Correlation between aggregated molecular cancer subtypes and selected clinical features

Colon/Rectal 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/C1WW7FP3

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 588 patients, 20 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE', '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 correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'DISTANT.METASTASIS', 'LYMPH.NODE.METASTASIS', 'COMPLETENESS.OF.RESECTION', and 'NEOPLASM.DISEASESTAGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'DISTANT.METASTASIS', 'LYMPH.NODE.METASTASIS', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and '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 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 20 significant findings detected.


Clinical Features	Time to Death	AGE	PRIMARY SITE OF DISEASE	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	Chi-square test	ANOVA	ANOVA	Chi-square test
mRNA CNMF subtypes	0.214 (1.00)	0.783 (1.00)	0.0247 (1.00)	0.0611 (1.00)	1.51e-06 (0.000187)	0.396 (1.00)	0.0146 (1.00)	0.0825 (1.00)	0.0186 (1.00)	0.0369 (1.00)		0.0121 (1.00)
mRNA cHierClus subtypes	0.21 (1.00)	0.503 (1.00)	0.104 (1.00)	0.187 (1.00)	2.27e-06 (0.000279)	1 (1.00)	0.165 (1.00)	0.268 (1.00)	0.108 (1.00)	0.151 (1.00)		0.0999 (1.00)
Copy Number Ratio CNMF subtypes	0.852 (1.00)	0.628 (1.00)	7.44e-05 (0.00901)	0.827 (1.00)	4.39e-13 (5.8e-11)	0.216 (1.00)	0.205 (1.00)	0.00159 (0.18)	0.0789 (1.00)	0.0996 (1.00)		0.0809 (1.00)
METHLYATION CNMF	0.955 (1.00)	0.00307 (0.338)	0.00677 (0.704)	0.0424 (1.00)	0.00955 (0.984)	0.0622 (1.00)	0.638 (1.00)	0.0133 (1.00)	0.113 (1.00)	0.574 (1.00)		0.614 (1.00)
RPPA CNMF subtypes	0.246 (1.00)	0.459 (1.00)	0.966 (1.00)	0.854 (1.00)	0.325 (1.00)	0.903 (1.00)	0.00563 (0.591)	0.38 (1.00)	0.0412 (1.00)	0.243 (1.00)		0.0386 (1.00)
RPPA cHierClus subtypes	0.0161 (1.00)	0.457 (1.00)	0.337 (1.00)	0.332 (1.00)	0.165 (1.00)	0.142 (1.00)	0.00379 (0.413)	0.0562 (1.00)	0.0865 (1.00)	0.0972 (1.00)		0.0102 (1.00)
RNAseq CNMF subtypes	0.576 (1.00)	0.655 (1.00)	0.00258 (0.286)	0.966 (1.00)	0.000743 (0.0854)	0.17 (1.00)	0.245 (1.00)	0.0544 (1.00)	0.0267 (1.00)	0.00549 (0.582)		0.0421 (1.00)
RNAseq cHierClus subtypes	0.847 (1.00)	0.162 (1.00)	0.000781 (0.089)	0.323 (1.00)	0.00046 (0.0538)	0.739 (1.00)	0.594 (1.00)	0.0733 (1.00)	0.501 (1.00)	0.0109 (1.00)		0.202 (1.00)
MIRSEQ CNMF	0.517 (1.00)	0.00518 (0.554)	0.0828 (1.00)	0.254 (1.00)	0.000465 (0.0539)	0.0599 (1.00)	2.96e-08 (3.85e-06)	3.92e-07 (4.91e-05)	8.21e-08 (1.05e-05)	0.016 (1.00)		0.00022 (0.0264)
MIRSEQ CHIERARCHICAL	0.764 (1.00)	0.00244 (0.273)	0.81 (1.00)	0.376 (1.00)	0.38 (1.00)	0.365 (1.00)	1.8e-07 (2.28e-05)	0.000456 (0.0538)	3.73e-07 (4.7e-05)	0.535 (1.00)		0.005 (0.54)
MIRseq Mature CNMF subtypes	0.992 (1.00)	0.499 (1.00)	1.86e-08 (2.43e-06)	0.753 (1.00)	5.73e-08 (7.39e-06)	1 (1.00)	0.337 (1.00)	0.131 (1.00)	0.33 (1.00)	0.0591 (1.00)		0.403 (1.00)
MIRseq Mature cHierClus subtypes	0.7 (1.00)	0.72 (1.00)	0.000319 (0.0379)	0.722 (1.00)	2.82e-05 (0.00344)	0.227 (1.00)	0.524 (1.00)	0.18 (1.00)	0.056 (1.00)	0.389 (1.00)		0.269 (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	46	62	72	42

