Colon/Rectal Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Colon/Rectal Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

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

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 10 different clustering approaches and 12 clinical features across 588 patients, 16 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 'PRIMARY.SITE.OF.DISEASE', 'HISTOLOGICAL.TYPE', and 'PATHOLOGY.N'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that correlate to 'PATHOLOGICSPREAD(M)'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE' and 'PATHOLOGY.N'.
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', 'PATHOLOGICSPREAD(M)', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGICSPREAD(M)' and 'COMPLETENESS.OF.RESECTION'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 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, 16 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
Time to Death	logrank test	0.517 (1.00)	0.76 (1.00)	0.85 (1.00)	0.964 (1.00)	0.365 (1.00)	0.442 (1.00)	0.567 (1.00)	0.847 (1.00)	0.525 (1.00)	0.766 (1.00)
AGE	ANOVA	0.781 (1.00)	0.246 (1.00)	0.628 (1.00)	0.00171 (0.182)	0.179 (1.00)	0.0169 (1.00)	0.655 (1.00)	0.162 (1.00)	0.00518 (0.507)	0.00244 (0.254)
PRIMARY SITE OF DISEASE	Fisher's exact test	0.0102 (0.936)	0.068 (1.00)	7.44e-05 (0.00841)	0.00302 (0.308)	0.841 (1.00)	0.777 (1.00)	0.00258 (0.266)	0.000781 (0.0844)	0.0828 (1.00)	0.81 (1.00)
GENDER	Fisher's exact test	0.0589 (1.00)	0.605 (1.00)	0.804 (1.00)	0.0473 (1.00)	0.0588 (1.00)	0.17 (1.00)	0.966 (1.00)	0.323 (1.00)	0.255 (1.00)	0.417 (1.00)
HISTOLOGICAL TYPE	Chi-square test	7.14e-07 (8.14e-05)	7.97e-08 (9.4e-06)	4.28e-13 (5.14e-11)	0.00521 (0.507)	0.263 (1.00)	0.929 (1.00)	0.000743 (0.0809)	0.00046 (0.051)	0.000466 (0.0512)	0.382 (1.00)
PATHOLOGY T	Chi-square test	0.185 (1.00)	0.825 (1.00)	0.934 (1.00)	0.466 (1.00)	0.354 (1.00)	0.577 (1.00)	0.162 (1.00)	0.0662 (1.00)	0.0523 (1.00)	0.0053 (0.509)
PATHOLOGY N	Chi-square test	0.00761 (0.708)	0.325 (1.00)	0.00155 (0.165)	0.497 (1.00)	0.0549 (1.00)	0.0741 (1.00)	0.000372 (0.0417)	0.00474 (0.469)	0.019 (1.00)	0.236 (1.00)
PATHOLOGICSPREAD(M)	Chi-square test	0.00331 (0.334)	0.347 (1.00)	0.2 (1.00)	0.748 (1.00)	0.0335 (1.00)	0.00199 (0.208)	0.245 (1.00)	0.594 (1.00)	3.15e-08 (3.75e-06)	1.91e-07 (2.21e-05)
TUMOR STAGE	Chi-square test	0.0188 (1.00)	0.476 (1.00)	0.00415 (0.415)	0.39 (1.00)	0.297 (1.00)	0.0947 (1.00)	0.0216 (1.00)	0.0699 (1.00)	0.0489 (1.00)	0.0748 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.396 (1.00)	0.482 (1.00)	0.216 (1.00)	0.0619 (1.00)	0.348 (1.00)	0.22 (1.00)	0.17 (1.00)	0.739 (1.00)	0.0599 (1.00)	0.365 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.0194 (1.00)	0.204 (1.00)	0.0654 (1.00)	0.08 (1.00)	0.0794 (1.00)	0.0272 (1.00)	0.0121 (1.00)	0.409 (1.00)	9.29e-08 (1.09e-05)	3.69e-07 (4.25e-05)
NUMBER OF LYMPH NODES	ANOVA	0.0213 (1.00)	0.287 (1.00)	0.081 (1.00)	0.581 (1.00)	0.0815 (1.00)	0.00675 (0.635)	0.00625 (0.593)	0.0128 (1.00)	0.0178 (1.00)	0.568 (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	48	62	72	40

