Correlation between aggregated 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 12 different clustering approaches and 14 clinical features across 625 patients, 59 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 'TUMOR_TISSUE_SITE', 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', 'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'PATHOLOGIC_STAGE', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'TUMOR_TISSUE_SITE', 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', 'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', and 'HISTOLOGICAL_TYPE'.
CNMF clustering analysis on RPPA data identified 7 subtypes that correlate to 'PATHOLOGIC_STAGE', 'RESIDUAL_TUMOR', 'RACE', and 'ETHNICITY'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGIC_STAGE' and 'RESIDUAL_TUMOR'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', 'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', 'RESIDUAL_TUMOR', and 'NUMBER_OF_LYMPH_NODES'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', and 'RESIDUAL_TUMOR'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'TUMOR_TISSUE_SITE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 59 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	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.312 (0.506)	0.223 (0.4)	0.646 (0.811)	0.75 (0.881)	0.63 (0.808)	0.817 (0.909)	0.0987 (0.234)	0.445 (0.651)	0.0609 (0.162)	0.846 (0.923)	0.855 (0.923)	0.503 (0.716)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.434 (0.651)	0.0483 (0.141)	0.512 (0.722)	0.000195 (0.00156)	0.621 (0.807)	0.125 (0.282)	0.00283 (0.0168)	0.00104 (0.00728)	0.00875 (0.0397)	0.00428 (0.0211)	0.201 (0.375)	0.516 (0.722)
TUMOR TISSUE SITE	Fisher's exact test	0.0253 (0.087)	0.0029 (0.0168)	1e-05 (0.000129)	0.00135 (0.00907)	0.624 (0.807)	0.169 (0.342)	0.0959 (0.233)	1e-05 (0.000129)	0.0396 (0.123)	0.261 (0.438)	1e-05 (0.000129)	1e-05 (0.000129)
PATHOLOGIC STAGE	Fisher's exact test	0.0056 (0.0269)	0.0492 (0.141)	0.00323 (0.0181)	0.658 (0.815)	0.0119 (0.0502)	0.00408 (0.0208)	0.00386 (0.0203)	3e-05 (0.000315)	0.00028 (0.00214)	0.012 (0.0502)	0.194 (0.366)	0.767 (0.894)
PATHOLOGY T STAGE	Fisher's exact test	0.0573 (0.158)	0.0614 (0.162)	0.555 (0.752)	0.804 (0.907)	0.913 (0.964)	0.605 (0.798)	0.0414 (0.126)	0.68 (0.822)	0.0969 (0.233)	0.903 (0.96)	0.143 (0.307)	0.441 (0.651)
PATHOLOGY N STAGE	Fisher's exact test	0.0163 (0.0623)	0.46 (0.666)	0.00014 (0.00124)	0.175 (0.346)	0.283 (0.466)	0.595 (0.793)	0.112 (0.258)	0.0149 (0.0594)	0.0152 (0.0594)	0.147 (0.313)	0.214 (0.396)	0.749 (0.881)
PATHOLOGY M STAGE	Fisher's exact test	0.0265 (0.0876)	0.0846 (0.209)	0.00378 (0.0203)	0.644 (0.811)	0.128 (0.282)	0.445 (0.651)	0.0843 (0.209)	0.101 (0.235)	0.817 (0.909)	0.192 (0.366)	0.0254 (0.087)	0.473 (0.679)
GENDER	Fisher's exact test	0.0615 (0.162)	0.187 (0.361)	0.225 (0.4)	0.16 (0.331)	0.354 (0.552)	0.647 (0.811)	0.608 (0.798)	0.361 (0.552)	0.543 (0.742)	0.52 (0.722)	0.857 (0.923)	0.323 (0.517)
RADIATION THERAPY	Fisher's exact test	0.98 (1.00)	0.018 (0.0657)	0.313 (0.506)	0.159 (0.331)	0.778 (0.901)	0.18 (0.352)	0.0673 (0.174)	0.726 (0.865)	0.71 (0.852)	0.84 (0.923)	0.361 (0.552)	0.00861 (0.0397)
HISTOLOGICAL TYPE	Fisher's exact test	1e-05 (0.000129)	1e-05 (0.000129)	1e-05 (0.000129)	1e-05 (0.000129)	0.226 (0.4)	0.274 (0.456)	2e-05 (0.000224)	1e-05 (0.000129)	5e-05 (0.000494)	0.237 (0.411)	1e-05 (0.000129)	1e-05 (0.000129)
RESIDUAL TUMOR	Fisher's exact test	0.0178 (0.0657)	0.00991 (0.0438)	0.0266 (0.0876)	0.242 (0.414)	0.00079 (0.00577)	0.00015 (0.00126)	2e-05 (0.000224)	1e-05 (0.000129)	1e-05 (0.000129)	0.00013 (0.00121)	0.534 (0.735)	0.357 (0.552)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.0127 (0.0521)	0.174 (0.346)	0.0028 (0.0168)	0.948 (0.99)	0.246 (0.417)	0.679 (0.822)	0.0494 (0.141)	0.0207 (0.074)	0.0488 (0.141)	0.127 (0.282)	0.0318 (0.103)	0.348 (0.552)
RACE	Fisher's exact test	0.134 (0.293)	0.168 (0.342)	0.671 (0.822)	0.431 (0.651)	0.00284 (0.0168)	0.787 (0.902)	0.863 (0.923)	0.0561 (0.157)	0.659 (0.815)	0.791 (0.902)	0.218 (0.398)	0.795 (0.902)
ETHNICITY	Fisher's exact test			1 (1.00)	0.86 (0.923)	0.0335 (0.106)	0.077 (0.196)	0.237 (0.411)	0.937 (0.984)	1 (1.00)	0.594 (0.793)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	46	62	72	42

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.312 (logrank test), Q value = 0.51

