Correlation between aggregated molecular cancer subtypes and selected clinical features

Colon Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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 (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Z31WPW

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 11 clinical features across 422 patients, 12 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 'HISTOLOGICAL.TYPE' and 'LYMPH.NODE.METASTASIS'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 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 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE', 'DISTANT.METASTASIS', 'LYMPH.NODE.METASTASIS', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'DISTANT.METASTASIS' and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 12 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	DISTANT METASTASIS	LYMPH NODE METASTASIS	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
Statistical Tests	logrank test	ANOVA	Chi-square test	Chi-square test	Chi-square test	Chi-square test	Chi-square test	Chi-square test	ANOVA	ANOVA	Chi-square test
mRNA CNMF subtypes	0.138 (1.00)	0.569 (1.00)	0.0355 (1.00)	6.71e-07 (7.72e-05)		0.0507 (1.00)	0.0423 (1.00)	0.204 (1.00)	0.104 (1.00)		0.00763 (0.793)
mRNA cHierClus subtypes	0.636 (1.00)	0.0114 (1.00)	0.197 (1.00)	6.46e-09 (7.62e-07)		0.699 (1.00)	0.756 (1.00)	0.454 (1.00)	0.988 (1.00)		0.268 (1.00)
Copy Number Ratio CNMF subtypes	0.763 (1.00)	0.472 (1.00)	0.296 (1.00)	4.13e-08 (4.83e-06)	0.337 (1.00)	0.00962 (0.991)	0.000912 (0.1)	0.0137 (1.00)	0.586 (1.00)		0.0148 (1.00)
METHLYATION CNMF	0.761 (1.00)	0.00364 (0.385)	0.11 (1.00)	0.0687 (1.00)	0.614 (1.00)	0.175 (1.00)	0.345 (1.00)	0.182 (1.00)	0.667 (1.00)		0.462 (1.00)
RPPA CNMF subtypes	0.487 (1.00)	0.92 (1.00)	0.885 (1.00)	0.042 (1.00)	0.499 (1.00)	0.0911 (1.00)	0.484 (1.00)	0.0156 (1.00)	0.189 (1.00)		0.257 (1.00)
RPPA cHierClus subtypes	0.00986 (0.998)	0.379 (1.00)	0.258 (1.00)	0.271 (1.00)	0.32 (1.00)	0.175 (1.00)	0.177 (1.00)	0.217 (1.00)	0.0461 (1.00)		0.269 (1.00)
RNAseq CNMF subtypes	0.685 (1.00)	0.77 (1.00)	0.954 (1.00)	0.00116 (0.125)	0.242 (1.00)	0.0818 (1.00)	0.215 (1.00)	0.121 (1.00)	0.0199 (1.00)		0.171 (1.00)
RNAseq cHierClus subtypes	0.42 (1.00)	0.123 (1.00)	0.884 (1.00)	0.0122 (1.00)	1 (1.00)	0.674 (1.00)	0.142 (1.00)	0.51 (1.00)	0.269 (1.00)		0.392 (1.00)
MIRSEQ CNMF	0.229 (1.00)	0.000748 (0.0831)	0.473 (1.00)	0.0162 (1.00)	0.544 (1.00)	1.17e-05 (0.00132)	0.000986 (0.108)	3.44e-05 (0.00385)	0.73 (1.00)		0.381 (1.00)
MIRSEQ CHIERARCHICAL	0.514 (1.00)	0.00978 (0.998)	0.453 (1.00)	0.684 (1.00)	1 (1.00)	7.22e-06 (0.000823)	0.0334 (1.00)	6.58e-07 (7.64e-05)	0.847 (1.00)		0.0175 (1.00)
MIRseq Mature CNMF subtypes	0.571 (1.00)	0.143 (1.00)	0.483 (1.00)	0.00363 (0.385)	0.75 (1.00)	0.776 (1.00)	0.312 (1.00)	0.41 (1.00)	0.14 (1.00)		0.776 (1.00)
MIRseq Mature cHierClus subtypes	0.87 (1.00)	0.5 (1.00)	0.774 (1.00)	0.00201 (0.215)	0.131 (1.00)	0.907 (1.00)	0.173 (1.00)	0.715 (1.00)	0.0762 (1.00)		0.0824 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	38	61	30	24

