Correlation between aggregated molecular cancer subtypes and selected clinical features

Colon Adenocarcinoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1H41QM7

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 13 clinical features across 456 patients, 36 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 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'HISTOLOGICAL_TYPE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'HISTOLOGICAL_TYPE' and 'RESIDUAL_TUMOR'.
Consensus hierarchical clustering analysis on RPPA data identified 9 subtypes that correlate to 'RESIDUAL_TUMOR'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'RESIDUAL_TUMOR'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'GENDER' and 'NUMBER_OF_LYMPH_NODES'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 36 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	0.86 (0.979)	0.364 (0.711)	0.943 (1.00)	0.829 (0.963)	0.583 (0.881)	0.604 (0.881)	0.408 (0.767)	0.825 (0.963)	0.0816 (0.283)	0.805 (0.963)	0.131 (0.411)	0.714 (0.95)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.597 (0.881)	0.0658 (0.258)	0.166 (0.446)	1.87e-05 (0.000416)	0.172 (0.453)	0.0607 (0.249)	0.00915 (0.0529)	0.000153 (0.00184)	0.000566 (0.0049)	0.000101 (0.00143)	0.00416 (0.027)	0.589 (0.881)
PATHOLOGIC STAGE	Fisher's exact test	0.00284 (0.0193)	0.63 (0.885)	0.00034 (0.00331)	0.519 (0.835)	0.385 (0.732)	0.0663 (0.258)	0.0794 (0.283)	0.00034 (0.00331)	0.0339 (0.165)	0.147 (0.44)	0.553 (0.862)	0.817 (0.963)
PATHOLOGY T STAGE	Fisher's exact test	0.68 (0.922)	0.647 (0.902)	0.771 (0.955)	0.252 (0.563)	0.743 (0.95)	0.822 (0.963)	0.493 (0.827)	0.75 (0.952)	0.344 (0.696)	0.872 (0.986)	0.58 (0.881)	0.836 (0.963)
PATHOLOGY N STAGE	Fisher's exact test	0.0082 (0.0512)	0.788 (0.963)	0.00192 (0.015)	0.623 (0.884)	0.702 (0.944)	0.177 (0.453)	0.365 (0.711)	0.091 (0.309)	0.338 (0.694)	0.113 (0.369)	0.24 (0.55)	0.0763 (0.283)
PATHOLOGY M STAGE	Fisher's exact test	0.0945 (0.314)	0.6 (0.881)	0.00039 (0.00358)	0.158 (0.443)	0.276 (0.603)	0.764 (0.955)	0.0255 (0.129)	0.0481 (0.208)	0.565 (0.873)	0.538 (0.856)	0.228 (0.536)	0.132 (0.411)
GENDER	Fisher's exact test	0.035 (0.165)	0.0814 (0.283)	0.445 (0.797)	0.14 (0.429)	0.508 (0.835)	0.623 (0.884)	0.0801 (0.283)	0.182 (0.457)	0.446 (0.797)	0.475 (0.815)	0.731 (0.95)	0.00111 (0.00911)
RADIATION THERAPY	Fisher's exact test	0.202 (0.493)	0.433 (0.794)	0.736 (0.95)	0.738 (0.95)	1 (1.00)	0.803 (0.963)	0.839 (0.963)	0.944 (1.00)	0.157 (0.443)	0.726 (0.95)
HISTOLOGICAL TYPE	Fisher's exact test	4e-05 (0.000693)	1e-05 (0.00026)	1e-05 (0.00026)	5e-05 (0.00078)	0.0164 (0.0915)	0.362 (0.711)	1e-05 (0.00026)	1e-05 (0.00026)	0.0428 (0.196)	0.207 (0.498)	0.025 (0.129)	0.247 (0.558)
RESIDUAL TUMOR	Fisher's exact test	0.16 (0.443)	0.545 (0.859)	0.0544 (0.23)	0.418 (0.777)	3e-05 (0.000585)	0.00867 (0.052)	0.00013 (0.00169)	1e-05 (0.00026)	1e-05 (0.00026)	0.00027 (0.00301)	0.484 (0.821)	0.942 (1.00)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.00264 (0.0187)	0.765 (0.955)	0.0021 (0.0156)	0.962 (1.00)	0.612 (0.884)	0.291 (0.621)	0.309 (0.652)	0.186 (0.46)	0.517 (0.835)	0.162 (0.443)	0.0241 (0.129)	0.0469 (0.208)
RACE	Fisher's exact test	0.455 (0.797)	1 (1.00)	0.902 (0.998)	0.882 (0.989)	0.326 (0.677)	0.23 (0.536)	0.452 (0.797)	0.838 (0.963)	0.676 (0.922)	0.278 (0.603)	0.663 (0.916)	0.993 (1.00)
ETHNICITY	Fisher's exact test			0.381 (0.732)	1 (1.00)	0.176 (0.453)	0.155 (0.443)	0.46 (0.798)	0.89 (0.992)	1 (1.00)	0.519 (0.835)