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.214 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	15	0.9 - 52.0 (4.0)
subtype1	26	1	1.0 - 30.0 (1.5)
subtype2	35	10	1.0 - 52.0 (10.9)
subtype3	32	2	0.9 - 49.9 (1.0)
subtype4	26	2	0.9 - 52.0 (13.0)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.783 (ANOVA), Q value = 1

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

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

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

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

P value = 0.0247 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

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

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.51e-06 (Chi-square test), Q value = 0.00019

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	127	24	58	7
subtype1	26	13	4	2
subtype2	36	1	25	0
subtype3	44	1	21	2
subtype4	21	9	8	3

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

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

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

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

'mRNA CNMF subtypes' versus 'DISTANT.METASTASIS'

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

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

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

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

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

P value = 0.0825 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N1A	N1B	N2	N2A
ALL	136	41	1	1	42	1
subtype1	30	6	0	0	10	0
subtype2	28	16	1	0	17	0
subtype3	46	12	0	0	13	1
subtype4	32	7	0	1	2	0

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

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

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

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

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

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

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

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

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

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

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

Figure S11. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

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

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.21 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	15	0.9 - 52.0 (4.0)
subtype1	13	1	0.9 - 17.0 (1.0)
subtype2	32	3	0.9 - 52.0 (12.0)
subtype3	38	3	1.0 - 41.0 (1.5)
subtype4	36	8	0.9 - 41.0 (10.7)

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

'mRNA cHierClus subtypes' versus 'AGE'

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

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

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

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

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

P value = 0.104 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

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

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

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

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.27e-06 (Chi-square test), Q value = 0.00028

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	127	24	58	7
subtype1	31	1	11	1
subtype2	30	2	14	2
subtype3	31	20	10	3
subtype4	35	1	23	1

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

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

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

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

'mRNA cHierClus subtypes' versus 'DISTANT.METASTASIS'

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

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

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

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

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

P value = 0.268 (Chi-square test), Q value = 1

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A
ALL	136	41	1	1	42	1
subtype1	29	10	0	0	5	1
subtype2	34	8	0	0	9	0
subtype3	44	11	0	1	10	0
subtype4	29	12	1	0	18	0

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

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

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

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

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

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

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

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

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

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

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

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

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

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

Cluster Labels	1	2	3	4
Number of samples	200	234	100	41

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

P value = 0.852 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	451	60	0.1 - 135.5 (7.0)
subtype1	153	22	0.1 - 129.1 (8.0)
subtype2	192	22	0.1 - 135.5 (6.0)
subtype3	75	12	0.2 - 112.7 (6.6)
subtype4	31	4	0.3 - 87.8 (12.6)

Figure S23. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.628 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	574	66.6 (12.7)
subtype1	200	66.8 (11.2)
subtype2	233	66.8 (14.1)
subtype3	100	66.8 (12.1)
subtype4	41	64.1 (13.4)

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

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

P value = 7.44e-05 (Fisher's exact test), Q value = 0.009

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	412	159
subtype1	124	72
subtype2	192	42
subtype3	66	34
subtype4	30	11

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.827 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	266	309
subtype1	88	112
subtype2	113	121
subtype3	47	53
subtype4	18	23

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

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

P value = 4.39e-13 (Chi-square test), Q value = 5.8e-11

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	357	54	143	13
subtype1	121	3	72	0
subtype2	145	46	32	9
subtype3	61	5	28	4
subtype4	30	0	11	0

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

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

P value = 0.216 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	566
subtype1	3	197
subtype2	2	232
subtype3	2	98
subtype4	2	39