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.138 (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	75	11	0.9 - 52.0 (4.0)
subtype1	24	1	1.0 - 30.0 (1.5)
subtype2	27	6	1.0 - 41.0 (14.4)
subtype3	16	3	1.0 - 52.0 (8.0)
subtype4	8	1	0.9 - 1.0 (1.0)

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 = 0.569 (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 'GENDER'

P value = 0.0355 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.71e-07 (Fisher's exact test), Q value = 7.7e-05

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	127	24
subtype1	23	15
subtype2	60	1
subtype3	22	7
subtype4	22	1

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

'mRNA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0423 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1B	N2	N2A
ALL	94	27	1	30	1
subtype1	25	5	0	8	0
subtype2	27	15	0	18	1
subtype3	24	4	1	1	0
subtype4	18	3	0	3	0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.204 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	128	1	19
subtype1	32	0	3
subtype2	47	1	13
subtype3	28	0	2
subtype4	21	0	1

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.104 (ANOVA), Q value = 1

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: '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 S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'mRNA CNMF subtypes' versus 'TUMOR.STAGECODE'

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

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'TUMOR.STAGECODE'

	nPatients	Mean (Std.Dev)
ALL	0	NaN (NA)

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	57	46	50

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.636 (logrank test), Q value = 1

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	75	11	0.9 - 52.0 (4.0)
subtype1	33	7	0.9 - 52.0 (15.0)
subtype2	28	2	1.0 - 30.0 (1.0)
subtype3	14	2	0.9 - 34.0 (1.0)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0114 (ANOVA), Q value = 1

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	153	70.8 (11.5)
subtype1	57	71.8 (9.4)
subtype2	46	73.7 (10.9)
subtype3	50	67.0 (13.4)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.197 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	75	78
subtype1	28	29
subtype2	27	19
subtype3	20	30

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.46e-09 (Fisher's exact test), Q value = 7.6e-07

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	127	24
subtype1	54	3
subtype2	25	20
subtype3	48	1

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.756 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1B	N2	N2A
ALL	94	27	1	30	1
subtype1	33	12	1	11	0
subtype2	31	7	0	8	0
subtype3	30	8	0	11	1

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.454 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	128	1	19
subtype1	48	1	8
subtype2	40	0	3
subtype3	40	0	8

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.988 (ANOVA), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	152	2.1 (4.5)
subtype1	56	2.2 (4.9)
subtype2	46	2.2 (4.7)
subtype3	50	2.1 (3.9)

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGECODE'

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

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'TUMOR.STAGECODE'

	nPatients	Mean (Std.Dev)
ALL	0	NaN (NA)

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

Table S19. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	170	187	32	24

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

P value = 0.763 (logrank test), Q value = 1

Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	321	49	0.1 - 135.5 (7.7)
subtype1	129	23	0.1 - 129.1 (10.0)
subtype2	149	19	0.1 - 129.1 (6.6)
subtype3	23	4	0.3 - 135.5 (7.0)
subtype4	20	3	0.2 - 112.7 (6.0)

Figure S15. 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 = 0.472 (ANOVA), Q value = 1

Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	412	67.3 (13.0)
subtype1	170	66.4 (12.4)
subtype2	186	68.3 (13.7)
subtype3	32	66.0 (13.0)
subtype4	24	68.2 (12.0)

Figure S16. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.296 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	192	221
subtype1	77	93
subtype2	92	95
subtype3	16	16
subtype4	7	17

Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 4.13e-08 (Fisher's exact test), Q value = 4.8e-06

Table S23. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	357	54
subtype1	162	7
subtype2	141	45
subtype3	31	1
subtype4	23	1

Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.337 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	410
subtype1	3	167
subtype2	0	187
subtype3	0	32
subtype4	0	24

Figure S19. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.00962 (Chi-square test), Q value = 0.99

Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	313	49	7	1	36
subtype1	119	32	3	0	14
subtype2	153	10	1	1	18
subtype3	21	6	2	0	2
subtype4	20	1	1	0	2