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	38	61	30	24

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.86 (logrank test), Q value = 0.98

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	146	30	0.0 - 54.0 (23.5)
subtype1	35	8	0.0 - 46.6 (24.0)
subtype2	57	13	0.0 - 51.0 (24.0)
subtype3	30	5	0.0 - 54.0 (18.6)
subtype4	24	4	0.0 - 50.0 (26.0)

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 = 0.597 (Kruskal-Wallis (anova)), Q value = 0.88

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

	nPatients	Mean (Std.Dev)
ALL	153	70.8 (11.5)
subtype1	38	72.4 (11.4)
subtype2	61	69.9 (11.7)
subtype3	30	72.0 (10.9)
subtype4	24	68.9 (12.3)

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

'mRNA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00284 (Fisher's exact test), Q value = 0.019

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

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	29	12	45	5	8	3	12	16	21	1
subtype1	6	1	14	3	3	1	3	1	5	0
subtype2	11	2	12	0	2	2	5	13	14	0
subtype3	7	6	9	2	1	0	3	0	1	1
subtype4	5	3	10	0	2	0	1	2	1	0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.68 (Fisher's exact test), Q value = 0.92

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

nPatients	T1	T2	T3	T4
ALL	4	31	103	15
subtype1	1	7	24	6
subtype2	2	12	42	5
subtype3	0	7	19	4
subtype4	1	5	18	0

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0082 (Fisher's exact test), Q value = 0.051

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

nPatients	N0	N1	N2
ALL	94	28	31
subtype1	25	5	8
subtype2	27	15	19
subtype3	24	5	1
subtype4	18	3	3

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0945 (Fisher's exact test), Q value = 0.31

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

nPatients	0	1
ALL	129	22
subtype1	32	5
subtype2	47	14
subtype3	28	2
subtype4	22	1

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.035 (Fisher's exact test), Q value = 0.17

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

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

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.202 (Fisher's exact test), Q value = 0.49

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

nPatients	NO	YES
ALL	123	3
subtype1	30	0
subtype2	49	1
subtype3	24	0
subtype4	20	2

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00069

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	129	22
subtype1	25	13
subtype2	60	1
subtype3	22	7
subtype4	22	1

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

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.16 (Fisher's exact test), Q value = 0.44

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

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

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

'mRNA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00264 (Kruskal-Wallis (anova)), Q value = 0.019

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

	nPatients	Mean (Std.Dev)
ALL	152	2.1 (4.5)
subtype1	38	2.7 (5.8)
subtype2	61	2.8 (4.8)
subtype3	29	0.7 (2.3)
subtype4	24	1.2 (2.6)

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.455 (Fisher's exact test), Q value = 0.8

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

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11
subtype1	0	6
subtype2	1	4
subtype3	0	0
subtype4	0	1

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	46	73	34

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.364 (logrank test), Q value = 0.71

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	146	30	0.0 - 54.0 (23.5)
subtype1	44	10	0.0 - 53.0 (22.5)
subtype2	72	17	0.0 - 54.0 (23.0)
subtype3	30	3	0.9 - 39.0 (24.0)