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

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

P value = 0.205 (Chi-square test), Q value = 1

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

nPatients	M0	M1	M1A	M1B	MX
ALL	436	71	9	1	49
subtype1	143	35	4	0	16
subtype2	189	16	2	1	21
subtype3	73	15	2	0	10
subtype4	31	5	1	0	2

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

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

P value = 0.00159 (Chi-square test), Q value = 0.18

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	327	104	17	17	3	81	6	16	2
subtype1	96	52	8	4	0	31	2	5	1
subtype2	156	34	4	7	1	21	4	7	0
subtype3	51	14	4	2	2	22	0	4	1
subtype4	24	4	1	4	0	7	0	0	0

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

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

P value = 0.0789 (Chi-square test), Q value = 1

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	410	5	36	25
subtype1	140	1	17	6
subtype2	174	3	7	12
subtype3	68	0	10	3
subtype4	28	1	2	4

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0996 (ANOVA), Q value = 1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	541	2.2 (4.7)
subtype1	192	2.3 (4.9)
subtype2	219	1.7 (4.2)
subtype3	94	3.1 (6.0)
subtype4	36	1.9 (2.8)

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

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

P value = 0.0809 (Chi-square test), Q value = 1

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: '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	98	1	41	154	10	3	27	22	70	47	57	25	1
subtype1	27	0	8	49	3	1	10	8	33	17	26	12	0
subtype2	48	1	20	72	6	0	10	7	26	15	13	7	1
subtype3	17	0	11	18	1	2	6	4	10	12	14	4	0
subtype4	6	0	2	15	0	0	1	3	1	3	4	2	0

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S37. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	145	114	91

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.955 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	326	44	0.1 - 135.5 (7.0)
subtype1	136	20	0.1 - 135.5 (7.5)
subtype2	105	16	0.1 - 129.1 (7.6)
subtype3	85	8	0.1 - 102.4 (6.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00307 (ANOVA), Q value = 0.34

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

	nPatients	Mean (Std.Dev)
ALL	349	64.9 (13.0)
subtype1	145	65.4 (12.6)
subtype2	114	67.2 (13.0)
subtype3	90	61.1 (13.0)

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

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

P value = 0.00677 (Fisher's exact test), Q value = 0.7

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	255	93
subtype1	96	49
subtype2	95	19
subtype3	64	25

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0424 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	156	194
subtype1	62	83
subtype2	61	53
subtype3	33	58

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00955 (Chi-square test), Q value = 0.98

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	225	30	87	6
subtype1	90	6	47	2
subtype2	82	13	16	3
subtype3	53	11	24	1

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

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

P value = 0.0622 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	8	342
subtype1	4	141
subtype2	0	114
subtype3	4	87

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

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.638 (Chi-square test), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	247	37	8	1	50
subtype1	97	19	5	0	22
subtype2	84	10	1	1	15
subtype3	66	8	2	0	13

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 0.0133 (Chi-square test), Q value = 1

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	192	60	16	16	3	37	5	16	3
subtype1	73	24	11	7	2	16	1	6	3
subtype2	71	18	2	4	0	17	1	1	0
subtype3	48	18	3	5	1	4	3	9	0

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

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

P value = 0.113 (Chi-square test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	221	4	6	24
subtype1	92	1	2	8
subtype2	67	2	4	13
subtype3	62	1	0	3

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.574 (ANOVA), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	316	2.2 (4.9)
subtype1	133	2.3 (5.4)
subtype2	101	1.9 (3.8)
subtype3	82	2.6 (5.1)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.614 (Chi-square test), Q value = 1

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: '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	48	1	25	93	6	3	12	19	49	27	24	24	1
subtype1	16	0	8	37	2	2	5	10	24	10	10	13	0
subtype2	21	1	9	33	3	1	6	2	12	8	8	5	1
subtype3	11	0	8	23	1	0	1	7	13	9	6	6	0

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

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	118	186	35	122

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.246 (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	372	51	0.1 - 135.5 (7.1)
subtype1	91	5	0.1 - 119.0 (6.0)
subtype2	145	22	0.1 - 135.5 (7.4)
subtype3	28	3	0.1 - 61.9 (9.1)
subtype4	108	21	0.1 - 124.1 (7.9)

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 = 0.459 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	460	66.8 (12.7)
subtype1	118	67.4 (12.5)
subtype2	185	67.1 (12.2)
subtype3	35	67.9 (11.5)
subtype4	122	65.2 (13.8)