Figure S20. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000912 (Chi-square test), Q value = 0.1

Table S26. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	244	69	13	12	2	58	4	9	1
subtype1	78	44	7	3	0	32	2	2	1
subtype2	130	20	3	6	1	19	2	6	0
subtype3	20	3	1	3	0	5	0	0	0
subtype4	16	2	2	0	1	2	0	1	0

Figure S21. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0137 (Chi-square test), Q value = 1

Table S27. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	289	3	24	22
subtype1	117	1	14	5
subtype2	139	2	4	11
subtype3	18	0	5	5
subtype4	15	0	1	1

Figure S22. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.586 (ANOVA), Q value = 1

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	389	2.0 (4.5)
subtype1	165	2.4 (5.0)
subtype2	175	1.7 (4.2)
subtype3	27	2.1 (3.3)
subtype4	22	1.6 (4.0)

Figure S23. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

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

P value = 0.0148 (Chi-square test), Q value = 1

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: '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	68	1	33	116	8	1	22	13	47	34	40	16	1
subtype1	22	0	7	39	2	1	9	8	26	17	25	9	0
subtype2	36	1	18	63	6	0	10	3	18	13	9	3	1
subtype3	4	0	3	10	0	0	2	2	0	1	6	2	0
subtype4	6	0	5	4	0	0	1	0	3	3	0	2	0

Figure S24. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S30. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	108	83	64

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.761 (logrank test), Q value = 1

Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	238	36	0.1 - 135.5 (7.6)
subtype1	98	15	0.1 - 135.5 (8.0)
subtype2	78	12	0.1 - 129.1 (8.7)
subtype3	62	9	0.1 - 102.4 (5.9)

Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00364 (ANOVA), Q value = 0.38

Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	254	65.4 (13.3)
subtype1	108	66.1 (13.1)
subtype2	83	68.0 (13.1)
subtype3	63	60.8 (12.8)

Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.11 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	114	141
subtype1	43	65
subtype2	45	38
subtype3	26	38

Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0687 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	225	30
subtype1	101	7
subtype2	70	13
subtype3	54	10

Figure S28. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.614 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	252
subtype1	2	106
subtype2	0	83
subtype3	1	63

Figure S29. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.175 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	180	27	6	1	36
subtype1	70	17	4	0	16
subtype2	60	4	1	1	14
subtype3	50	6	1	0	6

Figure S30. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.345 (Chi-square test), Q value = 1

Table S37. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	151	39	13	11	2	25	3	9	1
subtype1	60	16	9	3	2	12	1	3	1
subtype2	52	13	1	5	0	10	1	1	0
subtype3	39	10	3	3	0	3	1	5	0

Figure S31. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.182 (Chi-square test), Q value = 1

Table S38. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	157	2	4	21
subtype1	66	0	3	8
subtype2	49	2	1	10
subtype3	42	0	0	3

Figure S32. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.667 (ANOVA), Q value = 1

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	232	1.9 (4.5)
subtype1	99	2.2 (5.8)
subtype2	75	1.5 (2.8)
subtype3	58	2.0 (3.7)

Figure S33. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.462 (Chi-square test), Q value = 1

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: '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	37	1	19	75	4	1	8	10	36	18	18	15	1
subtype1	15	0	7	28	1	1	4	5	15	7	10	10	0
subtype2	17	1	4	25	2	0	4	2	11	6	3	3	1
subtype3	5	0	8	22	1	0	0	3	10	5	5	2	0

Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S41. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	82	137	27	85

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.487 (logrank test), Q value = 1

Table S42. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	267	40	0.1 - 135.5 (8.0)
subtype1	67	5	0.1 - 119.0 (6.7)
subtype2	105	17	0.1 - 135.5 (9.4)
subtype3	20	2	0.6 - 52.0 (14.0)
subtype4	75	16	0.1 - 124.1 (8.0)

Figure S35. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.92 (ANOVA), Q value = 1

Table S43. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	330	67.3 (13.0)
subtype1	82	67.3 (12.6)
subtype2	136	67.8 (12.7)
subtype3	27	66.9 (12.0)
subtype4	85	66.5 (14.2)

