71	70

Figure S111. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.31 (Fisher's exact test), Q value = 1

Table S122. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	351	53
subtype1	120	16
subtype2	40	10
subtype3	71	7
subtype4	120	20

Figure S112. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0616 (Fisher's exact test), Q value = 1

Table S123. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	403
subtype1	0	136
subtype2	2	48
subtype3	0	79
subtype4	1	140

Figure S113. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.00028 (Fisher's exact test), Q value = 0.036

Table S124. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	282	3	25	22
subtype1	76	1	3	11
subtype2	37	0	0	5
subtype3	51	1	9	5
subtype4	118	1	13	1

Figure S114. 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 = 0.162 (Kruskal-Wallis (anova)), Q value = 1

Table S125. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	382	2.0 (4.5)
subtype1	128	1.6 (3.2)
subtype2	44	1.6 (2.9)
subtype3	72	2.9 (6.5)
subtype4	138	2.2 (4.7)

Figure S115. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.28 (Fisher's exact test), Q value = 1

Table S126. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	23	213
subtype1	0	4	11	106
subtype2	1	3	3	43
subtype3	0	4	6	32
subtype4	0	0	3	32

Figure S116. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S127. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	79	78	63

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

P value = 0.131 (logrank test), Q value = 1

Table S128. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	217	44	0.1 - 140.4 (17.6)
subtype1	77	16	0.1 - 140.4 (20.6)
subtype2	77	15	0.1 - 135.7 (19.4)
subtype3	63	13	0.2 - 62.8 (15.0)

Figure S117. 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 = 0.135 (Kruskal-Wallis (anova)), Q value = 1

Table S129. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	219	65.3 (13.2)
subtype1	79	66.7 (11.2)
subtype2	77	66.3 (13.9)
subtype3	63	62.5 (14.3)

Figure S118. 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 = 0.562 (Fisher's exact test), Q value = 1

Table S130. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: '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	30	1	10	74	3	1	7	8	33	18	15	11	1
subtype1	10	0	3	26	0	1	4	4	10	8	6	3	0
subtype2	15	1	5	28	2	0	2	3	10	3	3	3	1
subtype3	5	0	2	20	1	0	1	1	13	7	6	5	0

Figure S119. 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 = 0.175 (Fisher's exact test), Q value = 1

Table S131. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	5	31	157	26
subtype1	2	12	58	7
subtype2	2	15	53	7
subtype3	1	4	46	12

Figure S120. 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 = 0.0243 (Fisher's exact test), Q value = 1

Table S132. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	129	55	35
subtype1	45	20	14
subtype2	55	16	6
subtype3	29	19	15

Figure S121. 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 = 0.43 (Fisher's exact test), Q value = 1

Table S133. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	154	20	5	1	36
subtype1	59	7	2	0	10
subtype2	55	4	1	1	16
subtype3	40	9	2	0	10

Figure S122. 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 = 0.323 (Fisher's exact test), Q value = 1

Table S134. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	96	124
subtype1	30	49
subtype2	39	39
subtype3	27	36

Figure S123. 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 = 0.00011 (Fisher's exact test), Q value = 0.014

Table S135. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	191	29
subtype1	78	1
subtype2	62	16
subtype3	51	12

Figure S124. 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 = 0.746 (Fisher's exact test), Q value = 1

Table S136. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	218
subtype1	1	78
subtype2	0	78
subtype3	1	62

Figure S125. 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 = 0.364 (Fisher's exact test), Q value = 1

Table S137. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	130	1	2	20
subtype1	44	0	1	7
subtype2	44	1	1	10
subtype3	42	0	0	3

Figure S126. 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 = 0.0409 (Kruskal-Wallis (anova)), Q value = 1

Table S138. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	200	2.0 (4.7)
subtype1	72	1.8 (3.1)
subtype2	69	1.1 (2.6)
subtype3	59	3.2 (7.3)

Figure S127. 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 = 0.688 (Fisher's exact test), Q value = 1

Table S139. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	10	18	181
subtype1	0	4	4	65
subtype2	0	4	7	64
subtype3	1	2	7	52

Figure S128. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S140. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	66	51	83	20

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

P value = 0.296 (logrank test), Q value = 1

Table S141. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	217	44	0.1 - 140.4 (17.6)
subtype1	65	12	0.1 - 140.4 (17.8)
subtype2	50	11	0.8 - 135.7 (22.6)
subtype3	83	18	0.1 - 131.5 (13.0)
subtype4	19	3	0.1 - 139.2 (17.1)

Figure S129. 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 = 0.476 (Kruskal-Wallis (anova)), Q value = 1

Table S142. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	219	65.3 (13.2)
subtype1	66	66.2 (12.5)
subtype2	50	67.1 (12.6)
subtype3	83	63.4 (14.2)
subtype4	20	66.1 (12.7)

Figure S130. 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 = 0.143 (Fisher's exact test), Q value = 1

Table S143. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: '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	30	1	10	74	3	1	7	8	33	18	15	11	1
subtype1	10	1	4	17	0	1	6	4	9	2	4	4	0
subtype2	7	0	2	25	2	0	1	1	5	3	2	1	1
subtype3	10	0	3	27	1	0	0	3	13	11	6	6	0
subtype4	3	0	1	5	0	0	0	0	6	2	3	0	0

Figure S131. 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 = 0.832 (Fisher's exact test), Q value = 1

Table S144. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	5	31	157	26
subtype1	2	9	48	6
subtype2	2	9	36	4
subtype3	1	10	58	14
subtype4	0	3	15	2

Figure S132. 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 = 0.0331 (Fisher's exact test), Q value = 1

Table S145. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	129	55	35
subtype1	37	21	8
subtype2	39	5	6
subtype3	43	23	17
subtype4	10	6	4

Figure S133. 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 = 0.726 (Fisher's exact test), Q value = 1

Table S146. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	154	20	5	1	36
subtype1	43	5	2	0	14
subtype2	39	2	1	1	7
subtype3	58	10	2	0	12
subtype4	14	3	0	0	3

Figure S134. 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 = 0.765 (Fisher's exact test), Q value = 1

Table S147. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	96	124
subtype1	31	35
subtype2	22	29
subtype3	33	50
subtype4	10	10

Figure S135. 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 = 0.00313 (Fisher's exact test), Q value = 0.37

Table S148. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	191	29
subtype1	64	2
subtype2	43	8
subtype3	65	18
subtype4	19	1

Figure S136. 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 = 0.594 (Fisher's exact test), Q value = 1

Table S149. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	218
subtype1	0	66
subtype2	0	51
subtype3	2	81
subtype4	0	20

Figure S137. 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 = 0.289 (Fisher's exact test), Q value = 1

Table S150. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	130	1	2	20
subtype1	44	1	1	4
subtype2	22	0	1	5
subtype3	53	0	0	7
subtype4	11	0	0	4

Figure S138. 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 = 0.0625 (Kruskal-Wallis (anova)), Q value = 1

Table S151. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	200	2.0 (4.7)
subtype1	64	1.6 (2.8)
subtype2	46	1.1 (2.7)
subtype3	74	2.2 (3.8)
subtype4	16	4.9 (12.5)

Figure S139. 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 = 0.786 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	10	18	181
subtype1	0	1	4	54
subtype2	0	2	5	42
subtype3	1	5	7	69
subtype4	0	2	2	16

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

Methods & Data

Input

Cluster data file = COAD-TP.mergedcluster.txt
Clinical data file = COAD-TP.merged_data.txt
Number of patients = 439
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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