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

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

P value = 0.966 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	331	130
subtype1	83	35
subtype2	133	53
subtype3	26	9
subtype4	89	33

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.854 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	215	246
subtype1	55	63
subtype2	90	96
subtype3	17	18
subtype4	53	69

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.325 (Chi-square test), Q value = 1

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	291	40	117	10
subtype1	69	14	29	4
subtype2	125	8	48	4
subtype3	21	5	8	1
subtype4	76	13	32	1

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

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

P value = 0.903 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	8	453
subtype1	3	115
subtype2	3	183
subtype3	0	35
subtype4	2	120

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

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.00563 (Chi-square test), Q value = 0.59

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

nPatients	M0	M1	M1A	M1B	MX
ALL	357	54	8	1	36
subtype1	92	14	5	0	5
subtype2	135	33	0	1	16
subtype3	32	1	0	0	2
subtype4	98	6	3	0	13

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

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

P value = 0.38 (Chi-square test), Q value = 1

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	260	86	16	13	2	61	7	12	2
subtype1	58	21	8	6	0	15	3	5	0
subtype2	98	42	7	3	1	27	3	4	1
subtype3	26	3	0	1	0	4	0	1	0
subtype4	78	20	1	3	1	15	1	2	1

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

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

P value = 0.0412 (Chi-square test), Q value = 1

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	336	2	28	18
subtype1	89	1	6	7
subtype2	119	0	19	6
subtype3	33	0	1	0
subtype4	95	1	2	5

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.243 (ANOVA), Q value = 1

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	435	2.1 (4.6)
subtype1	111	2.7 (5.9)
subtype2	175	2.2 (4.4)
subtype3	35	1.4 (3.1)
subtype4	114	1.6 (3.7)

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

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0386 (Chi-square test), Q value = 1

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	70	1	32	132	9	2	25	16	61	38	42	19	1
subtype1	12	0	11	30	3	0	3	4	21	11	9	8	0
subtype2	27	0	12	46	4	1	12	7	27	11	27	7	1
subtype3	9	1	2	14	0	0	2	0	2	4	1	0	0
subtype4	22	0	7	42	2	1	8	5	11	12	5	4	0

Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S61. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

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

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0161 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	372	51	0.1 - 135.5 (7.1)
subtype1	70	3	0.1 - 119.0 (5.9)
subtype2	193	27	0.1 - 135.5 (7.8)
subtype3	84	13	0.1 - 124.1 (6.9)
subtype4	25	8	0.1 - 105.3 (9.4)

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

'RPPA cHierClus subtypes' versus 'AGE'

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

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

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

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

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

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

Table S64. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.332 (Fisher's exact test), Q value = 1

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

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

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	291	40	117	10
subtype1	46	11	19	3
subtype2	162	15	64	6
subtype3	56	9	29	0
subtype4	27	5	5	1

Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.00379 (Chi-square test), Q value = 0.41

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

Figure S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 0.0562 (Chi-square test), Q value = 1

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	260	86	16	13	2	61	7	12	2
subtype1	40	13	2	5	0	9	4	4	0
subtype2	133	57	11	4	1	36	3	4	1
subtype3	66	12	1	2	1	9	0	2	1
subtype4	21	4	2	2	0	7	0	2	0

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

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

P value = 0.0865 (Chi-square test), Q value = 1

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	336	2	28	18
subtype1	62	1	1	3
subtype2	169	1	23	10
subtype3	77	0	1	3
subtype4	28	0	3	2

Figure S64. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

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

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

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

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

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0102 (Chi-square test), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	70	1	32	132	9	2	25	16	61	38	42	19	1
subtype1	12	0	2	22	1	0	1	4	14	7	3	9	0
subtype2	34	1	22	62	6	1	19	8	33	17	33	7	1
subtype3	22	0	5	33	1	1	4	4	8	7	4	3	0
subtype4	2	0	3	15	1	0	1	0	6	7	2	0	0

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S73. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	38	83	56	66