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	211	40	0.0 - 54.0 (21.0)
subtype1	42	9	0.0 - 46.6 (23.0)
subtype2	58	15	1.0 - 52.0 (20.5)
subtype3	69	10	0.0 - 50.0 (20.1)
subtype4	42	6	0.0 - 54.0 (22.0)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.434 (Kruskal-Wallis (anova)), Q value = 0.65

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

	nPatients	Mean (Std.Dev)
ALL	222	69.5 (11.4)
subtype1	46	70.8 (11.8)
subtype2	62	68.5 (8.9)
subtype3	72	69.5 (12.4)
subtype4	42	69.6 (12.6)

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

'mRNA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.0253 (Fisher's exact test), Q value = 0.087

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

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

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0056 (Fisher's exact test), Q value = 0.027

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

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	47	15	65	5	10	3	22	20	33	1
subtype1	8	3	15	3	3	1	4	2	6	0
subtype2	9	2	14	0	2	2	5	11	17	0
subtype3	19	4	23	0	4	0	7	7	8	0
subtype4	11	6	13	2	1	0	6	0	2	1

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0573 (Fisher's exact test), Q value = 0.16

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

nPatients	T1	T2	T3	T4
ALL	9	46	148	19
subtype1	1	9	30	6
subtype2	1	12	41	8
subtype3	5	16	51	0
subtype4	2	9	26	5

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0163 (Fisher's exact test), Q value = 0.062

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

nPatients	N0	N1	N2
ALL	136	43	43
subtype1	30	6	10
subtype2	28	17	17
subtype3	46	12	14
subtype4	32	8	2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0265 (Fisher's exact test), Q value = 0.088

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

nPatients	0	1
ALL	186	34
subtype1	39	6
subtype2	45	17
subtype3	63	8
subtype4	39	3

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0615 (Fisher's exact test), Q value = 0.16

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

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

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.98 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	165	13
subtype1	33	2
subtype2	45	3
subtype3	54	5
subtype4	33	3

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	129	22	58	7
subtype1	28	11	4	2
subtype2	36	1	25	0
subtype3	44	1	21	2
subtype4	21	9	8	3

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

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0178 (Fisher's exact test), Q value = 0.066

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

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

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

'mRNA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0127 (Kruskal-Wallis (anova)), Q value = 0.052

Table S13. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

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

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.134 (Fisher's exact test), Q value = 0.29

Table S14. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15
subtype1	0	7
subtype2	2	3
subtype3	0	4
subtype4	0	1

Figure S13. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S15. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	52	41	77	52

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.223 (logrank test), Q value = 0.4

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	211	40	0.0 - 54.0 (21.0)
subtype1	49	11	0.0 - 53.0 (22.0)
subtype2	39	11	1.0 - 48.0 (24.0)
subtype3	76	13	0.0 - 54.0 (19.5)
subtype4	47	5	0.9 - 50.0 (22.0)

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0483 (Kruskal-Wallis (anova)), Q value = 0.14

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

	nPatients	Mean (Std.Dev)
ALL	222	69.5 (11.4)
subtype1	52	72.9 (11.6)
subtype2	41	67.7 (9.6)
subtype3	77	68.1 (11.7)
subtype4	52	69.5 (11.6)

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

'mRNA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.0029 (Fisher's exact test), Q value = 0.017

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	152	68
subtype1	46	6
subtype2	24	17
subtype3	48	28
subtype4	34	17

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0492 (Fisher's exact test), Q value = 0.14

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	47	15	65	5	10	3	22	20	33	1
subtype1	9	3	17	4	3	1	6	2	5	1
subtype2	4	2	10	1	0	1	4	7	12	0
subtype3	20	8	23	0	6	1	5	5	9	0
subtype4	14	2	15	0	1	0	7	6	7	0

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0614 (Fisher's exact test), Q value = 0.16

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	9	46	148	19
subtype1	1	10	33	8
subtype2	0	7	28	6
subtype3	7	15	51	4
subtype4	1	14	36	1

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.46 (Fisher's exact test), Q value = 0.67

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	136	43	43
subtype1	34	9	9
subtype2	19	11	11
subtype3	52	12	13
subtype4	31	11	10

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0846 (Fisher's exact test), Q value = 0.21

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	186	34
subtype1	45	6
subtype2	29	12
subtype3	68	9
subtype4	44	7

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.187 (Fisher's exact test), Q value = 0.36

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	106	116
subtype1	30	22
subtype2	22	19
subtype3	34	43
subtype4	20	32

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.018 (Fisher's exact test), Q value = 0.066

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	165	13
subtype1	37	2
subtype2	30	1
subtype3	57	10
subtype4	41	0

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	129	22	58	7
subtype1	27	18	2	3
subtype2	24	0	17	0
subtype3	44	4	23	3
subtype4	34	0	16	1

Figure S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00991 (Fisher's exact test), Q value = 0.044

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2
ALL	185	2	29
subtype1	45	0	3
subtype2	28	2	11
subtype3	69	0	8
subtype4	43	0	7

Figure S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'mRNA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.174 (Kruskal-Wallis (anova)), Q value = 0.35

Table S27. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	221	2.2 (4.7)
subtype1	52	2.8 (6.4)
subtype2	41	3.3 (6.4)
subtype3	76	1.5 (2.9)
subtype4	52	1.6 (2.6)

Figure S25. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.168 (Fisher's exact test), Q value = 0.34

Table S28. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15
subtype1	0	6
subtype2	2	3
subtype3	0	5
subtype4	0	1

Figure S26. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

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

Table S29. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	253	207	152

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

P value = 0.646 (logrank test), Q value = 0.81

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	593	121	0.0 - 148.0 (20.0)
subtype1	243	55	0.0 - 148.0 (20.0)
subtype2	201	35	0.1 - 131.5 (20.0)
subtype3	149	31	0.0 - 130.7 (21.4)

Figure S27. 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 'YEARS_TO_BIRTH'