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.517 (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	113	15	0.9 - 52.0 (5.0)
subtype1	26	2	1.0 - 30.0 (1.0)
subtype2	35	9	1.0 - 52.0 (13.0)
subtype3	28	2	0.9 - 49.9 (1.0)
subtype4	24	2	0.9 - 52.0 (12.5)

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 = 0.781 (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	48	70.3 (11.8)
subtype2	62	68.3 (9.4)
subtype3	72	69.5 (12.4)
subtype4	40	70.2 (12.2)

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 = 0.0102 (Fisher's exact test), Q value = 0.94

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

nPatients	COLON	RECTUM
ALL	152	68
subtype1	41	7
subtype2	36	26
subtype3	45	25
subtype4	30	10

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 = 0.0589 (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	29	19
subtype2	33	29
subtype3	30	42
subtype4	14	26

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 = 7.14e-07 (Chi-square test), Q value = 8.1e-05

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	126	24	58	7
subtype1	27	13	4	2
subtype2	35	1	26	0
subtype3	44	1	21	2
subtype4	20	9	7	3

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

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

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	9	46	148	17
subtype1	1	9	32	4
subtype2	1	12	41	8
subtype3	5	16	51	0
subtype4	2	9	24	5

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.00761 (Chi-square test), Q value = 0.71

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

nPatients	N0	N1	N2
ALL	136	43	43
subtype1	30	6	12
subtype2	28	18	16
subtype3	46	12	14
subtype4	32	7	1

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

'mRNA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.00331 (Chi-square test), Q value = 0.33

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	185	33	1
subtype1	41	6	0
subtype2	44	18	0
subtype3	63	8	0
subtype4	37	1	1

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0188 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	46	85	55	31
subtype1	8	21	12	5
subtype2	9	16	19	16
subtype3	19	27	18	8
subtype4	10	21	6	2

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

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

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

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

nPatients	NO	YES
ALL	1	221
subtype1	1	47
subtype2	0	62
subtype3	0	72
subtype4	0	40

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

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

P value = 0.0194 (Chi-square test), Q value = 1

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

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

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

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

P value = 0.0213 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	220	2.2 (4.7)
subtype1	48	3.4 (6.8)
subtype2	61	2.9 (5.4)
subtype3	72	1.6 (2.7)
subtype4	39	0.7 (2.1)

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	64	115	43

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.76 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	15	0.9 - 52.0 (5.0)
subtype1	35	3	1.0 - 41.0 (1.0)
subtype2	68	11	0.9 - 52.0 (12.0)
subtype3	10	1	0.9 - 17.0 (1.0)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.246 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	222	69.5 (11.4)
subtype1	64	71.5 (11.5)
subtype2	115	68.5 (10.8)
subtype3	43	69.1 (12.7)

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

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

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

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

nPatients	COLON	RECTUM
ALL	152	68
subtype1	51	13
subtype2	72	42
subtype3	29	13

Figure S15. 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 = 0.605 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	106	116
subtype1	34	30
subtype2	52	63
subtype3	20	23

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 7.97e-08 (Chi-square test), Q value = 9.4e-06

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	126	24	58	7
subtype1	29	20	9	3
subtype2	68	4	37	3
subtype3	29	0	12	1

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.825 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	9	46	148	17
subtype1	3	12	41	6
subtype2	5	24	76	10
subtype3	1	10	31	1

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

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

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

nPatients	N0	N1	N2
ALL	136	43	43
subtype1	43	11	10
subtype2	65	22	28
subtype3	28	10	5

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

'mRNA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.347 (Chi-square test), Q value = 1

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	185	33	1
subtype1	57	6	0
subtype2	91	22	1
subtype3	37	5	0

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

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.476 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	46	85	55	31
subtype1	13	29	15	5
subtype2	23	38	30	21
subtype3	10	18	10	5

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

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

P value = 0.482 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	1	221
subtype1	1	63
subtype2	0	115
subtype3	0	43