Figure S13. 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 = 0.0658 (Kruskal-Wallis (anova)), Q value = 0.26

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

	nPatients	Mean (Std.Dev)
ALL	153	70.8 (11.5)
subtype1	46	73.4 (11.8)
subtype2	73	70.6 (10.6)
subtype3	34	67.5 (12.5)

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

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.63 (Fisher's exact test), Q value = 0.89

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	29	12	45	5	8	3	12	16	21	1
subtype1	9	3	14	4	3	1	5	1	5	0
subtype2	12	7	20	1	4	2	4	10	12	1
subtype3	8	2	11	0	1	0	3	5	4	0

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.647 (Fisher's exact test), Q value = 0.9

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

nPatients	T1	T2	T3	T4
ALL	4	31	103	15
subtype1	1	10	28	7
subtype2	2	13	51	7
subtype3	1	8	24	1

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.788 (Fisher's exact test), Q value = 0.96

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

nPatients	N0	N1	N2
ALL	94	28	31
subtype1	31	7	8
subtype2	42	16	15
subtype3	21	5	8

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.6 (Fisher's exact test), Q value = 0.88

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	129	22
subtype1	40	5
subtype2	60	13
subtype3	29	4

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0814 (Fisher's exact test), Q value = 0.28

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

nPatients	FEMALE	MALE
ALL	75	78
subtype1	28	18
subtype2	35	38
subtype3	12	22

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

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.433 (Fisher's exact test), Q value = 0.79

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	123	3
subtype1	35	0
subtype2	60	3
subtype3	28	0

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00026

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	129	22
subtype1	27	18
subtype2	69	4
subtype3	33	0

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

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.545 (Fisher's exact test), Q value = 0.86

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

nPatients	R0	R1	R2
ALL	128	1	19
subtype1	39	0	3
subtype2	60	1	12
subtype3	29	0	4

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

'mRNA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.765 (Kruskal-Wallis (anova)), Q value = 0.96

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

	nPatients	Mean (Std.Dev)
ALL	152	2.1 (4.5)
subtype1	46	2.3 (5.3)
subtype2	72	2.2 (4.6)
subtype3	34	1.7 (2.7)

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

'mRNA cHierClus subtypes' versus 'RACE'

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

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11
subtype1	0	5
subtype2	1	5
subtype3	0	1

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

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

Table S27. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	202	168	77

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

P value = 0.943 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	433	99	0.0 - 148.0 (21.9)
subtype1	194	47	0.0 - 148.0 (21.6)
subtype2	162	36	0.1 - 131.5 (22.2)
subtype3	77	16	0.0 - 119.7 (19.1)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.166 (Kruskal-Wallis (anova)), Q value = 0.45

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	445	67.0 (13.0)
subtype1	200	67.9 (14.2)
subtype2	168	66.5 (12.1)
subtype3	77	65.5 (11.8)

Figure S26. 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 'PATHOLOGIC_STAGE'

P value = 0.00034 (Fisher's exact test), Q value = 0.0033

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: '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	74	1	28	133	8	1	21	9	59	40	45	17	2
subtype1	39	1	13	75	6	0	11	1	23	14	12	2	1
subtype2	21	0	5	42	2	1	7	7	23	18	24	12	1
subtype3	14	0	10	16	0	0	3	1	13	8	9	3	0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.771 (Fisher's exact test), Q value = 0.96

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

nPatients	T1	T2	T3	T4
ALL	11	77	305	53
subtype1	5	38	131	27
subtype2	5	24	120	19
subtype3	1	15	54	7

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00192 (Fisher's exact test), Q value = 0.015

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	260	105	82
subtype1	137	35	30
subtype2	79	52	37
subtype3	44	18	15

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00039 (Fisher's exact test), Q value = 0.0036

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	324	64
subtype1	159	15
subtype2	110	37
subtype3	55	12

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.445 (Fisher's exact test), Q value = 0.8

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

nPatients	FEMALE	MALE
ALL	211	236
subtype1	102	100
subtype2	74	94
subtype3	35	42

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.736 (Fisher's exact test), Q value = 0.95