Figure S36. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.885 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	155	176
subtype1	41	41
subtype2	61	76
subtype3	13	14
subtype4	40	45

Figure S37. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.042 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	291	40
subtype1	67	15
subtype2	128	9
subtype3	23	4
subtype4	73	12

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

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

P value = 0.499 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	328
subtype1	2	80
subtype2	1	136
subtype3	0	27
subtype4	0	85

Figure S39. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0911 (Chi-square test), Q value = 1

Table S47. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	259	36	6	1	26
subtype1	67	7	3	0	4
subtype2	100	23	0	1	12
subtype3	24	2	0	0	1
subtype4	68	4	3	0	9

Figure S40. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RPPA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.484 (Chi-square test), Q value = 1

Table S48. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B
ALL	196	57	12	9	2	42	4	8
subtype1	44	12	6	5	0	9	1	4
subtype2	78	28	4	1	1	19	3	3
subtype3	17	4	1	1	0	4	0	0
subtype4	57	13	1	2	1	10	0	1

Figure S41. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

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

Table S49. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R2	RX
ALL	239	16	16
subtype1	63	1	5
subtype2	84	13	7
subtype3	26	1	0
subtype4	66	1	4

Figure S42. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.189 (ANOVA), Q value = 1

Table S50. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	311	2.0 (4.5)
subtype1	76	2.8 (6.8)
subtype2	128	2.0 (3.7)
subtype3	27	1.7 (3.3)
subtype4	80	1.3 (2.8)

Figure S43. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.257 (Chi-square test), Q value = 1

Table S51. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: '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	25	101	8	1	19	8	42	29	27	14	1
subtype1	11	0	6	23	2	0	2	3	14	9	4	5	0
subtype2	20	0	14	33	4	1	9	2	19	9	18	5	1
subtype3	3	1	1	12	0	0	2	0	3	3	1	1	0
subtype4	15	0	4	33	2	0	6	3	6	8	4	3	0

Figure S44. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S52. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	29	57	64	181

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00986 (logrank test), Q value = 1

Table S53. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	267	40	0.1 - 135.5 (8.0)
subtype1	20	8	0.1 - 105.3 (9.8)
subtype2	52	9	0.1 - 124.1 (5.1)
subtype3	59	3	0.1 - 129.1 (6.7)
subtype4	136	20	0.1 - 135.5 (10.0)

Figure S45. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.379 (ANOVA), Q value = 1

Table S54. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	330	67.3 (13.0)
subtype1	29	69.4 (13.4)
subtype2	57	65.3 (13.5)
subtype3	64	66.1 (12.1)
subtype4	180	67.9 (13.1)

Figure S46. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.258 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	155	176
subtype1	17	12
subtype2	21	36
subtype3	30	34
subtype4	87	94

Figure S47. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.271 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	291	40
subtype1	26	3
subtype2	47	10
subtype3	54	10
subtype4	164	17

Figure S48. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.32 (Fisher's exact test), Q value = 1

Table S57. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	328
subtype1	0	29
subtype2	0	57
subtype3	2	62
subtype4	1	180

Figure S49. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.175 (Chi-square test), Q value = 1

Table S58. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	259	36	6	1	26
subtype1	23	2	0	0	2
subtype2	47	2	3	0	5
subtype3	49	7	3	0	4
subtype4	140	25	0	1	15

Figure S50. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RPPA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.177 (Chi-square test), Q value = 1

Table S59. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B
ALL	196	57	12	9	2	42	4	8
subtype1	19	4	1	1	0	4	0	0
subtype2	42	6	1	1	1	5	0	1
subtype3	34	7	3	5	0	9	1	4
subtype4	101	40	7	2	1	24	3	3

Figure S51. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.217 (Chi-square test), Q value = 1

Table S60. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R2	RX
ALL	239	16	16
subtype1	21	2	2
subtype2	46	0	2
subtype3	48	1	2
subtype4	124	13	10

Figure S52. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0461 (ANOVA), Q value = 1