P value = 0.512 (Kruskal-Wallis (anova)), Q value = 0.72

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	610	66.3 (12.8)
subtype1	251	66.7 (14.1)
subtype2	207	66.2 (11.2)
subtype3	152	65.7 (12.4)

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

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

nPatients	COLON	RECTUM
ALL	446	162
subtype1	210	43
subtype2	132	71
subtype3	104	48

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00323 (Fisher's exact test), Q value = 0.018

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	104	1	36	173	10	3	26	18	83	54	63	26	2
subtype1	53	1	18	82	6	0	12	5	28	19	16	6	1
subtype2	33	0	5	56	3	1	9	7	34	14	26	13	1
subtype3	18	0	13	35	1	2	5	6	21	21	21	7	0

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.555 (Fisher's exact test), Q value = 0.75

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	20	106	417	67
subtype1	10	50	163	29
subtype2	8	32	144	22
subtype3	2	24	110	16

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00014 (Fisher's exact test), Q value = 0.0012

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	345	150	115
subtype1	166	48	39
subtype2	105	67	34
subtype3	74	35	42

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00378 (Fisher's exact test), Q value = 0.02

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	450	88
subtype1	197	22
subtype2	146	39
subtype3	107	27

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.225 (Fisher's exact test), Q value = 0.4

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	287	325
subtype1	128	125
subtype2	88	119
subtype3	71	81

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.313 (Fisher's exact test), Q value = 0.51

Table S38. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	446	28
subtype1	187	8
subtype2	153	13
subtype3	106	7

Figure S35. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	382	60	146	13
subtype1	159	48	33	9
subtype2	127	5	71	0
subtype3	96	7	42	4

Figure S36. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0266 (Fisher's exact test), Q value = 0.088

Table S40. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	442	5	37	29
subtype1	185	3	9	17
subtype2	155	0	13	6
subtype3	102	2	15	6

Figure S37. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0028 (Kruskal-Wallis (anova)), Q value = 0.017

Table S41. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	576	2.2 (4.7)
subtype1	237	1.8 (4.3)
subtype2	195	1.9 (3.4)
subtype3	144	3.3 (6.5)

Figure S38. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.671 (Fisher's exact test), Q value = 0.82

Table S42. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	64	292
subtype1	1	7	26	127
subtype2	0	2	25	93
subtype3	0	3	13	72

Figure S39. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

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

Table S43. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	348
subtype1	2	153
subtype2	2	113
subtype3	1	82

Figure S40. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S44. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	101	159	130

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.75 (logrank test), Q value = 0.88

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	384	80	0.1 - 148.0 (19.8)
subtype1	99	20	0.7 - 148.0 (19.1)
subtype2	157	30	0.1 - 139.2 (21.0)
subtype3	128	30	0.1 - 140.4 (19.5)

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000195 (Kruskal-Wallis (anova)), Q value = 0.0016

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	388	64.4 (13.0)
subtype1	100	60.4 (13.6)
subtype2	159	64.3 (12.1)
subtype3	129	67.7 (12.8)

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.00135 (Fisher's exact test), Q value = 0.0091

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	292	96
subtype1	71	28
subtype2	109	50
subtype3	112	18

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.658 (Fisher's exact test), Q value = 0.81

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	54	1	20	114	7	3	11	15	61	35	29	25	2
subtype1	13	0	5	30	1	0	2	5	13	11	8	8	0
subtype2	18	0	9	44	2	2	4	9	31	11	12	12	1
subtype3	23	1	6	40	4	1	5	1	17	13	9	5	1

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.804 (Fisher's exact test), Q value = 0.91

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	56	271	50
subtype1	3	11	71	15
subtype2	5	22	114	18
subtype3	3	23	86	17

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.175 (Fisher's exact test), Q value = 0.35

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	213	103	71
subtype1	53	28	19
subtype2	82	50	25
subtype3	78	25	27

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.644 (Fisher's exact test), Q value = 0.81

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	264	53
subtype1	69	14
subtype2	108	25
subtype3	87	14

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.16 (Fisher's exact test), Q value = 0.33

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	178	212
subtype1	41	60
subtype2	69	90
subtype3	68	62

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.159 (Fisher's exact test), Q value = 0.33

Table S53. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	286	15
subtype1	79	4
subtype2	108	9
subtype3	99	2

Figure S49. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S54. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	251	38	90	6
subtype1	55	16	27	1
subtype2	104	3	48	2
subtype3	92	19	15	3

Figure S50. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.242 (Fisher's exact test), Q value = 0.41

Table S55. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	256	4	7	29
subtype1	68	1	1	5
subtype2	110	1	2	9
subtype3	78	2	4	15

Figure S51. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.948 (Kruskal-Wallis (anova)), Q value = 0.99

Table S56. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	354	2.3 (4.8)
subtype1	92	2.5 (5.0)
subtype2	145	2.1 (5.1)
subtype3	117	2.3 (4.2)

Figure S52. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.431 (Fisher's exact test), Q value = 0.65

Table S57. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	62	282
subtype1	1	3	15	78
subtype2	0	3	23	118
subtype3	0	6	24	86

Figure S53. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.86 (Fisher's exact test), Q value = 0.92

Table S58. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	335
subtype1	1	91
subtype2	3	136
subtype3	1	108

Figure S54. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S59. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	77	87	46	65	108	92	13

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.63 (logrank test), Q value = 0.81

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	474	98	0.0 - 140.4 (20.0)
subtype1	73	16	0.1 - 129.3 (17.8)
subtype2	86	22	0.5 - 140.4 (23.7)
subtype3	44	7	0.0 - 131.5 (21.0)
subtype4	62	17	0.7 - 124.3 (17.8)
subtype5	107	18	0.7 - 135.7 (20.0)
subtype6	89	17	0.0 - 88.2 (20.0)
subtype7	13	1	1.0 - 83.0 (19.0)

Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.621 (Kruskal-Wallis (anova)), Q value = 0.81