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

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

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

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

nPatients	R0	R1	R2
ALL	185	2	29
subtype1	56	0	4
subtype2	93	2	20
subtype3	36	0	5

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

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

P value = 0.287 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	220	2.2 (4.7)
subtype1	64	2.5 (6.0)
subtype2	113	2.3 (4.5)
subtype3	43	1.2 (2.1)

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

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

Table S27. 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 = 0.85 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	444	60	0.1 - 135.5 (7.0)
subtype1	150	22	0.1 - 129.1 (8.0)
subtype2	191	22	0.1 - 135.5 (6.0)
subtype3	73	12	0.2 - 112.7 (6.6)
subtype4	30	4	0.3 - 87.8 (13.3)

Figure S25. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

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

Table S29. 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 S26. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 7.44e-05 (Fisher's exact test), Q value = 0.0084

Table S30. 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 S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	267	308
subtype1	88	112
subtype2	113	121
subtype3	48	52
subtype4	18	23

Figure S28. 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 = 4.28e-13 (Chi-square test), Q value = 5.1e-11

Table S32. 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	356	54	143	13
subtype1	121	3	72	0
subtype2	144	46	32	9
subtype3	61	5	28	4
subtype4	30	0	11	0

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.934 (Chi-square test), Q value = 1

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	20	101	392	59
subtype1	7	31	140	22
subtype2	10	44	154	24
subtype3	2	18	69	11
subtype4	1	8	29	2

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

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

P value = 0.00155 (Chi-square test), Q value = 0.17

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

nPatients	N0	N1	N2
ALL	328	140	103
subtype1	96	64	38
subtype2	157	45	32
subtype3	51	22	26
subtype4	24	9	7

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.2 (Chi-square test), Q value = 1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	435	71	9	1	49
subtype1	143	35	4	0	16
subtype2	189	16	2	1	21
subtype3	72	15	2	0	10
subtype4	31	5	1	0	2

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.00415 (Chi-square test), Q value = 0.42

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

nPatients	I	II	III	IV
ALL	97	207	166	81
subtype1	28	58	68	38
subtype2	47	100	58	19
subtype3	16	32	32	18
subtype4	6	17	8	6

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

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

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

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

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

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

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

P value = 0.0654 (Chi-square test), Q value = 1

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

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

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

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

P value = 0.081 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	538	2.2 (4.8)
subtype1	191	2.3 (4.9)
subtype2	219	1.7 (4.2)
subtype3	92	3.2 (6.0)
subtype4	36	1.9 (2.8)

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

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	146	113	91

'METHLYATION CNMF' versus 'Time to Death'

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

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	325	44	0.1 - 135.5 (7.0)
subtype1	136	20	0.1 - 135.5 (7.5)
subtype2	104	16	0.1 - 129.1 (7.5)
subtype3	85	8	0.1 - 102.4 (5.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00171 (ANOVA), Q value = 0.18

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

	nPatients	Mean (Std.Dev)
ALL	349	64.9 (13.0)
subtype1	146	65.2 (12.6)
subtype2	113	67.4 (13.0)
subtype3	90	61.0 (12.9)

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

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

P value = 0.00302 (Fisher's exact test), Q value = 0.31

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

nPatients	COLON	RECTUM
ALL	255	93
subtype1	95	50
subtype2	95	18
subtype3	65	25

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0473 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	157	193
subtype1	62	84
subtype2	61	52
subtype3	34	57

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00521 (Chi-square test), Q value = 0.51

Table S45. 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	89	6	48	2
subtype2	82	13	15	3
subtype3	54	11	24	1

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.466 (Chi-square test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	11	51	244	42
subtype1	4	23	101	17
subtype2	4	20	78	10
subtype3	3	8	65	15

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.497 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	193	94	58
subtype1	75	43	23
subtype2	70	24	19
subtype3	48	27	16

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.748 (Chi-square test), Q value = 1

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	247	37	8	1	50
subtype1	99	18	5	0	22
subtype2	82	10	1	1	16
subtype3	66	9	2	0	12

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

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.39 (Chi-square test), Q value = 1

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	48	126	107	49
subtype1	18	48	49	23
subtype2	21	45	28	14
subtype3	9	33	30	12

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

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

P value = 0.0619 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	8	342
subtype1	4	142
subtype2	0	113
subtype3	4	87