Table S61. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	311	2.0 (4.5)
subtype1	27	1.3 (2.5)
subtype2	54	1.1 (2.7)
subtype3	60	3.2 (7.4)
subtype4	170	1.9 (3.6)

Figure S53. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.269 (Chi-square test), Q value = 1

Table S62. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: '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	25	101	8	1	19	8	42	29	27	14	1
subtype1	2	0	3	13	1	0	2	0	4	1	2	0	0
subtype2	14	0	3	22	1	0	2	1	4	5	2	3	0
subtype3	8	0	2	21	1	0	0	3	9	9	3	6	0
subtype4	25	1	17	45	5	1	15	4	25	14	20	5	1

Figure S54. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S63. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	31	60	47	54

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.685 (logrank test), Q value = 1

Table S64. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	176	25	0.1 - 129.1 (6.7)
subtype1	28	2	0.1 - 105.3 (6.1)
subtype2	55	11	0.1 - 129.1 (8.1)
subtype3	43	4	0.1 - 75.2 (4.0)
subtype4	50	8	0.1 - 103.0 (7.1)

Figure S55. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.77 (ANOVA), Q value = 1

Table S65. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	191	65.4 (13.8)
subtype1	31	66.1 (13.4)
subtype2	60	65.7 (13.5)
subtype3	47	63.6 (13.1)
subtype4	53	66.3 (15.1)

Figure S56. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.954 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	92	100
subtype1	14	17
subtype2	28	32
subtype3	24	23
subtype4	26	28

Figure S57. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00116 (Fisher's exact test), Q value = 0.12

Table S67. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	168	24
subtype1	29	2
subtype2	59	1
subtype3	37	10
subtype4	43	11

Figure S58. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.242 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	190
subtype1	1	30
subtype2	0	60
subtype3	1	46
subtype4	0	54

Figure S59. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0818 (Chi-square test), Q value = 1

Table S69. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	130	19	5	1	33
subtype1	22	5	0	0	4
subtype2	38	4	5	0	12
subtype3	32	7	0	0	6
subtype4	38	3	0	1	11

Figure S60. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.215 (Chi-square test), Q value = 1

Table S70. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	117	27	12	7	1	16	2	9	1
subtype1	19	7	2	1	0	2	0	0	0
subtype2	34	10	6	2	1	4	0	2	1
subtype3	24	5	2	2	0	6	2	6	0
subtype4	40	5	2	2	0	4	0	1	0

Figure S61. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.121 (Chi-square test), Q value = 1

Table S71. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	116	1	2	22
subtype1	19	0	2	2
subtype2	32	0	0	7
subtype3	32	0	0	5
subtype4	33	1	0	8

Figure S62. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0199 (ANOVA), Q value = 1

Table S72. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	170	2.0 (4.9)
subtype1	29	1.0 (2.1)
subtype2	53	1.5 (2.8)
subtype3	41	4.0 (8.5)
subtype4	47	1.3 (3.0)

Figure S63. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.171 (Chi-square test), Q value = 1

Table S73. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	32	17	55	3	1	9	5	24	12	12	13	1
subtype1	7	2	7	0	0	4	2	3	0	3	3	0
subtype2	10	5	14	0	1	2	2	10	3	2	7	0
subtype3	5	3	13	1	0	1	1	6	7	5	2	0
subtype4	10	7	21	2	0	2	0	5	2	2	1	1

Figure S64. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S74. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	49	63	80

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.42 (logrank test), Q value = 1

Table S75. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	176	25	0.1 - 129.1 (6.7)
subtype1	47	8	0.1 - 56.5 (6.7)
subtype2	56	8	0.1 - 129.1 (7.8)
subtype3	73	9	0.1 - 87.8 (6.0)

Figure S65. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.123 (ANOVA), Q value = 1

Table S76. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	191	65.4 (13.8)
subtype1	48	63.2 (14.9)
subtype2	63	68.2 (12.3)
subtype3	80	64.5 (14.0)

Figure S66. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.884 (Fisher's exact test), Q value = 1

Table S77. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	92	100
subtype1	25	24
subtype2	30	33
subtype3	37	43

Figure S67. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0122 (Fisher's exact test), Q value = 1