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	353	9
subtype1	155	3
subtype2	140	4
subtype3	58	2

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00026

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	382	60
subtype1	151	48
subtype2	161	6
subtype3	70	6

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0544 (Fisher's exact test), Q value = 0.23

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

nPatients	R0	R1	R2	RX
ALL	319	3	25	25
subtype1	146	2	5	16
subtype2	121	1	13	6
subtype3	52	0	7	3

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0021 (Kruskal-Wallis (anova)), Q value = 0.016

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

	nPatients	Mean (Std.Dev)
ALL	421	2.1 (4.5)
subtype1	189	1.7 (4.1)
subtype2	159	2.6 (5.2)
subtype3	73	1.9 (3.3)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.902 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	58	211
subtype1	1	6	25	98
subtype2	0	3	25	78
subtype3	0	2	8	35

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.381 (Fisher's exact test), Q value = 0.73

Table S40. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	265
subtype1	1	125
subtype2	3	96
subtype3	0	44

Figure S37. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S41. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	119	98	75

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.829 (logrank test), Q value = 0.96

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	287	68	0.1 - 148.0 (21.8)
subtype1	117	26	0.1 - 140.4 (23.6)
subtype2	96	24	0.1 - 135.7 (20.1)
subtype3	74	18	1.4 - 148.0 (19.9)

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 1.87e-05 (Kruskal-Wallis (anova)), Q value = 0.00042

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	290	64.9 (13.2)
subtype1	119	65.9 (12.1)
subtype2	97	68.3 (13.0)
subtype3	74	59.0 (13.5)

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.519 (Fisher's exact test), Q value = 0.84

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: '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	43	1	14	94	5	1	7	6	47	25	23	16	2
subtype1	17	0	6	33	1	1	3	4	21	8	11	10	1
subtype2	20	1	3	32	3	0	3	1	14	10	4	3	1
subtype3	6	0	5	29	1	0	1	1	12	7	8	3	0

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.252 (Fisher's exact test), Q value = 0.56

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	7	43	202	39
subtype1	4	19	82	14
subtype2	2	19	62	14
subtype3	1	5	58	11

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.623 (Fisher's exact test), Q value = 0.88

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	170	73	49
subtype1	66	34	19
subtype2	61	19	18
subtype3	43	20	12

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.158 (Fisher's exact test), Q value = 0.44

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	194	41
subtype1	76	22
subtype2	64	8
subtype3	54	11

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.14 (Fisher's exact test), Q value = 0.43

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	134	158
subtype1	49	70
subtype2	53	45
subtype3	32	43

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.738 (Fisher's exact test), Q value = 0.95

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	235	5
subtype1	92	3
subtype2	77	1
subtype3	66	1

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00078

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	251	38
subtype1	113	4
subtype2	79	18
subtype3	59	16

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.418 (Fisher's exact test), Q value = 0.78

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	190	2	5	24
subtype1	81	0	3	7
subtype2	60	2	1	11
subtype3	49	0	1	6

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.962 (Kruskal-Wallis (anova)), Q value = 1

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	267	2.0 (4.5)
subtype1	108	2.2 (5.6)
subtype2	90	1.8 (3.3)
subtype3	69	2.0 (3.7)

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.882 (Fisher's exact test), Q value = 0.99

Table S53. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	57	205
subtype1	0	4	23	81
subtype2	0	3	19	71
subtype3	1	4	15	53

Figure S49. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

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

Table S54. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	258
subtype1	2	103
subtype2	1	86
subtype3	1	69

Figure S50. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S55. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	87	144	126

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.583 (logrank test), Q value = 0.88

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	348	76	0.0 - 140.4 (21.6)
subtype1	83	19	0.0 - 91.8 (20.0)
subtype2	140	33	0.1 - 140.4 (24.3)
subtype3	125	24	0.0 - 135.7 (20.0)

Figure S51. 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 = 0.172 (Kruskal-Wallis (anova)), Q value = 0.45