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

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

P value = 0.08 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	221	2	6	24
subtype1	92	1	2	8
subtype2	66	0	4	13
subtype3	63	1	0	3

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

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

P value = 0.581 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	314	2.2 (4.9)
subtype1	134	2.3 (5.4)
subtype2	99	1.9 (3.8)
subtype3	81	2.7 (5.2)

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

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	104	160	135

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.365 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	308	30	0.1 - 121.1 (6.0)
subtype1	87	5	0.1 - 100.0 (4.8)
subtype2	105	12	0.1 - 121.1 (5.8)
subtype3	116	13	0.1 - 119.5 (7.5)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.179 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	398	66.2 (12.8)
subtype1	104	67.6 (12.0)
subtype2	159	66.7 (12.7)
subtype3	135	64.6 (13.5)

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

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

P value = 0.841 (Fisher's exact test), Q value = 1

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

nPatients	COLON	RECTUM
ALL	269	130
subtype1	68	36
subtype2	108	52
subtype3	93	42

Figure S51. 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 = 0.0588 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	187	212
subtype1	47	57
subtype2	86	74
subtype3	54	81

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.263 (Chi-square test), Q value = 1

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	238	30	117	10
subtype1	54	13	30	4
subtype2	100	8	47	4
subtype3	84	9	40	2

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.354 (Chi-square test), Q value = 1

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	10	68	276	41
subtype1	0	16	73	14
subtype2	4	28	112	13
subtype3	6	24	91	14

Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0549 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	224	103	68
subtype1	52	28	22
subtype2	85	41	33
subtype3	87	34	13

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

'RPPA CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0335 (Chi-square test), Q value = 1

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	300	49	8	1	36
subtype1	79	13	5	0	5
subtype2	115	26	0	1	17
subtype3	106	10	3	0	14

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

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.297 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	61	147	119	57
subtype1	13	37	32	17
subtype2	24	51	50	27
subtype3	24	59	37	13

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

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

P value = 0.348 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	8	391
subtype1	4	100
subtype2	2	158
subtype3	2	133

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

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

P value = 0.0794 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	301	2	23	17
subtype1	83	1	4	5
subtype2	111	0	16	6
subtype3	107	1	3	6

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

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

P value = 0.0815 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	371	2.2 (4.8)
subtype1	97	2.8 (6.3)
subtype2	148	2.5 (4.7)
subtype3	126	1.4 (3.5)

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5	6
Number of samples	42	88	69	44	32	124

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.442 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	308	30	0.1 - 121.1 (6.0)
subtype1	22	0	0.9 - 43.9 (1.0)
subtype2	76	9	0.1 - 103.0 (6.1)
subtype3	62	2	0.1 - 87.8 (5.2)
subtype4	40	7	0.1 - 121.1 (7.9)
subtype5	20	3	0.1 - 105.3 (8.8)
subtype6	88	9	0.2 - 75.2 (5.5)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0169 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	398	66.2 (12.8)
subtype1	42	71.9 (10.0)
subtype2	88	65.3 (12.6)
subtype3	69	64.9 (12.7)
subtype4	44	62.5 (14.8)
subtype5	32	66.7 (13.2)
subtype6	123	67.0 (12.6)

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

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

P value = 0.777 (Chi-square test), Q value = 1

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

nPatients	COLON	RECTUM
ALL	269	130
subtype1	27	15
subtype2	59	29
subtype3	48	21
subtype4	30	14
subtype5	25	7
subtype6	80	44

Figure S63. 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 = 0.17 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	187	212
subtype1	17	25
subtype2	34	54
subtype3	31	38
subtype4	19	25
subtype5	17	15
subtype6	69	55

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.929 (Chi-square test), Q value = 1

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	238	30	117	10
subtype1	23	3	13	1
subtype2	52	7	27	2
subtype3	40	8	18	3
subtype4	26	4	13	0
subtype5	23	2	6	1
subtype6	74	6	40	3

Figure S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.577 (Chi-square test), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	10	68	276	41
subtype1	1	6	31	3
subtype2	3	19	59	7
subtype3	1	12	44	11
subtype4	0	5	30	8
subtype5	1	3	26	2
subtype6	4	23	86	10