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	487	66.6 (12.7)
subtype1	77	66.3 (12.1)
subtype2	87	66.3 (11.4)
subtype3	45	67.3 (14.0)
subtype4	65	66.3 (14.0)
subtype5	108	65.1 (12.8)
subtype6	92	68.0 (13.0)
subtype7	13	71.1 (9.0)

Figure S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.624 (Fisher's exact test), Q value = 0.81

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	357	131
subtype1	59	18
subtype2	66	21
subtype3	30	16
subtype4	51	14
subtype5	79	29
subtype6	63	29
subtype7	9	4

Figure S57. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0119 (Fisher's exact test), Q value = 0.05

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	75	1	27	148	9	2	24	15	69	42	46	20	2
subtype1	10	0	0	25	0	0	1	3	15	9	8	6	0
subtype2	11	1	4	25	1	1	7	3	10	4	11	6	0
subtype3	10	0	0	11	0	0	4	0	7	6	5	1	0
subtype4	9	0	5	16	2	0	3	3	14	5	5	1	1
subtype5	16	0	5	41	4	1	3	4	14	12	2	5	1
subtype6	17	0	13	25	2	0	5	1	9	6	12	1	0
subtype7	2	0	0	5	0	0	1	1	0	0	3	0	0

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.913 (Fisher's exact test), Q value = 0.96

Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	81	339	55
subtype1	0	11	55	11
subtype2	2	13	61	10
subtype3	2	8	31	4
subtype4	1	12	44	8
subtype5	4	15	74	15
subtype6	2	19	64	7
subtype7	0	3	10	0

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.283 (Fisher's exact test), Q value = 0.47

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	274	122	89
subtype1	37	20	20
subtype2	47	27	13
subtype3	22	10	13
subtype4	34	19	12
subtype5	68	24	16
subtype6	59	18	14
subtype7	7	4	1

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.128 (Fisher's exact test), Q value = 0.28

Table S66. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	367	67
subtype1	54	14
subtype2	57	16
subtype3	35	6
subtype4	49	7
subtype5	89	8
subtype6	75	13
subtype7	8	3

Figure S61. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.354 (Fisher's exact test), Q value = 0.55

Table S67. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	233	255
subtype1	37	40
subtype2	37	50
subtype3	29	17
subtype4	34	31
subtype5	47	61
subtype6	43	49
subtype7	6	7

Figure S62. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.778 (Fisher's exact test), Q value = 0.9

Table S68. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	357	23
subtype1	52	2
subtype2	62	3
subtype3	32	1
subtype4	43	2
subtype5	82	8
subtype6	74	7
subtype7	12	0

Figure S63. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.226 (Fisher's exact test), Q value = 0.4

Table S69. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	312	42	118	10
subtype1	49	10	17	1
subtype2	61	5	18	3
subtype3	28	2	15	0
subtype4	47	2	12	2
subtype5	62	16	28	1
subtype6	56	7	25	2
subtype7	9	0	3	1

Figure S64. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00079 (Fisher's exact test), Q value = 0.0058

Table S70. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	363	2	29	19
subtype1	57	1	1	3
subtype2	60	0	7	1
subtype3	33	0	6	2
subtype4	42	1	2	5
subtype5	90	0	1	2
subtype6	73	0	9	4
subtype7	8	0	3	2

Figure S65. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.246 (Kruskal-Wallis (anova)), Q value = 0.42

Table S71. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	460	2.1 (4.6)
subtype1	73	3.2 (7.1)
subtype2	84	2.1 (4.4)
subtype3	43	2.8 (4.4)
subtype4	59	2.2 (4.3)
subtype5	103	1.5 (2.8)
subtype6	87	1.7 (4.1)
subtype7	11	1.3 (1.7)

Figure S66. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.00284 (Fisher's exact test), Q value = 0.017

Table S72. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	51	246
subtype1	1	3	12	49
subtype2	0	0	9	43
subtype3	0	4	7	14
subtype4	0	1	8	35
subtype5	0	0	10	82
subtype6	0	4	4	21
subtype7	0	0	1	2

Figure S67. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.0335 (Fisher's exact test), Q value = 0.11

Table S73. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	297
subtype1	0	65
subtype2	0	49
subtype3	1	20
subtype4	0	44
subtype5	0	91
subtype6	1	27
subtype7	0	1

Figure S68. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S74. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	236	166	86

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.817 (logrank test), Q value = 0.91

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	474	98	0.0 - 140.4 (20.0)
subtype1	231	42	0.0 - 135.7 (18.8)
subtype2	161	41	0.1 - 140.4 (22.1)
subtype3	82	15	0.0 - 88.2 (20.0)

Figure S69. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.125 (Kruskal-Wallis (anova)), Q value = 0.28

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	487	66.6 (12.7)
subtype1	236	65.8 (12.3)
subtype2	165	66.7 (12.6)
subtype3	86	68.4 (13.8)

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.169 (Fisher's exact test), Q value = 0.34

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	357	131
subtype1	175	61
subtype2	126	40
subtype3	56	30

Figure S71. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00408 (Fisher's exact test), Q value = 0.021

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	75	1	27	148	9	2	24	15	69	42	46	20	2
subtype1	36	1	6	84	3	1	5	7	35	23	16	13	1
subtype2	28	0	9	47	4	1	12	6	20	15	15	5	1
subtype3	11	0	12	17	2	0	7	2	14	4	15	2	0

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.605 (Fisher's exact test), Q value = 0.8

Table S79. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	81	339	55
subtype1	5	35	168	26
subtype2	3	33	108	22
subtype3	3	13	63	7

Figure S73. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.595 (Fisher's exact test), Q value = 0.79

Table S80. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	274	122	89
subtype1	137	54	43
subtype2	91	41	33
subtype3	46	27	13

Figure S74. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.445 (Fisher's exact test), Q value = 0.65

Table S81. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	367	67
subtype1	180	31
subtype2	123	20
subtype3	64	16

Figure S75. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.647 (Fisher's exact test), Q value = 0.81

Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	233	255
subtype1	111	125
subtype2	77	89
subtype3	45	41

Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.18 (Fisher's exact test), Q value = 0.35

Table S83. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	357	23
subtype1	177	10
subtype2	115	5
subtype3	65	8

Figure S77. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.274 (Fisher's exact test), Q value = 0.46

Table S84. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	312	42	118	10
subtype1	147	27	57	4
subtype2	113	11	35	4
subtype3	52	4	26	2

Figure S78. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00015 (Fisher's exact test), Q value = 0.0013

Table S85. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	363	2	29	19
subtype1	192	1	4	7
subtype2	106	1	13	10
subtype3	65	0	12	2

Figure S79. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.679 (Kruskal-Wallis (anova)), Q value = 0.82

Table S86. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	460	2.1 (4.6)
subtype1	224	2.1 (5.0)
subtype2	153	2.4 (4.3)
subtype3	83	1.7 (3.8)

Figure S80. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.787 (Fisher's exact test), Q value = 0.9

Table S87. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	51	246
subtype1	1	6	27	144
subtype2	0	4	20	77
subtype3	0	2	4	25

Figure S81. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.077 (Fisher's exact test), Q value = 0.2

Table S88. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	297
subtype1	0	173
subtype2	1	94
subtype3	1	30

Figure S82. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S89. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	148	142	131	199

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0987 (logrank test), Q value = 0.23

Table S90. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	602	121	0.0 - 148.0 (20.0)
subtype1	144	37	0.1 - 140.4 (21.0)
subtype2	141	23	0.0 - 135.7 (20.0)
subtype3	127	32	0.1 - 148.0 (17.8)
subtype4	190	29	0.0 - 117.1 (21.5)

Figure S83. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00283 (Kruskal-Wallis (anova)), Q value = 0.017

Table S91. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	618	66.4 (12.7)
subtype1	148	63.8 (12.3)
subtype2	141	66.4 (13.4)
subtype3	130	65.5 (13.2)
subtype4	199	68.8 (11.9)

Figure S84. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.0959 (Fisher's exact test), Q value = 0.23

Table S92. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	453	163
subtype1	103	45
subtype2	110	30
subtype3	103	28
subtype4	137	60

Figure S85. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00386 (Fisher's exact test), Q value = 0.02

Table S93. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	104	1	37	178	11	3	27	18	83	54	62	26	2
subtype1	23	0	7	37	2	2	4	4	24	11	14	13	0
subtype2	29	1	11	42	2	1	7	6	19	7	7	6	2
subtype3	12	0	5	44	2	0	3	3	18	18	16	6	0
subtype4	40	0	14	55	5	0	13	5	22	18	25	1	0

Figure S86. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0414 (Fisher's exact test), Q value = 0.13

Table S94. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	20	106	423	69
subtype1	6	25	99	17
subtype2	4	30	94	13
subtype3	1	13	94	23
subtype4	9	38	136	16

Figure S87. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.112 (Fisher's exact test), Q value = 0.26

Table S95. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	352	150	115
subtype1	83	40	23
subtype2	89	34	19
subtype3	64	31	35
subtype4	116	45	38

Figure S88. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0843 (Fisher's exact test), Q value = 0.21

Table S96. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	458	87
subtype1	96	27
subtype2	101	13
subtype3	91	21
subtype4	170	26

Figure S89. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.608 (Fisher's exact test), Q value = 0.8

Table S97. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	289	331
subtype1	64	84
subtype2	68	74
subtype3	58	73
subtype4	99	100

Figure S90. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0673 (Fisher's exact test), Q value = 0.17

Table S98. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	454	29
subtype1	97	6
subtype2	111	6
subtype3	105	2
subtype4	141	15

Figure S91. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00022

Table S99. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	388	61	147	13
subtype1	99	2	45	0
subtype2	85	23	24	5
subtype3	85	18	25	3
subtype4	119	18	53	5

Figure S92. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 2e-05 (Fisher's exact test), Q value = 0.00022

Table S100. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	449	5	36	30
subtype1	90	1	7	10
subtype2	94	2	3	9
subtype3	94	1	4	11
subtype4	171	1	22	0

Figure S93. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0494 (Kruskal-Wallis (anova)), Q value = 0.14

Table S101. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	583	2.2 (4.7)
subtype1	133	1.9 (3.9)
subtype2	132	1.8 (4.1)
subtype3	121	3.2 (6.5)
subtype4	197	2.1 (4.3)

Figure S94. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.863 (Fisher's exact test), Q value = 0.92

Table S102. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	64	294
subtype1	0	4	21	96
subtype2	0	4	22	77
subtype3	1	4	17	101
subtype4	0	0	4	20

Figure S95. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.237 (Fisher's exact test), Q value = 0.41

Table S103. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	349
subtype1	4	112
subtype2	0	97
subtype3	1	116
subtype4	0	24

Figure S96. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S104. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	80	86	64	57	90	140	103

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.445 (logrank test), Q value = 0.65

Table S105. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	602	121	0.0 - 148.0 (20.0)
subtype1	79	20	0.1 - 148.0 (18.2)
subtype2	85	16	0.8 - 133.2 (19.1)
subtype3	61	16	0.1 - 135.7 (20.0)
subtype4	56	7	1.4 - 91.8 (20.9)
subtype5	90	20	0.4 - 129.3 (16.6)
subtype6	131	20	0.0 - 117.1 (21.0)
subtype7	100	22	0.0 - 64.0 (24.0)

Figure S97. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00104 (Kruskal-Wallis (anova)), Q value = 0.0073

Table S106. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	618	66.4 (12.7)
subtype1	80	64.5 (13.0)
subtype2	86	65.0 (13.4)
subtype3	62	64.7 (15.4)
subtype4	57	64.7 (12.8)
subtype5	90	63.9 (11.6)
subtype6	140	68.0 (10.9)
subtype7	103	70.8 (12.2)