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.576 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	225	27	0.1 - 129.1 (6.5)
subtype1	33	2	0.1 - 105.3 (7.6)
subtype2	78	12	0.1 - 129.1 (8.0)
subtype3	52	3	0.2 - 121.1 (3.2)
subtype4	62	10	0.1 - 103.0 (7.1)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.655 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	242	65.0 (13.4)
subtype1	38	67.2 (12.4)
subtype2	83	65.2 (12.9)
subtype3	56	63.8 (12.7)
subtype4	65	64.3 (15.3)

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

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

P value = 0.00258 (Fisher's exact test), Q value = 0.29

Table S76. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	192	49
subtype1	28	8
subtype2	59	24
subtype3	43	13
subtype4	62	4

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.966 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	115	128
subtype1	18	20
subtype2	39	44
subtype3	28	28
subtype4	30	36

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000743 (Chi-square test), Q value = 0.085

Table S78. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	168	24	47	2
subtype1	26	2	8	0
subtype2	58	1	24	0
subtype3	33	10	12	1
subtype4	51	11	3	1

Figure S71. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

Table S79. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	238
subtype1	2	36
subtype2	1	82
subtype3	2	54
subtype4	0	66

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

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

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

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	167	23	6	1	42
subtype1	25	5	0	0	8
subtype2	55	7	5	0	16
subtype3	38	8	1	0	7
subtype4	49	3	0	1	11

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

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

P value = 0.0544 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	143	33	13	10	1	24	2	14	3
subtype1	22	9	2	2	0	2	0	1	0
subtype2	48	11	7	3	1	8	0	3	2
subtype3	25	6	2	2	0	10	2	8	1
subtype4	48	7	2	3	0	4	0	2	0

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

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

P value = 0.0267 (Chi-square test), Q value = 1

Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	151	2	2	25
subtype1	24	1	2	2
subtype2	46	0	0	9
subtype3	41	0	0	3
subtype4	40	1	0	11

Figure S75. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.00549 (ANOVA), Q value = 0.58

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	214	2.2 (5.1)
subtype1	35	1.6 (3.9)
subtype2	71	1.6 (2.9)
subtype3	52	4.4 (8.6)
subtype4	56	1.3 (2.9)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0421 (Chi-square test), Q value = 1

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: '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	43	19	65	4	2	9	9	29	19	14	18	1
subtype1	9	3	7	0	0	4	3	5	0	3	4	0
subtype2	16	5	19	1	2	2	4	12	6	4	8	0
subtype3	4	2	16	1	0	1	1	6	10	5	5	0
subtype4	14	9	23	2	0	2	1	6	3	2	1	1

Figure S77. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S85. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	95	67	81

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.847 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	225	27	0.1 - 129.1 (6.5)
subtype1	88	7	0.1 - 121.1 (4.8)
subtype2	62	9	0.1 - 103.0 (7.1)
subtype3	75	11	0.1 - 129.1 (8.3)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.162 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	242	65.0 (13.4)
subtype1	95	63.0 (13.6)
subtype2	66	65.5 (14.2)
subtype3	81	66.8 (12.4)

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

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

P value = 0.000781 (Fisher's exact test), Q value = 0.089

Table S88. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	192	49
subtype1	73	21
subtype2	62	4
subtype3	57	24

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

'RNAseq cHierClus subtypes' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	115	128
subtype1	43	52
subtype2	37	30
subtype3	35	46

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00046 (Chi-square test), Q value = 0.054

Table S90. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	168	24	47	2
subtype1	61	12	19	2
subtype2	51	11	4	0
subtype3	56	1	24	0

Figure S82. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.739 (Fisher's exact test), Q value = 1

Table S91. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	238
subtype1	3	92
subtype2	1	66
subtype3	1	80

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

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.594 (Chi-square test), Q value = 1

Table S92. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	167	23	6	1	42
subtype1	62	10	4	0	17
subtype2	46	6	0	1	13
subtype3	59	7	2	0	12

Figure S84. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

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

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	143	33	13	10	1	24	2	14	3
subtype1	43	13	8	6	0	13	2	9	1
subtype2	48	9	2	3	0	4	0	1	0
subtype3	52	11	3	1	1	7	0	4	2

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

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

P value = 0.501 (Chi-square test), Q value = 1

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	151	2	2	25
subtype1	67	0	0	7
subtype2	39	1	1	9
subtype3	45	1	1	9

Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0109 (ANOVA), Q value = 1

Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	214	2.2 (5.1)
subtype1	85	3.5 (7.2)
subtype2	59	1.3 (2.8)
subtype3	70	1.5 (2.9)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.202 (Chi-square test), Q value = 1

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: '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	43	19	65	4	2	9	9	29	19	14	18	1
subtype1	10	8	22	1	0	2	2	15	12	6	10	0
subtype2	15	7	21	2	0	3	3	6	1	4	3	1
subtype3	18	4	22	1	2	4	4	8	6	4	5	0

Figure S88. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S97. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	245	222	83

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.517 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	422	59	0.1 - 135.5 (7.0)
subtype1	138	18	0.2 - 72.1 (6.5)
subtype2	207	32	0.1 - 135.5 (8.0)
subtype3	77	9	0.1 - 100.0 (4.8)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00518 (ANOVA), Q value = 0.55

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

	nPatients	Mean (Std.Dev)
ALL	549	66.8 (12.7)
subtype1	245	68.7 (12.1)
subtype2	221	65.4 (12.6)
subtype3	83	64.8 (14.1)

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

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

P value = 0.0828 (Fisher's exact test), Q value = 1

Table S100. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	406	140
subtype1	177	66
subtype2	160	61
subtype3	69	13

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.254 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	257	293
subtype1	120	125
subtype2	105	117
subtype3	32	51

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.000465 (Chi-square test), Q value = 0.054

Table S102. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	350	55	127	10
subtype1	152	24	56	7
subtype2	147	13	60	1
subtype3	51	18	11	2

Figure S93. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.0599 (Fisher's exact test), Q value = 1

Table S103. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	541
subtype1	2	243
subtype2	3	219
subtype3	4	79

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

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 2.96e-08 (Chi-square test), Q value = 3.8e-06

Table S104. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	413	68	9	1	50
subtype1	200	40	1	0	1
subtype2	157	19	6	1	35
subtype3	56	9	2	0	14

Figure S95. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 3.92e-07 (Chi-square test), Q value = 4.9e-05

Table S105. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	314	98	17	15	3	78	6	14	3
subtype1	150	43	2	3	0	46	1	0	0
subtype2	127	40	12	7	3	22	1	7	2
subtype3	37	15	3	5	0	10	4	7	1

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

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

P value = 8.21e-08 (Chi-square test), Q value = 1.1e-05

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	383	5	36	25
subtype1	200	3	32	2
subtype2	125	2	4	19
subtype3	58	0	0	4

Figure S97. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.016 (ANOVA), Q value = 1

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	515	2.2 (4.7)
subtype1	240	2.1 (4.3)
subtype2	196	1.7 (3.5)
subtype3	79	3.5 (7.5)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00022 (Chi-square test), Q value = 0.026

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	94	1	41	147	10	2	28	17	67	44	55	23	1
subtype1	51	0	24	62	6	0	16	4	20	18	36	4	0
subtype2	36	1	11	65	4	2	10	10	34	14	16	11	1
subtype3	7	0	6	20	0	0	2	3	13	12	3	8	0

Figure S99. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S109. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	236	33	281

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.764 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	422	59	0.1 - 135.5 (7.0)
subtype1	131	17	0.6 - 72.1 (8.1)
subtype2	30	5	0.5 - 112.7 (9.8)
subtype3	261	37	0.1 - 135.5 (6.9)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00244 (ANOVA), Q value = 0.27

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

	nPatients	Mean (Std.Dev)
ALL	549	66.8 (12.7)
subtype1	236	68.9 (12.2)
subtype2	33	67.2 (11.3)
subtype3	280	65.0 (13.1)

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

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

P value = 0.81 (Fisher's exact test), Q value = 1

Table S112. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	406	140
subtype1	177	57
subtype2	25	8
subtype3	204	75

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.376 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	257	293
subtype1	115	121
subtype2	18	15
subtype3	124	157

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.38 (Chi-square test), Q value = 1

Table S114. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	350	55	127	10
subtype1	150	26	47	7
subtype2	22	3	7	1
subtype3	178	26	73	2

Figure S104. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

Table S115. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	9	541
subtype1	2	234
subtype2	0	33
subtype3	7	274

