Correlation between molecular cancer subtypes and selected clinical features

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 9 clinical features across 422 patients, 13 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 'CN CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'PATHOLOGY.N', and 'TUMOR.STAGE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GENDER', 'HISTOLOGICAL.TYPE', and 'TUMOR.STAGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE' and 'PATHOLOGICSPREAD(M)'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'PATHOLOGICSPREAD(M)'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 13 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	HISTOLOGICAL TYPE	PATHOLOGY T	PATHOLOGY N	PATHOLOGICSPREAD(M)	TUMOR STAGE	RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Fisher's exact test	Chi-square test	Chi-square test	Chi-square test	Chi-square test	Fisher's exact test
mRNA CNMF subtypes	0.31 (1.00)	0.418 (1.00)	0.0625 (1.00)	3.12e-06 (0.000262)	0.89 (1.00)	0.0267 (1.00)	0.0549 (1.00)	0.0374 (1.00)
mRNA cHierClus subtypes	0.755 (1.00)	0.146 (1.00)	0.0978 (1.00)	3.57e-09 (3.14e-07)	0.561 (1.00)	0.58 (1.00)	0.848 (1.00)	0.509 (1.00)
CN CNMF	0.755 (1.00)	0.472 (1.00)	0.487 (1.00)	3.7e-08 (3.22e-06)	0.944 (1.00)	0.00113 (0.0895)	0.00999 (0.729)	3.6e-05 (0.00292)	0.337 (1.00)
METHLYATION CNMF	0.928 (1.00)	0.00205 (0.16)	0.0531 (1.00)	0.0769 (1.00)	0.375 (1.00)	0.904 (1.00)	0.304 (1.00)	0.036 (1.00)	0.473 (1.00)
RPPA CNMF subtypes	0.429 (1.00)	0.501 (1.00)	0.306 (1.00)	0.015 (1.00)	0.23 (1.00)	0.141 (1.00)	0.186 (1.00)	0.117 (1.00)	0.302 (1.00)
RPPA cHierClus subtypes	0.302 (1.00)	0.169 (1.00)	0.348 (1.00)	0.464 (1.00)	0.28 (1.00)	0.123 (1.00)	0.0432 (1.00)	0.0747 (1.00)	0.339 (1.00)
RNAseq CNMF subtypes	0.871 (1.00)	0.105 (1.00)	0.00324 (0.246)	2.4e-07 (2.04e-05)	0.7 (1.00)	0.0246 (1.00)	0.022 (1.00)	0.00265 (0.204)	1 (1.00)
RNAseq cHierClus subtypes	0.311 (1.00)	0.00338 (0.254)	0.456 (1.00)	8.97e-08 (7.71e-06)	0.638 (1.00)	0.238 (1.00)	0.342 (1.00)	0.631 (1.00)	1 (1.00)
MIRseq CNMF subtypes	0.246 (1.00)	0.000748 (0.0599)	0.525 (1.00)	0.0162 (1.00)	0.264 (1.00)	0.515 (1.00)	1.25e-05 (0.00102)	0.596 (1.00)	0.544 (1.00)
MIRseq cHierClus subtypes	0.521 (1.00)	0.00978 (0.724)	0.514 (1.00)	0.67 (1.00)	0.498 (1.00)	0.95 (1.00)	7.64e-06 (0.000634)	0.448 (1.00)	1 (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	63	35	31	26

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.31 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	75	11	0.9 - 52.0 (5.0)
subtype1	27	6	1.0 - 41.0 (14.4)
subtype2	22	1	1.0 - 30.0 (1.0)
subtype3	18	3	1.0 - 52.0 (8.0)
subtype4	8	1	0.9 - 1.0 (1.0)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.418 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	155	70.6 (11.7)
subtype1	63	69.2 (11.9)
subtype2	35	72.1 (11.7)
subtype3	31	72.7 (10.9)
subtype4	26	69.3 (12.1)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0625 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	76	79
subtype1	32	31
subtype2	23	12
subtype3	11	20
subtype4	10	16

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.12e-06 (Fisher's exact test), Q value = 0.00026