Figure S98. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S107. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	453	163
subtype1	58	22
subtype2	73	13
subtype3	60	4
subtype4	32	23
subtype5	59	31
subtype6	88	51
subtype7	83	19

Figure S99. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00031

Table S108. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	104	1	37	178	11	3	27	18	83	54	62	26	2
subtype1	14	0	1	25	1	1	1	3	12	9	5	5	0
subtype2	15	1	6	24	1	0	3	4	14	5	5	5	1
subtype3	13	0	4	28	1	0	1	1	4	6	2	1	1
subtype4	8	0	3	8	1	1	5	4	11	3	6	5	0
subtype5	7	0	3	26	1	1	1	1	17	11	9	9	0
subtype6	28	0	7	37	1	0	8	4	16	14	25	0	0
subtype7	19	0	13	30	5	0	8	1	9	6	10	1	0

Figure S100. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.68 (Fisher's exact test), Q value = 0.82

Table S109. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	20	106	423	69
subtype1	2	14	55	9
subtype2	5	15	53	12
subtype3	2	11	44	7
subtype4	1	9	40	7
subtype5	0	10	66	13
subtype6	6	30	94	10
subtype7	4	17	71	11

Figure S101. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0149 (Fisher's exact test), Q value = 0.059

Table S110. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	352	150	115
subtype1	44	18	17
subtype2	52	23	11
subtype3	46	9	9
subtype4	26	22	9
subtype5	39	27	22
subtype6	77	34	29
subtype7	68	17	18

Figure S102. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.101 (Fisher's exact test), Q value = 0.24

Table S111. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	458	87
subtype1	56	10
subtype2	57	8
subtype3	46	4
subtype4	34	11
subtype5	61	18
subtype6	113	25
subtype7	91	11

Figure S103. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.361 (Fisher's exact test), Q value = 0.55

Table S112. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	289	331
subtype1	37	43
subtype2	41	45
subtype3	29	35
subtype4	18	39
subtype5	43	47
subtype6	68	72
subtype7	53	50

Figure S104. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.726 (Fisher's exact test), Q value = 0.86

Table S113. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	454	29
subtype1	48	3
subtype2	71	2
subtype3	48	2
subtype4	45	3
subtype5	69	4
subtype6	96	10
subtype7	77	5

Figure S105. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S114. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	388	61	147	13
subtype1	55	2	22	0
subtype2	58	14	11	2
subtype3	47	12	3	1
subtype4	30	2	22	1
subtype5	53	6	30	1
subtype6	85	3	50	0
subtype7	60	22	9	8

Figure S106. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S115. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	449	5	36	30
subtype1	45	0	2	8
subtype2	58	0	1	4
subtype3	41	1	1	9
subtype4	34	2	2	2
subtype5	66	0	0	7
subtype6	115	2	22	0
subtype7	90	0	8	0

Figure S107. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0207 (Kruskal-Wallis (anova)), Q value = 0.074

Table S116. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	583	2.2 (4.7)
subtype1	72	2.1 (3.2)
subtype2	78	1.8 (4.5)
subtype3	57	1.4 (3.3)
subtype4	53	2.6 (5.1)
subtype5	82	3.1 (6.5)
subtype6	138	2.2 (4.4)
subtype7	103	2.2 (5.1)

Figure S108. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0561 (Fisher's exact test), Q value = 0.16

Table S117. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	64	294
subtype1	0	3	13	59
subtype2	0	0	23	49
subtype3	0	4	8	49
subtype4	0	1	4	43
subtype5	1	4	11	71
subtype6	0	0	4	13
subtype7	0	0	1	10

Figure S109. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.937 (Fisher's exact test), Q value = 0.98

Table S118. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	349
subtype1	1	70
subtype2	2	67
subtype3	1	58
subtype4	0	45
subtype5	1	81
subtype6	0	17
subtype7	0	11

Figure S110. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S119. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	212	96	241

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0609 (logrank test), Q value = 0.16

Table S120. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	531	110	0.0 - 148.0 (21.0)
subtype1	209	42	0.1 - 148.0 (20.9)
subtype2	94	24	0.1 - 109.3 (17.5)
subtype3	228	44	0.0 - 117.1 (22.2)

Figure S111. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00875 (Kruskal-Wallis (anova)), Q value = 0.04

Table S121. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	548	66.8 (12.7)
subtype1	211	65.9 (12.5)
subtype2	96	64.0 (13.8)
subtype3	241	68.8 (12.1)

Figure S112. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.0396 (Fisher's exact test), Q value = 0.12

Table S122. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	405	140
subtype1	150	61
subtype2	80	15
subtype3	175	64

Figure S113. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00028 (Fisher's exact test), Q value = 0.0021

Table S123. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	94	1	36	154	10	2	27	13	72	46	57	22	1
subtype1	32	1	8	67	4	2	8	7	35	14	16	10	1
subtype2	9	0	5	26	0	0	3	2	16	14	7	8	0
subtype3	53	0	23	61	6	0	16	4	21	18	34	4	0

Figure S114. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0969 (Fisher's exact test), Q value = 0.23

Table S124. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	20	94	377	56
subtype1	8	32	152	18
subtype2	2	10	69	15
subtype3	10	52	156	23

Figure S115. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0152 (Fisher's exact test), Q value = 0.059

Table S125. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	312	134	100
subtype1	120	59	31
subtype2	43	29	23
subtype3	149	46	46

Figure S116. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.817 (Fisher's exact test), Q value = 0.91

Table S126. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	412	78
subtype1	149	26
subtype2	65	14
subtype3	198	38

Figure S117. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.543 (Fisher's exact test), Q value = 0.74

Table S127. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	257	292
subtype1	102	110
subtype2	40	56
subtype3	115	126

Figure S118. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.71 (Fisher's exact test), Q value = 0.85

Table S128. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	399	25
subtype1	144	7
subtype2	73	5
subtype3	182	13