Table S78. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	168	24
subtype1	40	9
subtype2	61	2
subtype3	67	13

Figure S68. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

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

Table S79. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	190
subtype1	0	49
subtype2	1	62
subtype3	1	79

Figure S69. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.674 (Chi-square test), Q value = 1

Table S80. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	130	19	5	1	33
subtype1	36	4	0	1	7
subtype2	43	6	2	0	11
subtype3	51	9	3	0	15

Figure S70. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.142 (Chi-square test), Q value = 1

Table S81. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	117	27	12	7	1	16	2	9	1
subtype1	37	5	0	2	0	4	0	1	0
subtype2	40	10	4	0	1	5	0	2	1
subtype3	40	12	8	5	0	7	2	6	0

Figure S71. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.51 (Chi-square test), Q value = 1

Table S82. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	116	1	2	22
subtype1	29	0	1	5
subtype2	34	0	1	10
subtype3	53	1	0	7

Figure S72. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.269 (ANOVA), Q value = 1

Table S83. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	170	2.0 (4.9)
subtype1	44	1.4 (3.1)
subtype2	55	1.5 (2.9)
subtype3	71	2.7 (6.7)

Figure S73. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.392 (Chi-square test), Q value = 1

Table S84. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	32	17	55	3	1	9	5	24	12	12	13	1
subtype1	11	5	17	2	0	3	2	3	1	3	1	1
subtype2	12	5	17	0	1	4	2	7	3	3	6	0
subtype3	9	7	21	1	0	2	1	14	8	6	6	0

Figure S74. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S85. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	182	163	62

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.229 (logrank test), Q value = 1

Table S86. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	311	49	0.1 - 135.5 (7.5)
subtype1	99	17	0.9 - 71.7 (7.0)
subtype2	154	26	0.1 - 135.5 (9.4)
subtype3	58	6	0.1 - 100.0 (4.3)

Figure S75. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.000748 (ANOVA), Q value = 0.083

Table S87. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	406	67.3 (13.1)
subtype1	182	69.8 (12.5)
subtype2	162	66.0 (12.6)
subtype3	62	63.3 (14.8)

Figure S76. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.473 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	216
subtype1	91	91
subtype2	74	89
subtype3	26	36

Figure S77. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0162 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	350	55
subtype1	155	25
subtype2	148	15
subtype3	47	15

Figure S78. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.544 (Fisher's exact test), Q value = 1

Table S90. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	404
subtype1	1	181
subtype2	1	162
subtype3	1	61

Figure S79. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 1.17e-05 (Chi-square test), Q value = 0.0013

Table S91. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	306	50	7	1	36
subtype1	149	28	1	0	1
subtype2	111	15	5	1	28
subtype3	46	7	1	0	7

Figure S80. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.000986 (Chi-square test), Q value = 0.11

Table S92. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	240	68	13	12	2	57	4	9	1
subtype1	111	32	1	3	0	33	1	0	1
subtype2	97	25	10	4	2	19	2	4	0
subtype3	32	11	2	5	0	5	1	5	0

Figure S81. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 3.44e-05 (Chi-square test), Q value = 0.0039

Table S93. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	281	3	25	22
subtype1	148	2	22	2
subtype2	92	1	2	15
subtype3	41	0	1	5

Figure S82. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.73 (ANOVA), Q value = 1

Table S94. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	383	2.0 (4.5)
subtype1	177	1.9 (3.7)
subtype2	149	2.0 (5.0)
subtype3	57	2.5 (5.4)

Figure S83. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

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

Table S95. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: '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	33	114	8	1	22	12	47	32	41	16	1
subtype1	35	0	18	46	6	0	12	4	15	14	25	4	0
subtype2	24	1	10	51	2	1	8	6	23	11	12	9	1
subtype3	7	0	5	17	0	0	2	2	9	7	4	3	0

Figure S84. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S96. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	180	45	182

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.514 (logrank test), Q value = 1

Table S97. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	311	49	0.1 - 135.5 (7.5)
subtype1	98	16	0.9 - 71.7 (8.6)
subtype2	42	8	0.1 - 129.1 (6.7)
subtype3	171	25	0.1 - 135.5 (7.0)