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

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 1.8e-07 (Chi-square test), Q value = 2.3e-05

Table S116. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	413	68	9	1	50
subtype1	192	38	2	0	1
subtype2	25	4	0	0	3
subtype3	196	26	7	1	46

Figure S106. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

P value = 0.000456 (Chi-square test), Q value = 0.054

Table S117. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	314	98	17	15	3	78	6	14	3
subtype1	140	44	2	5	0	43	1	0	0
subtype2	24	3	1	0	1	4	0	0	0
subtype3	150	51	14	10	2	31	5	14	3

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

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

P value = 3.73e-07 (Chi-square test), Q value = 4.7e-05

Table S118. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	383	5	36	25
subtype1	195	2	29	1
subtype2	21	0	3	1
subtype3	167	3	4	23

Figure S108. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.535 (ANOVA), Q value = 1

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	515	2.2 (4.7)
subtype1	232	2.2 (4.7)
subtype2	32	1.3 (3.3)
subtype3	251	2.3 (4.9)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.005 (Chi-square test), Q value = 0.54

Table S120. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	94	1	41	147	10	2	28	17	67	44	55	23	1
subtype1	49	0	21	58	4	0	17	4	22	17	33	5	0
subtype2	9	0	2	11	2	0	1	0	4	0	4	0	0
subtype3	36	1	18	78	4	2	10	13	41	27	18	18	1

Figure S110. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S121. Get Full Table Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	105	106	85

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

P value = 0.992 (logrank test), Q value = 1

Table S122. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	276	39	0.1 - 135.5 (7.0)
subtype1	100	16	0.1 - 124.1 (8.2)
subtype2	97	16	0.1 - 135.5 (7.0)
subtype3	79	7	0.1 - 121.1 (5.2)

Figure S111. 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 = 0.499 (ANOVA), Q value = 1

Table S123. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	295	65.3 (13.0)
subtype1	104	66.0 (13.8)
subtype2	106	65.6 (11.6)
subtype3	85	63.9 (13.6)

Figure S112. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1.86e-08 (Fisher's exact test), Q value = 2.4e-06

Table S124. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	221	73
subtype1	94	11
subtype2	58	47
subtype3	69	15

Figure S113. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.753 (Fisher's exact test), Q value = 1

Table S125. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	131	165
subtype1	49	56
subtype2	47	59
subtype3	35	50

Figure S114. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

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

P value = 5.73e-08 (Chi-square test), Q value = 7.4e-06

Table S126. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	192	29	71	2
subtype1	78	16	11	0
subtype2	57	1	46	1
subtype3	57	12	14	1

Figure S115. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

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

Table S127. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	289
subtype1	2	103
subtype2	3	103
subtype3	2	83

Figure S116. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.337 (Chi-square test), Q value = 1

Table S128. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	205	27	7	1	50
subtype1	72	7	1	1	22
subtype2	79	8	3	0	15
subtype3	54	12	3	0	13

Figure S117. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

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

Table S129. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	162	51	15	10	3	32	5	14	3
subtype1	66	20	4	5	0	7	1	1	1
subtype2	55	18	5	2	3	14	1	6	2
subtype3	41	13	6	3	0	11	3	7	0

Figure S118. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

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

P value = 0.33 (Chi-square test), Q value = 1

Table S130. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	174	2	4	23
subtype1	60	2	1	12
subtype2	56	0	2	6
subtype3	58	0	1	5

Figure S119. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0591 (ANOVA), Q value = 1

Table S131. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	265	2.2 (4.8)
subtype1	93	1.4 (3.5)
subtype2	94	2.1 (3.5)
subtype3	78	3.2 (6.9)

Figure S120. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

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

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

Table S132. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	41	1	17	85	5	2	10	13	44	26	19	18	1
subtype1	16	1	8	31	2	2	5	5	18	4	5	4	1
subtype2	17	0	4	29	2	0	4	7	14	12	6	6	0
subtype3	8	0	5	25	1	0	1	1	12	10	8	8	0

Figure S121. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S133. Get Full Table Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	30	24	62	65	46	69