Figure S119. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00049

Table S129. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	348	53	127	10
subtype1	138	9	60	1
subtype2	60	20	13	2
subtype3	150	24	54	7

Figure S120. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S130. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	384	5	36	26
subtype1	121	2	5	17
subtype2	66	0	2	7
subtype3	197	3	29	2

Figure S121. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0488 (Kruskal-Wallis (anova)), Q value = 0.14

Table S131. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	514	2.2 (4.7)
subtype1	189	1.8 (3.5)
subtype2	89	3.3 (7.1)
subtype3	236	2.1 (4.4)

Figure S122. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.659 (Fisher's exact test), Q value = 0.81

Table S132. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	26	280
subtype1	0	7	14	171
subtype2	1	4	7	73
subtype3	0	1	5	36

Figure S123. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

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

Table S133. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	305
subtype1	2	184
subtype2	0	80
subtype3	0	41

Figure S124. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S134. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	229	144	176

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.846 (logrank test), Q value = 0.92

Table S135. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	531	110	0.0 - 148.0 (21.0)
subtype1	226	51	0.1 - 140.4 (19.3)
subtype2	138	28	0.0 - 148.0 (20.7)
subtype3	167	31	0.0 - 109.3 (25.0)

Figure S125. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00428 (Kruskal-Wallis (anova)), Q value = 0.021

Table S136. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	548	66.8 (12.7)
subtype1	228	65.8 (12.8)
subtype2	144	65.1 (13.0)
subtype3	176	69.6 (11.9)

Figure S126. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_TISSUE_SITE'

P value = 0.261 (Fisher's exact test), Q value = 0.44

Table S137. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	405	140
subtype1	176	51
subtype2	100	43
subtype3	129	46

Figure S127. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.012 (Fisher's exact test), Q value = 0.05

Table S138. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	94	1	36	154	10	2	27	13	72	46	57	22	1
subtype1	36	0	10	78	4	1	7	6	31	19	13	13	1
subtype2	22	1	6	34	2	1	8	5	21	14	23	4	0
subtype3	36	0	20	42	4	0	12	2	20	13	21	5	0

Figure S128. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.903 (Fisher's exact test), Q value = 0.96

Table S139. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	20	94	377	56
subtype1	8	36	159	25
subtype2	6	22	101	14
subtype3	6	36	117	17

Figure S129. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.147 (Fisher's exact test), Q value = 0.31

Table S140. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	312	134	100
subtype1	137	54	36
subtype2	69	42	32
subtype3	106	38	32

Figure S130. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.192 (Fisher's exact test), Q value = 0.37

Table S141. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	412	78
subtype1	163	25
subtype2	103	27
subtype3	146	26

Figure S131. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.52 (Fisher's exact test), Q value = 0.72

Table S142. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	257	292
subtype1	101	128
subtype2	72	72
subtype3	84	92

Figure S132. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.84 (Fisher's exact test), Q value = 0.92

Table S143. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	399	25
subtype1	164	11
subtype2	98	7
subtype3	137	7

Figure S133. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.237 (Fisher's exact test), Q value = 0.41

Table S144. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	348	53	127	10
subtype1	149	25	49	2
subtype2	90	8	40	3
subtype3	109	20	38	5

Figure S134. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.00013 (Fisher's exact test), Q value = 0.0012

Table S145. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	384	5	36	26
subtype1	142	2	4	17
subtype2	95	1	15	7
subtype3	147	2	17	2

Figure S135. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.127 (Kruskal-Wallis (anova)), Q value = 0.28

Table S146. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	514	2.2 (4.7)
subtype1	212	1.9 (3.9)
subtype2	129	2.5 (5.4)
subtype3	173	2.3 (5.2)

Figure S136. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.791 (Fisher's exact test), Q value = 0.9

Table S147. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	26	280
subtype1	1	8	15	181
subtype2	0	4	7	68
subtype3	0	0	4	31

Figure S137. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.594 (Fisher's exact test), Q value = 0.79

Table S148. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	305
subtype1	1	195
subtype2	1	75
subtype3	0	35

Figure S138. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S149. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	24	20	29	28	18	17

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

P value = 0.855 (logrank test), Q value = 0.92

Table S150. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	134	39	0.1 - 140.4 (20.6)
subtype1	24	6	0.2 - 130.7 (22.6)
subtype2	19	6	0.1 - 74.8 (16.2)
subtype3	29	7	0.4 - 129.3 (24.1)
subtype4	27	10	1.4 - 140.4 (13.3)
subtype5	18	5	1.9 - 139.2 (20.0)
subtype6	17	5	0.7 - 131.5 (21.4)

Figure S139. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.201 (Kruskal-Wallis (anova)), Q value = 0.38

Table S151. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	135	64.9 (12.6)
subtype1	24	68.0 (12.5)
subtype2	20	62.6 (14.2)
subtype3	29	61.9 (12.3)
subtype4	28	66.4 (10.9)
subtype5	18	62.4 (12.8)
subtype6	16	68.6 (13.2)

Figure S140. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S152. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	85	50
subtype1	13	11
subtype2	20	0
subtype3	6	22
subtype4	16	12
subtype5	16	2
subtype6	14	3

Figure S141. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.194 (Fisher's exact test), Q value = 0.37

Table S153. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	15	7	39	1	1	2	4	21	14	13	9
subtype1	6	1	7	0	0	1	0	4	1	2	1
subtype2	2	1	4	0	0	0	0	4	4	4	1
subtype3	0	1	8	0	0	0	1	5	4	4	4
subtype4	1	0	10	0	1	1	3	4	2	0	1
subtype5	2	2	4	1	0	0	0	1	1	3	2
subtype6	4	2	6	0	0	0	0	3	2	0	0

Figure S142. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.143 (Fisher's exact test), Q value = 0.31

Table S154. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	4	16	100	15
subtype1	2	5	15	2
subtype2	0	2	16	2
subtype3	0	0	26	2
subtype4	1	3	21	3
subtype5	0	3	10	5
subtype6	1	3	12	1