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	356	67.0 (13.0)
subtype1	87	65.8 (13.7)
subtype2	143	68.5 (12.3)
subtype3	126	66.1 (13.2)

Figure S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.385 (Fisher's exact test), Q value = 0.73

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: '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	116	8	1	18	7	49	33	30	15	2
subtype1	12	1	4	28	1	0	4	2	12	10	8	4	1
subtype2	21	0	13	38	4	1	8	1	23	10	16	5	1
subtype3	21	0	3	50	3	0	6	4	14	13	6	6	0

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.743 (Fisher's exact test), Q value = 0.95

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	6	58	247	45
subtype1	0	16	61	9
subtype2	2	22	101	19
subtype3	4	20	85	17

Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.702 (Fisher's exact test), Q value = 0.94

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	209	85	63
subtype1	49	21	17
subtype2	80	38	26
subtype3	80	26	20

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.276 (Fisher's exact test), Q value = 0.6

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	268	47
subtype1	66	13
subtype2	102	22
subtype3	100	12

Figure S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.508 (Fisher's exact test), Q value = 0.84

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	172	185
subtype1	45	42
subtype2	64	80
subtype3	63	63

Figure S57. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

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

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	285	7
subtype1	71	2
subtype2	112	3
subtype3	102	2

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0164 (Fisher's exact test), Q value = 0.091

Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	312	42
subtype1	75	12
subtype2	134	9
subtype3	103	21

Figure S59. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 3e-05 (Fisher's exact test), Q value = 0.00058

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R2	RX
ALL	265	17	16
subtype1	75	5	1
subtype2	84	12	11
subtype3	106	0	4

Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.612 (Kruskal-Wallis (anova)), Q value = 0.88

Table S66. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	335	2.0 (4.4)
subtype1	85	2.1 (3.8)
subtype2	132	2.1 (3.9)
subtype3	118	1.9 (5.4)

Figure S61. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.326 (Fisher's exact test), Q value = 0.68

Table S67. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	47	181
subtype1	0	1	12	32
subtype2	0	7	19	67
subtype3	1	3	16	82

Figure S62. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.176 (Fisher's exact test), Q value = 0.45

Table S68. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	232
subtype1	0	45
subtype2	2	85
subtype3	0	102

Figure S63. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S69. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7	8	9
Number of samples	52	33	34	38	43	86	25	20	26

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.604 (logrank test), Q value = 0.88

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	348	76	0.0 - 140.4 (21.6)
subtype1	51	13	0.0 - 109.3 (18.1)
subtype2	32	7	1.0 - 139.2 (27.6)
subtype3	32	5	0.2 - 140.4 (29.5)
subtype4	37	12	0.1 - 131.5 (17.1)
subtype5	41	9	0.0 - 85.0 (27.0)
subtype6	84	16	0.0 - 135.7 (20.1)
subtype7	25	8	2.0 - 133.2 (17.9)
subtype8	20	2	8.3 - 88.2 (23.9)
subtype9	26	4	1.0 - 130.7 (22.5)

Figure S64. 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 = 0.0607 (Kruskal-Wallis (anova)), Q value = 0.25

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	356	67.0 (13.0)
subtype1	52	67.1 (12.1)
subtype2	32	66.3 (11.5)
subtype3	34	68.2 (11.5)
subtype4	38	69.8 (13.3)
subtype5	43	69.7 (13.0)
subtype6	86	66.0 (13.6)
subtype7	25	68.8 (12.4)
subtype8	20	57.6 (14.2)
subtype9	26	66.0 (13.5)

Figure S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0663 (Fisher's exact test), Q value = 0.26

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: '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	116	8	1	18	7	49	33	30	15	2
subtype1	11	0	0	16	0	0	0	1	9	6	7	2	0
subtype2	6	0	2	12	0	0	4	0	3	4	1	1	0
subtype3	5	0	3	6	1	0	2	2	5	4	3	1	0
subtype4	3	0	1	12	2	1	1	0	4	7	4	2	1
subtype5	4	0	8	11	2	0	4	1	6	1	6	0	0
subtype6	17	0	3	33	1	0	5	2	9	7	4	5	0
subtype7	2	0	1	13	1	0	1	0	3	1	2	0	0
subtype8	3	1	1	4	0	0	1	0	6	0	3	1	0
subtype9	3	0	1	9	1	0	0	1	4	3	0	3	1