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	128	24
subtype1	62	1
subtype2	22	12
subtype3	21	9
subtype4	23	2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.89 (Chi-square test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	4	31	105	13
subtype1	2	13	43	5
subtype2	1	7	23	2
subtype3	0	6	20	5
subtype4	1	5	19	1

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

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

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

nPatients	N0	N1	N2
ALL	95	28	32
subtype1	29	15	19
subtype2	24	4	7
subtype3	24	6	1
subtype4	18	3	5

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

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

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

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	129	22	1
subtype1	48	15	0
subtype2	30	4	0
subtype3	28	1	1
subtype4	23	2	0

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0374 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	28	63	39	22
subtype1	12	15	21	15
subtype2	6	17	7	3
subtype3	5	18	5	2
subtype4	5	13	6	2

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	47	48	14	46

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.755 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	75	11	0.9 - 52.0 (5.0)
subtype1	28	2	1.0 - 30.0 (1.0)
subtype2	27	5	1.0 - 41.0 (15.0)
subtype3	7	2	0.9 - 52.0 (14.4)
subtype4	13	2	0.9 - 34.0 (1.0)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.146 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	155	70.6 (11.7)
subtype1	47	73.1 (11.5)
subtype2	48	70.7 (9.6)
subtype3	14	71.6 (12.8)
subtype4	46	67.5 (13.0)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0978 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	76	79
subtype1	27	20
subtype2	27	21
subtype3	6	8
subtype4	16	30

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.57e-09 (Fisher's exact test), Q value = 3.1e-07

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	128	24
subtype1	25	20
subtype2	46	2
subtype3	12	2
subtype4	45	0

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.561 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	4	31	105	13
subtype1	1	10	28	6
subtype2	1	7	36	4
subtype3	1	1	11	1
subtype4	1	13	30	2

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.58 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	95	28	32
subtype1	31	7	9
subtype2	25	13	10
subtype3	10	1	3
subtype4	29	7	10

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.848 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	129	22	1
subtype1	40	6	0
subtype2	38	8	1
subtype3	12	2	0
subtype4	39	6	0

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.509 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	28	63	39	22
subtype1	9	21	10	5
subtype2	5	17	16	9
subtype3	2	8	2	2
subtype4	12	17	11	6

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #3: 'CN CNMF'

Table S19. Get Full Table Description of clustering approach #3: 'CN CNMF'

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

'CN CNMF' versus 'Time to Death'

P value = 0.755 (logrank test), Q value = 1

Table S20. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	319	49	0.1 - 135.5 (7.7)
subtype1	127	23	0.1 - 129.1 (10.0)
subtype2	149	19	0.1 - 129.1 (6.1)
subtype3	23	4	0.3 - 135.5 (7.0)
subtype4	20	3	0.2 - 112.7 (6.0)

Figure S17. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.472 (ANOVA), Q value = 1

Table S21. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

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

Figure S18. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.487 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	193	220
subtype1	77	93
subtype2	92	95
subtype3	16	16
subtype4	8	16

Figure S19. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 3.7e-08 (Fisher's exact test), Q value = 3.2e-06

Table S23. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	356	54
subtype1	162	7
subtype2	140	45
subtype3	31	1
subtype4	23	1

Figure S20. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.944 (Chi-square test), Q value = 1

Table S24. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	11	72	283	45
subtype1	5	27	120	18
subtype2	6	33	124	22
subtype3	0	6	23	3
subtype4	0	6	16	2

Figure S21. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.00113 (Chi-square test), Q value = 0.089

Table S25. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	245	95	71
subtype1	78	54	36
subtype2	131	29	27
subtype3	20	7	5
subtype4	16	5	3

Figure S22. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.00999 (Chi-square test), Q value = 0.73

Table S26. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	312	49	7	1	36
subtype1	119	32	3	0	14
subtype2	153	10	1	1	18
subtype3	21	6	2	0	2
subtype4	19	1	1	0	2

Figure S23. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 3.6e-05 (Chi-square test), Q value = 0.0029

Table S27. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	67	157	116	56
subtype1	23	46	60	35
subtype2	35	89	44	11
subtype3	4	13	5	8
subtype4	5	9	7	2

Figure S24. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S28. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S25. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S29. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	106	86	63

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.928 (logrank test), Q value = 1

Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	237	36	0.1 - 135.5 (7.6)
subtype1	95	15	0.1 - 135.5 (8.0)
subtype2	81	13	0.1 - 129.1 (8.0)
subtype3	61	8	0.1 - 102.4 (5.8)

Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00205 (ANOVA), Q value = 0.16

Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	254	65.4 (13.3)
subtype1	106	66.3 (13.1)
subtype2	86	68.0 (13.1)
subtype3	62	60.5 (12.6)

Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0531 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	115	140
subtype1	42	64
subtype2	48	38
subtype3	25	38

Figure S28. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0769 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	225	30
subtype1	99	7
subtype2	73	13
subtype3	53	10

Figure S29. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

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

Table S34. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	7	38	178	31
subtype1	3	18	72	13
subtype2	3	16	58	8
subtype3	1	4	48	10

Figure S30. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.904 (Chi-square test), Q value = 1

Table S35. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	152	64	37
subtype1	59	29	16
subtype2	53	20	13
subtype3	40	15	8

Figure S31. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.304 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	180	27	6	1	36
subtype1	69	16	4	0	16
subtype2	61	6	1	1	14
subtype3	50	5	1	0	6

Figure S32. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.036 (Chi-square test), Q value = 1

Table S37. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	37	98	72	34
subtype1	15	34	31	20
subtype2	18	32	23	9
subtype3	4	32	18	5

Figure S33. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S38. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	252
subtype1	2	104
subtype2	0	86
subtype3	1	62

Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S39. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	65	119	7	78

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.429 (logrank test), Q value = 1

Table S40. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	19	0.1 - 105.3 (6.0)
subtype1	56	2	0.1 - 87.8 (5.2)
subtype2	82	7	0.1 - 75.2 (6.5)
subtype3	2	0	0.6 - 18.1 (9.4)
subtype4	66	10	0.1 - 105.3 (6.0)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.501 (ANOVA), Q value = 1

Table S41. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	268	66.6 (13.3)
subtype1	65	66.7 (12.0)
subtype2	118	66.2 (13.4)
subtype3	7	74.1 (9.4)
subtype4	78	66.3 (14.6)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.306 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	127	142
subtype1	34	31
subtype2	55	64
subtype3	1	6
subtype4	37	41

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.015 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	238	30
subtype1	51	14
subtype2	112	7
subtype3	6	1
subtype4	69	8

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

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.23 (Chi-square test), Q value = 1

Table S44. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	5	45	185	31
subtype1	0	9	46	10
subtype2	1	22	82	11
subtype3	0	0	7	0
subtype4	4	14	50	10

Figure S39. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.141 (Chi-square test), Q value = 1

Table S45. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	160	66	42
subtype1	33	19	12
subtype2	66	31	22
subtype3	6	0	1
subtype4	55	16	7

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

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

P value = 0.186 (Chi-square test), Q value = 1

Table S46. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	202	31	6	1	26
subtype1	50	7	3	0	4
subtype2	84	20	0	1	13
subtype3	7	0	0	0	0
subtype4	61	4	3	0	9

Figure S41. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.117 (Chi-square test), Q value = 1

Table S47. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	40	107	77	38
subtype1	7	24	22	10
subtype2	19	39	35	21
subtype3	0	6	1	0
subtype4	14	38	19	7

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

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

P value = 0.302 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	266
subtype1	2	63
subtype2	1	118
subtype3	0	7
subtype4	0	78

Figure S43. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S49. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	143	25	49	52

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.302 (logrank test), Q value = 1

Table S50. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	19	0.1 - 105.3 (6.0)
subtype1	100	11	0.1 - 103.0 (6.6)
subtype2	15	1	0.1 - 105.3 (8.0)
subtype3	44	6	0.1 - 100.0 (5.1)
subtype4	47	1	0.1 - 87.8 (5.4)

Figure S44. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.169 (ANOVA), Q value = 1

Table S51. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	268	66.6 (13.3)
subtype1	142	68.0 (13.6)
subtype2	25	67.9 (12.0)
subtype3	49	63.9 (13.8)
subtype4	52	64.6 (12.6)

Figure S45. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

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

Table S52. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	127	142
subtype1	71	72
subtype2	14	11
subtype3	18	31
subtype4	24	28