Figure S143. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.214 (Fisher's exact test), Q value = 0.4

Table S155. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	65	39	30
subtype1	13	6	5
subtype2	7	5	8
subtype3	9	10	8
subtype4	13	10	5
subtype5	10	6	2
subtype6	13	2	2

Figure S144. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0254 (Fisher's exact test), Q value = 0.087

Table S156. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	87	21
subtype1	17	2
subtype2	11	5
subtype3	17	7
subtype4	18	2
subtype5	8	5
subtype6	16	0

Figure S145. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.857 (Fisher's exact test), Q value = 0.92

Table S157. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	58	78
subtype1	8	16
subtype2	9	11
subtype3	15	14
subtype4	12	16
subtype5	7	11
subtype6	7	10

Figure S146. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.361 (Fisher's exact test), Q value = 0.55

Table S158. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	93	6
subtype1	16	2
subtype2	17	0
subtype3	18	3
subtype4	15	1
subtype5	14	0
subtype6	13	0

Figure S147. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S159. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	74	9	49	1
subtype1	13	0	11	0
subtype2	16	4	0	0
subtype3	6	0	22	0
subtype4	13	1	11	1
subtype5	12	4	2	0
subtype6	14	0	3	0

Figure S148. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.534 (Fisher's exact test), Q value = 0.74

Table S160. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	73	1	2	7
subtype1	12	0	0	2
subtype2	14	0	0	0
subtype3	18	0	2	2
subtype4	11	1	0	0
subtype5	9	0	0	1
subtype6	9	0	0	2

Figure S149. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0318 (Kruskal-Wallis (anova)), Q value = 0.1

Table S161. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	125	2.9 (6.2)
subtype1	23	2.6 (4.4)
subtype2	19	6.9 (11.6)
subtype3	25	3.1 (4.7)
subtype4	26	2.0 (5.4)
subtype5	17	1.4 (3.0)
subtype6	15	1.1 (2.3)

Figure S150. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.218 (Fisher's exact test), Q value = 0.4

Table S162. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	8	117
subtype1	0	0	2	20
subtype2	1	0	2	16
subtype3	0	1	0	25
subtype4	0	0	1	27
subtype5	0	0	2	15
subtype6	0	2	1	14

Figure S151. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S163. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	55	15	34	32

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

P value = 0.503 (logrank test), Q value = 0.72

Table S164. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	134	39	0.1 - 140.4 (20.6)
subtype1	54	19	0.1 - 100.0 (20.0)
subtype2	15	3	0.7 - 129.3 (16.3)
subtype3	34	9	0.2 - 131.5 (24.4)
subtype4	31	8	0.4 - 140.4 (17.5)

Figure S152. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.516 (Kruskal-Wallis (anova)), Q value = 0.72

Table S165. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	135	64.9 (12.6)
subtype1	55	64.6 (13.8)
subtype2	15	61.7 (12.3)
subtype3	33	67.3 (12.6)
subtype4	32	64.3 (10.5)

Figure S153. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S166. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	COLON	RECTUM
ALL	85	50
subtype1	50	5
subtype2	5	9
subtype3	18	16
subtype4	12	20

Figure S154. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.767 (Fisher's exact test), Q value = 0.89

Table S167. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	15	7	39	1	1	2	4	21	14	13	9
subtype1	9	3	15	1	0	0	0	7	7	6	2
subtype2	0	1	5	0	0	0	1	4	1	1	2
subtype3	4	2	9	0	1	1	2	7	3	2	1
subtype4	2	1	10	0	0	1	1	3	3	4	4

Figure S155. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.441 (Fisher's exact test), Q value = 0.65

Table S168. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	4	16	100	15
subtype1	1	9	36	9
subtype2	0	0	14	1
subtype3	2	5	25	2
subtype4	1	2	25	3

Figure S156. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.749 (Fisher's exact test), Q value = 0.88

Table S169. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	65	39	30
subtype1	31	12	12
subtype2	6	5	4
subtype3	14	12	8
subtype4	14	10	6

Figure S157. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.473 (Fisher's exact test), Q value = 0.68

Table S170. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	87	21
subtype1	34	8
subtype2	10	2
subtype3	23	3
subtype4	20	8

Figure S158. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.323 (Fisher's exact test), Q value = 0.52

Table S171. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients	FEMALE	MALE
ALL	58	78
subtype1	23	32
subtype2	9	6
subtype3	11	23
subtype4	15	17

Figure S159. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00861 (Fisher's exact test), Q value = 0.04

Table S172. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	93	6
subtype1	48	0
subtype2	7	2
subtype3	20	3
subtype4	18	1

Figure S160. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00013

Table S173. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	74	9	49	1
subtype1	42	8	5	0
subtype2	5	0	9	0
subtype3	18	0	15	1
subtype4	9	1	20	0

Figure S161. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.357 (Fisher's exact test), Q value = 0.55

Table S174. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	73	1	2	7
subtype1	33	0	0	3
subtype2	11	0	0	1
subtype3	13	1	0	2
subtype4	16	0	2	1

Figure S162. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.348 (Kruskal-Wallis (anova)), Q value = 0.55

Table S175. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	125	2.9 (6.2)
subtype1	51	3.0 (7.7)
subtype2	14	3.6 (5.8)
subtype3	32	3.3 (5.8)
subtype4	28	1.8 (2.9)

Figure S163. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.795 (Fisher's exact test), Q value = 0.9

Table S176. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	8	117
subtype1	1	1	5	46
subtype2	0	1	0	13
subtype3	0	1	2	30
subtype4	0	0	1	28

Figure S164. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/COADREAD-TP/20147373/COADREAD-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/COADREAD-TP/19775152/COADREAD-TP.merged_data.txt
Number of patients = 625
Number of clustering approaches = 12
Number of selected clinical features = 14
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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