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

P value = 0.7 (logrank test), Q value = 1

Table S134. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	276	39	0.1 - 135.5 (7.0)
subtype1	26	3	0.1 - 135.5 (8.2)
subtype2	23	2	0.1 - 129.1 (4.7)
subtype3	57	16	0.1 - 129.1 (11.0)
subtype4	62	6	0.2 - 121.1 (5.1)
subtype5	42	3	0.3 - 119.0 (8.0)
subtype6	66	9	0.1 - 124.1 (6.0)

Figure S122. 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 = 0.72 (ANOVA), Q value = 1

Table S135. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	295	65.3 (13.0)
subtype1	30	66.4 (12.0)
subtype2	24	63.6 (12.9)
subtype3	62	66.8 (12.2)
subtype4	65	63.8 (12.6)
subtype5	46	66.4 (12.8)
subtype6	68	64.5 (14.6)

Figure S123. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.000319 (Chi-square test), Q value = 0.038

Table S136. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	221	73
subtype1	21	9
subtype2	12	12
subtype3	41	20
subtype4	46	18
subtype5	37	9
subtype6	64	5

Figure S124. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.722 (Chi-square test), Q value = 1

Table S137. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	131	165
subtype1	15	15
subtype2	10	14
subtype3	22	40
subtype4	30	35
subtype5	22	24
subtype6	32	37

Figure S125. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

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

P value = 2.82e-05 (Chi-square test), Q value = 0.0034

Table S138. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	192	29	71	2
subtype1	19	2	9	0
subtype2	11	1	11	1
subtype3	40	1	20	0
subtype4	38	8	18	0
subtype5	36	1	9	0
subtype6	48	16	4	1

Figure S126. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.227 (Chi-square test), Q value = 1

Table S139. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	289
subtype1	0	30
subtype2	1	23
subtype3	3	59
subtype4	3	62
subtype5	0	46
subtype6	0	69

Figure S127. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Table S140. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	M1B	MX
ALL	205	27	7	1	50
subtype1	21	3	0	0	6
subtype2	12	6	1	0	4
subtype3	47	3	1	0	9
subtype4	43	7	2	0	11
subtype5	30	3	2	0	11
subtype6	52	5	1	1	9

Figure S128. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'DISTANT.METASTASIS'

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

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

Table S141. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	162	51	15	10	3	32	5	14	3
subtype1	17	8	1	0	0	3	1	0	0
subtype2	7	6	2	1	1	4	0	2	1
subtype3	37	13	0	1	1	6	0	3	1
subtype4	29	10	4	3	0	7	3	7	1
subtype5	25	8	5	1	0	6	0	1	0
subtype6	47	6	3	4	1	6	1	1	0

Figure S129. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'LYMPH.NODE.METASTASIS'

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

P value = 0.056 (Chi-square test), Q value = 1

Table S142. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	174	2	4	23
subtype1	19	0	0	4
subtype2	10	0	2	0
subtype3	25	1	0	2
subtype4	42	0	0	3
subtype5	35	1	1	5
subtype6	43	0	1	9

Figure S130. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.389 (ANOVA), Q value = 1

Table S143. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	265	2.2 (4.8)
subtype1	24	3.2 (10.2)
subtype2	19	2.7 (3.4)
subtype3	60	2.1 (4.6)
subtype4	61	2.9 (4.7)
subtype5	42	1.5 (2.5)
subtype6	59	1.4 (2.8)

Figure S131. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

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

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

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	41	1	17	85	5	2	10	13	44	26	19	18	1
subtype1	5	0	2	7	1	0	2	1	6	2	3	1	0
subtype2	3	0	1	2	0	1	0	2	3	2	4	3	0
subtype3	7	0	2	23	1	1	1	4	9	6	2	2	0
subtype4	5	0	4	17	1	0	0	2	11	10	3	7	0
subtype5	9	1	2	11	0	0	5	2	7	3	2	4	0
subtype6	12	0	6	25	2	0	2	2	8	3	5	1	1

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

Methods & Data

Input

Cluster data file = COADREAD-TP.mergedcluster.txt
Clinical data file = COADREAD-TP.clin.merged.picked.txt
Number of patients = 588
Number of clustering approaches = 12
Number of selected clinical features = 12
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Fisher's exact test

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

Chi-square test

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

Q value calculation

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

Download Results

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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