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0741 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	224	103	68
subtype1	26	12	4
subtype2	62	16	9
subtype3	32	20	15
subtype4	21	12	11
subtype5	21	7	4
subtype6	62	36	25

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

'RPPA cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.00199 (Chi-square test), Q value = 0.21

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	300	49	8	1	36
subtype1	36	6	0	0	0
subtype2	67	6	3	0	11
subtype3	50	8	5	0	4
subtype4	32	8	0	1	3
subtype5	28	0	0	0	2
subtype6	87	21	0	0	16

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

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0947 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	61	147	119	57
subtype1	5	18	11	6
subtype2	18	39	20	9
subtype3	10	18	24	13
subtype4	4	18	13	9
subtype5	3	17	11	0
subtype6	21	37	40	20

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

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

P value = 0.22 (Chi-square test), Q value = 1

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

nPatients	NO	YES
ALL	8	391
subtype1	0	42
subtype2	1	87
subtype3	4	65
subtype4	1	43
subtype5	0	32
subtype6	2	122

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

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

P value = 0.0272 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	301	2	23	17
subtype1	34	0	7	0
subtype2	69	0	2	5
subtype3	55	1	1	2
subtype4	30	0	1	5
subtype5	26	0	1	2
subtype6	87	1	11	3

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

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

P value = 0.00675 (ANOVA), Q value = 0.63

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

	nPatients	Mean (Std.Dev)
ALL	371	2.2 (4.8)
subtype1	40	1.1 (1.6)
subtype2	82	1.2 (3.0)
subtype3	65	3.3 (7.3)
subtype4	37	4.1 (6.5)
subtype5	30	1.2 (2.4)
subtype6	117	2.3 (4.5)

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S79. 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 = 0.567 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	224	27	0.1 - 129.1 (6.1)
subtype1	33	2	0.1 - 105.3 (7.6)
subtype2	77	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 S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

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

Table S81. 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 S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.00258 (Fisher's exact test), Q value = 0.27

Table S82. 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 S75. 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 S83. 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 S76. 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 = 0.081

Table S84. 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 S77. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.162 (Chi-square test), Q value = 1

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	9	43	157	34
subtype1	1	9	23	5
subtype2	5	18	49	11
subtype3	0	4	40	12
subtype4	3	12	45	6

Figure S78. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.000372 (Chi-square test), Q value = 0.042

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

nPatients	N0	N1	N2
ALL	143	57	40
subtype1	22	13	3
subtype2	48	22	11
subtype3	25	10	20
subtype4	48	12	6

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

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

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

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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 S80. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0216 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	42	91	66	32
subtype1	9	10	12	7
subtype2	17	26	24	11
subtype3	3	20	18	10
subtype4	13	35	12	4

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

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

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

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

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

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

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

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

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

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

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

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

P value = 0.00625 (ANOVA), Q value = 0.59

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

	nPatients	Mean (Std.Dev)
ALL	212	2.2 (5.2)
subtype1	34	1.7 (3.9)
subtype2	70	1.7 (2.9)
subtype3	52	4.4 (8.6)
subtype4	56	1.3 (2.9)

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S92. 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 S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	224	27	0.1 - 129.1 (6.1)
subtype1	88	7	0.1 - 121.1 (4.8)
subtype2	62	9	0.1 - 103.0 (7.1)
subtype3	74	11	0.1 - 129.1 (8.2)

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

'RNAseq cHierClus subtypes' versus 'AGE'

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

Table S94. 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 S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.000781 (Fisher's exact test), Q value = 0.084

Table S95. 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 S87. 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 S96. 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 S88. 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 = 0.051

Table S97. 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 S89. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0662 (Chi-square test), Q value = 1

Table S98. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	9	43	157	34
subtype1	2	9	65	19
subtype2	3	14	43	7
subtype3	4	20	49	8

Figure S90. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.00474 (Chi-square test), Q value = 0.47

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

nPatients	N0	N1	N2
ALL	143	57	40
subtype1	43	27	24
subtype2	48	14	5
subtype3	52	16	11