Figure S85. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00978 (ANOVA), Q value = 1

Table S98. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	406	67.3 (13.1)
subtype1	180	69.5 (12.6)
subtype2	45	66.4 (11.8)
subtype3	181	65.3 (13.6)

Figure S86. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.453 (Fisher's exact test), Q value = 1

Table S99. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	216
subtype1	90	90
subtype2	22	23
subtype3	79	103

Figure S87. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.684 (Fisher's exact test), Q value = 1

Table S100. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	350	55
subtype1	152	26
subtype2	41	4
subtype3	157	25

Figure S88. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

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

Table S101. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	404
subtype1	1	179
subtype2	0	45
subtype3	2	180

Figure S89. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 7.22e-06 (Chi-square test), Q value = 0.00082

Table S102. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	306	50	7	1	36
subtype1	146	29	2	0	0
subtype2	30	5	2	0	6
subtype3	130	16	3	1	30

Figure S90. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0334 (Chi-square test), Q value = 1

Table S103. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	240	68	13	12	2	57	4	9	1
subtype1	107	33	1	5	0	32	1	0	0
subtype2	26	5	4	1	1	6	0	2	0
subtype3	107	30	8	6	1	19	3	7	1

Figure S91. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 6.58e-07 (Chi-square test), Q value = 7.6e-05

Table S104. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	281	3	25	22
subtype1	148	2	22	1
subtype2	24	0	2	1
subtype3	109	1	1	20

Figure S92. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.847 (ANOVA), Q value = 1

Table S105. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	383	2.0 (4.5)
subtype1	176	2.0 (3.7)
subtype2	42	1.8 (3.3)
subtype3	165	2.2 (5.4)

Figure S93. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0175 (Chi-square test), Q value = 1

Table S106. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: '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	33	114	8	1	22	12	47	32	41	16	1
subtype1	35	0	18	43	4	0	13	4	15	13	25	5	0
subtype2	7	1	2	13	2	1	2	1	6	1	4	3	0
subtype3	24	0	13	58	2	0	7	7	26	18	12	8	1

Figure S94. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S107. Get Full Table Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	77	79	65

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

P value = 0.571 (logrank test), Q value = 1

Table S108. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	208	32	0.1 - 135.5 (7.0)
subtype1	74	15	0.1 - 124.1 (8.3)
subtype2	73	11	0.1 - 135.5 (7.0)
subtype3	61	6	0.1 - 62.8 (5.2)

Figure S95. 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 = 0.143 (ANOVA), Q value = 1

Table S109. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	220	65.4 (13.2)
subtype1	76	66.2 (13.9)
subtype2	79	66.8 (11.9)
subtype3	65	62.7 (13.8)

Figure S96. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.483 (Fisher's exact test), Q value = 1

Table S110. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	97	124
subtype1	38	39
subtype2	33	46
subtype3	26	39

Figure S97. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.00363 (Fisher's exact test), Q value = 0.38

Table S111. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	192	29
subtype1	64	13
subtype2	76	3
subtype3	52	13

Figure S98. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.75 (Fisher's exact test), Q value = 1

Table S112. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	219
subtype1	0	77
subtype2	1	78
subtype3	1	64

Figure S99. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.776 (Chi-square test), Q value = 1

Table S113. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	155	20	5	1	36
subtype1	57	5	1	1	12
subtype2	55	7	3	0	13
subtype3	43	8	1	0	11

Figure S100. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.312 (Chi-square test), Q value = 1

Table S114. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	129	35	12	7	2	23	3	9	1
subtype1	54	9	3	2	0	6	1	1	1
subtype2	44	14	4	1	2	10	1	3	0
subtype3	31	12	5	4	0	7	1	5	0

Figure S101. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.41 (Chi-square test), Q value = 1

Table S115. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	129	1	2	20
subtype1	43	1	1	8
subtype2	41	0	1	9
subtype3	45	0	0	3

Figure S102. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.14 (ANOVA), Q value = 1