Figure S46. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.464 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	238	30
subtype1	129	13
subtype2	23	2
subtype3	41	8
subtype4	45	7

Figure S47. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.28 (Chi-square test), Q value = 1

Table S54. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	5	45	185	31
subtype1	1	23	99	17
subtype2	0	3	20	2
subtype3	3	12	30	4
subtype4	1	7	36	8

Figure S48. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

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

Table S55. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	160	66	42
subtype1	78	39	26
subtype2	19	5	1
subtype3	36	8	5
subtype4	27	14	10

Figure S49. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0432 (Chi-square test), Q value = 1

Table S56. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	202	31	6	1	26
subtype1	103	22	0	1	17
subtype2	22	0	0	0	1
subtype3	39	2	3	0	5
subtype4	38	7	3	0	3

Figure S50. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0747 (Chi-square test), Q value = 1

Table S57. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	40	107	77	38
subtype1	20	51	45	23
subtype2	2	16	6	0
subtype3	11	23	10	5
subtype4	7	17	16	10

Figure S51. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.339 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	266
subtype1	1	142
subtype2	0	25
subtype3	0	49
subtype4	2	50

Figure S52. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S59. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	68	61	40	23

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.871 (logrank test), Q value = 1

Table S60. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	108	16	0.9 - 71.7 (12.4)
subtype1	36	6	0.9 - 71.7 (15.9)
subtype2	41	5	1.0 - 49.0 (8.1)
subtype3	25	5	0.9 - 52.0 (12.0)
subtype4	6	0	1.0 - 4.0 (1.0)

Figure S53. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.105 (ANOVA), Q value = 1

Table S61. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	192	69.7 (12.4)
subtype1	68	66.8 (13.0)
subtype2	61	72.0 (12.2)
subtype3	40	70.6 (10.9)
subtype4	23	70.1 (12.9)

Figure S54. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00324 (Fisher's exact test), Q value = 0.25

Table S62. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	99	93
subtype1	38	30
subtype2	40	21
subtype3	13	27
subtype4	8	15

Figure S55. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.4e-07 (Fisher's exact test), Q value = 2e-05

Table S63. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	162	27
subtype1	67	0
subtype2	41	19
subtype3	32	7
subtype4	22	1

Figure S56. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.7 (Chi-square test), Q value = 1

Table S64. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	6	38	128	18
subtype1	2	15	44	7
subtype2	2	8	41	8
subtype3	1	8	28	3
subtype4	1	7	15	0

Figure S57. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0246 (Chi-square test), Q value = 1

Table S65. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	114	41	36
subtype1	31	20	16
subtype2	34	15	12
subtype3	31	4	5
subtype4	18	2	3

Figure S58. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.022 (Chi-square test), Q value = 1

Table S66. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	159	28	2
subtype1	47	18	1
subtype2	53	7	1
subtype3	37	2	0
subtype4	22	1	0

Figure S59. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.00265 (Chi-square test), Q value = 0.2

Table S67. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	35	73	50	27
subtype1	11	16	21	17
subtype2	8	25	19	7
subtype3	8	22	6	2
subtype4	8	10	4	1

Figure S60. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S68. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	191
subtype1	1	67
subtype2	0	61
subtype3	0	40
subtype4	0	23

Figure S61. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S69. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	67	19	106

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.311 (logrank test), Q value = 1

Table S70. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	108	16	0.9 - 71.7 (12.4)
subtype1	44	8	1.0 - 52.0 (8.0)
subtype2	11	0	7.0 - 34.0 (15.2)
subtype3	53	8	0.9 - 71.7 (10.9)

Figure S62. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00338 (ANOVA), Q value = 0.25

Table S71. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	192	69.7 (12.4)
subtype1	67	73.4 (11.6)
subtype2	19	71.1 (12.6)
subtype3	106	67.0 (12.4)

Figure S63. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.456 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	99	93
subtype1	36	31
subtype2	12	7
subtype3	51	55

Figure S64. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.97e-08 (Fisher's exact test), Q value = 7.7e-06

Table S73. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	162	27
subtype1	43	22
subtype2	17	2
subtype3	102	3