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

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

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

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 S92. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

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

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

nPatients	I	II	III	IV
ALL	42	91	66	32
subtype1	9	32	31	16
subtype2	14	31	13	7
subtype3	19	28	22	9

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

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

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

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

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

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

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

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

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

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

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

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

P value = 0.0128 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	212	2.2 (5.2)
subtype1	85	3.5 (7.2)
subtype2	58	1.3 (2.8)
subtype3	69	1.5 (2.9)

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

Clustering Approach #9: 'MIRSEQ CNMF'

Table S105. 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 = 0.525 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	415	59	0.1 - 135.5 (7.0)
subtype1	132	18	0.2 - 72.1 (7.3)
subtype2	206	32	0.1 - 135.5 (7.8)
subtype3	77	9	0.1 - 100.0 (4.8)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00518 (ANOVA), Q value = 0.51

Table S107. 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 S98. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

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

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

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

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

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.255 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	258	292
subtype1	120	125
subtype2	106	116
subtype3	32	51

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.000466 (Chi-square test), Q value = 0.051

Table S110. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	349	55	127	10
subtype1	151	24	56	7
subtype2	147	13	60	1
subtype3	51	18	11	2

Figure S101. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.0523 (Chi-square test), Q value = 1

Table S111. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	20	95	376	55
subtype1	10	50	162	21
subtype2	9	37	155	19
subtype3	1	8	59	15

Figure S102. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.019 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	315	132	98
subtype1	150	48	47
subtype2	128	61	30
subtype3	37	23	21

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

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 3.15e-08 (Chi-square test), Q value = 3.7e-06

Table S113. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	412	68	9	1	50
subtype1	199	40	1	0	1
subtype2	157	19	6	1	35
subtype3	56	9	2	0	14

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

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.0489 (Chi-square test), Q value = 1

Table S114. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	93	199	156	77
subtype1	50	91	58	37
subtype2	38	81	68	28
subtype3	5	27	30	12

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

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

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

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

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

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

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

P value = 9.29e-08 (Chi-square test), Q value = 1.1e-05

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

nPatients	R0	R1	R2	RX
ALL	383	3	36	25
subtype1	200	2	32	2
subtype2	125	1	4	19
subtype3	58	0	0	4

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

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

P value = 0.0178 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	512	2.2 (4.7)
subtype1	239	2.1 (4.3)
subtype2	194	1.8 (3.5)
subtype3	79	3.5 (7.5)

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

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S118. 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 = 0.766 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	415	59	0.1 - 135.5 (7.0)
subtype1	125	17	0.6 - 72.1 (9.4)
subtype2	30	5	0.5 - 112.7 (9.3)
subtype3	260	37	0.1 - 135.5 (6.9)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00244 (ANOVA), Q value = 0.25

Table S120. 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 S110. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

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

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	258	292
subtype1	115	121
subtype2	18	15
subtype3	125	156

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.382 (Chi-square test), Q value = 1

Table S123. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	349	55	127	10
subtype1	149	26	47	7
subtype2	22	3	7	1
subtype3	178	26	73	2

Figure S113. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.0053 (Chi-square test), Q value = 0.51

Table S124. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	20	95	376	55
subtype1	9	49	156	20
subtype2	5	4	21	3
subtype3	6	42	199	32

Figure S114. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.236 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	315	132	98
subtype1	140	51	44
subtype2	24	5	4
subtype3	151	76	50

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 1.91e-07 (Chi-square test), Q value = 2.2e-05

Table S126. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	412	68	9	1	50
subtype1	191	38	2	0	1
subtype2	25	4	0	0	3
subtype3	196	26	7	1	46

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

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.0748 (Chi-square test), Q value = 1

Table S127. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	93	199	156	77
subtype1	48	83	60	35
subtype2	9	14	5	4
subtype3	36	102	91	38

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

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

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

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

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

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

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

P value = 3.69e-07 (Chi-square test), Q value = 4.2e-05

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

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

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

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

P value = 0.568 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	512	2.2 (4.7)
subtype1	231	2.2 (4.7)
subtype2	31	1.3 (3.4)
subtype3	250	2.3 (4.9)

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

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 = 10
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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