Table S116. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	201	2.0 (4.7)
subtype1	69	1.2 (2.6)
subtype2	71	2.0 (3.6)
subtype3	61	2.8 (7.0)

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

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

P value = 0.776 (Chi-square test), Q value = 1

Table S117. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: '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	15	69	3	1	8	8	33	18	15	11	1
subtype1	12	1	7	28	2	0	2	3	10	4	4	2	1
subtype2	12	0	4	22	0	1	5	3	10	8	6	4	0
subtype3	6	0	4	19	1	0	1	2	13	6	5	5	0

Figure S104. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S118. Get Full Table Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	22	49	62	46	42

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

P value = 0.87 (logrank test), Q value = 1

Table S119. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	208	32	0.1 - 135.5 (7.0)
subtype1	19	3	0.1 - 135.5 (11.2)
subtype2	47	4	0.1 - 62.8 (4.0)
subtype3	59	9	0.1 - 129.1 (6.7)
subtype4	43	12	0.1 - 129.1 (8.3)
subtype5	40	4	0.1 - 105.3 (8.0)

Figure S105. 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 = 0.5 (ANOVA), Q value = 1

Table S120. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	220	65.4 (13.2)
subtype1	22	66.0 (12.9)
subtype2	49	63.8 (13.9)
subtype3	61	65.3 (14.8)
subtype4	46	68.3 (10.6)
subtype5	42	64.0 (13.0)

Figure S106. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.774 (Chi-square test), Q value = 1

Table S121. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	97	124
subtype1	10	12
subtype2	21	28
subtype3	26	36
subtype4	18	28
subtype5	22	20

Figure S107. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.00201 (Chi-square test), Q value = 0.21

Table S122. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	192	29
subtype1	20	2
subtype2	41	8
subtype3	46	16
subtype4	45	1
subtype5	40	2

Figure S108. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.131 (Chi-square test), Q value = 1

Table S123. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	219
subtype1	0	22
subtype2	2	47
subtype3	0	62
subtype4	0	46
subtype5	0	42

Figure S109. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.907 (Chi-square test), Q value = 1

Table S124. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	155	20	5	1	36
subtype1	15	3	0	0	4
subtype2	33	6	1	0	8
subtype3	47	5	1	1	7
subtype4	34	2	1	0	8
subtype5	26	4	2	0	9

Figure S110. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.173 (Chi-square test), Q value = 1

Table S125. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	129	35	12	7	2	23	3	9	1
subtype1	11	6	0	0	0	3	1	1	0
subtype2	26	10	3	1	0	3	1	5	0
subtype3	40	5	2	4	1	8	1	1	0
subtype4	33	4	2	0	1	5	0	0	1
subtype5	19	10	5	2	0	4	0	2	0

Figure S111. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

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

P value = 0.715 (Chi-square test), Q value = 1

Table S126. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	129	1	2	20
subtype1	12	0	0	4
subtype2	32	0	0	5
subtype3	37	0	1	6
subtype4	19	0	0	3
subtype5	29	1	1	2

Figure S112. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0762 (ANOVA), Q value = 1

Table S127. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	201	2.0 (4.7)
subtype1	18	4.7 (11.8)
subtype2	43	2.4 (4.3)
subtype3	55	1.6 (3.0)
subtype4	45	1.1 (2.1)
subtype5	40	1.9 (3.1)

Figure S113. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

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

P value = 0.0824 (Chi-square test), Q value = 1

Table S128. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: '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	15	69	3	1	8	8	33	18	15	11	1
subtype1	3	0	2	5	0	0	1	0	5	3	3	0	0
subtype2	5	0	5	14	1	0	0	2	8	5	3	4	0
subtype3	11	0	3	23	2	0	1	1	8	5	5	1	1
subtype4	4	0	3	21	0	0	0	2	5	5	1	2	0
subtype5	7	1	2	6	0	1	6	3	7	0	3	4	0

Figure S114. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = COAD-TP.mergedcluster.txt
Clinical data file = COAD-TP.clin.merged.picked.txt
Number of patients = 422
Number of clustering approaches = 12
Number of selected clinical features = 11
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Fisher's exact test

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

Chi-square test

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

Q value calculation

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

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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