Figure S65. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

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

Table S74. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	6	38	128	18
subtype1	2	14	41	8
subtype2	0	2	14	3
subtype3	4	22	73	7

Figure S66. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.238 (Chi-square test), Q value = 1

Table S75. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	114	41	36
subtype1	44	15	8
subtype2	10	2	6
subtype3	60	24	22

Figure S67. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.342 (Chi-square test), Q value = 1

Table S76. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	159	28	2
subtype1	59	6	1
subtype2	13	5	0
subtype3	87	17	1

Figure S68. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.631 (Chi-square test), Q value = 1

Table S77. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	35	73	50	27
subtype1	14	29	16	6
subtype2	2	7	4	4
subtype3	19	37	30	17

Figure S69. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S78. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	191
subtype1	0	67
subtype2	0	19
subtype3	1	105

Figure S70. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S79. Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	182	163	62

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.246 (logrank test), Q value = 1

Table S80. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	309	49	0.1 - 135.5 (7.5)
subtype1	98	17	0.9 - 71.7 (7.3)
subtype2	153	26	0.1 - 135.5 (9.4)
subtype3	58	6	0.1 - 100.0 (4.3)

Figure S71. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.000748 (ANOVA), Q value = 0.06

Table S81. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

Figure S72. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

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

Table S82. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	192	215
subtype1	91	91
subtype2	75	88
subtype3	26	36

Figure S73. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

Table S83. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	349	55
subtype1	154	25
subtype2	148	15
subtype3	47	15

Figure S74. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.264 (Chi-square test), Q value = 1

Table S84. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	11	69	279	45
subtype1	6	37	120	17
subtype2	5	23	117	17
subtype3	0	9	42	11

Figure S75. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.515 (Chi-square test), Q value = 1

Table S85. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	241	94	70
subtype1	111	36	34
subtype2	98	40	25
subtype3	32	18	11

Figure S76. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 1.25e-05 (Chi-square test), Q value = 0.001

Table S86. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	305	50	7	1	36
subtype1	148	28	1	0	1
subtype2	111	15	5	1	28
subtype3	46	7	1	0	7

Figure S77. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.596 (Chi-square test), Q value = 1

Table S87. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	65	155	113	57
subtype1	34	69	45	27
subtype2	26	63	48	22
subtype3	5	23	20	8

Figure S78. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S88. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S89. Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	180	45	182

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.521 (logrank test), Q value = 1

Table S90. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	309	49	0.1 - 135.5 (7.5)
subtype1	97	16	0.9 - 71.7 (9.1)
subtype2	42	8	0.1 - 129.1 (6.7)
subtype3	170	25	0.1 - 135.5 (7.0)

Figure S80. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.00978 (ANOVA), Q value = 0.72

Table S91. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

Figure S81. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.514 (Fisher's exact test), Q value = 1

Table S92. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	192	215
subtype1	90	90
subtype2	22	23
subtype3	80	102

Figure S82. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.67 (Fisher's exact test), Q value = 1

Table S93. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	349	55
subtype1	151	26
subtype2	41	4
subtype3	157	25

Figure S83. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.498 (Chi-square test), Q value = 1

Table S94. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T0+T1	T2	T3	T4
ALL	11	69	279	45
subtype1	6	36	119	17
subtype2	2	5	30	7
subtype3	3	28	130	21

Figure S84. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.95 (Chi-square test), Q value = 1

Table S95. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	241	94	70
subtype1	107	39	33
subtype2	27	10	8
subtype3	107	45	29

Figure S85. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 7.64e-06 (Chi-square test), Q value = 0.00063

Table S96. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	M1B	MX
ALL	305	50	7	1	36
subtype1	145	29	2	0	0
subtype2	30	5	2	0	6
subtype3	130	16	3	1	30

Figure S86. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.448 (Chi-square test), Q value = 1

Table S97. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	65	155	113	57
subtype1	34	64	45	28
subtype2	8	18	10	7
subtype3	23	73	58	22

Figure S87. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S98. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S88. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data

Input

Cluster data file = COAD-TP.mergedcluster.txt
Clinical data file = COAD-TP.clin.merged.picked.txt
Number of patients = 422
Number of clustering approaches = 10
Number of selected clinical features = 9
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.

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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