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.822 (Fisher's exact test), Q value = 0.96

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	6	58	247	45
subtype1	0	11	32	9
subtype2	0	6	23	4
subtype3	1	7	22	4
subtype4	0	4	26	8
subtype5	0	7	33	3
subtype6	3	16	58	9
subtype7	0	2	20	3
subtype8	1	2	15	1
subtype9	1	3	18	4

Figure S67. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.177 (Fisher's exact test), Q value = 0.45

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	209	85	63
subtype1	27	13	12
subtype2	21	7	5
subtype3	16	11	7
subtype4	19	6	13
subtype5	28	11	4
subtype6	57	15	14
subtype7	17	5	3
subtype8	9	9	2
subtype9	15	8	3

Figure S68. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.764 (Fisher's exact test), Q value = 0.96

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	268	47
subtype1	37	9
subtype2	25	2
subtype3	26	4
subtype4	27	7
subtype5	36	6
subtype6	64	9
subtype7	20	2
subtype8	13	4
subtype9	20	4

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.623 (Fisher's exact test), Q value = 0.88

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	172	185
subtype1	24	28
subtype2	18	15
subtype3	18	16
subtype4	16	22
subtype5	20	23
subtype6	36	50
subtype7	15	10
subtype8	9	11
subtype9	16	10

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.803 (Fisher's exact test), Q value = 0.96

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	285	7
subtype1	39	2
subtype2	24	0
subtype3	27	1
subtype4	25	1
subtype5	36	2
subtype6	72	1
subtype7	21	0
subtype8	20	0
subtype9	21	0

Figure S71. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.362 (Fisher's exact test), Q value = 0.71

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	312	42
subtype1	44	8
subtype2	27	6
subtype3	32	2
subtype4	36	1
subtype5	39	4
subtype6	72	13
subtype7	22	3
subtype8	19	1
subtype9	21	4

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00867 (Fisher's exact test), Q value = 0.052

Table S79. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R2	RX
ALL	265	17	16
subtype1	40	1	3
subtype2	25	1	1
subtype3	25	3	1
subtype4	15	3	6
subtype5	36	5	1
subtype6	71	1	3
subtype7	20	2	0
subtype8	12	1	1
subtype9	21	0	0

Figure S73. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.291 (Kruskal-Wallis (anova)), Q value = 0.62

Table S80. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	335	2.0 (4.4)
subtype1	48	3.4 (8.1)
subtype2	30	2.0 (4.2)
subtype3	33	2.1 (3.3)
subtype4	33	3.6 (5.3)
subtype5	42	1.0 (1.7)
subtype6	80	1.8 (3.6)
subtype7	24	1.2 (2.6)
subtype8	19	1.4 (2.0)
subtype9	26	1.4 (1.8)

Figure S74. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.23 (Fisher's exact test), Q value = 0.54

Table S81. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	47	181
subtype1	1	3	8	30
subtype2	0	2	8	9
subtype3	0	0	4	7
subtype4	0	2	4	28
subtype5	0	1	1	9
subtype6	0	1	13	55
subtype7	0	1	1	13
subtype8	0	1	4	13
subtype9	0	0	4	17

Figure S75. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.155 (Fisher's exact test), Q value = 0.44

Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	232
subtype1	0	42
subtype2	0	17
subtype3	0	9
subtype4	1	32
subtype5	1	10
subtype6	0	69
subtype7	0	14
subtype8	0	18
subtype9	0	21

Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S83. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	120	108	91	135

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.408 (logrank test), Q value = 0.77

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	441	100	0.0 - 148.0 (22.0)
subtype1	117	32	0.1 - 148.0 (25.0)
subtype2	106	20	0.0 - 135.7 (21.7)
subtype3	89	23	0.1 - 100.0 (17.8)
subtype4	129	25	0.0 - 109.3 (22.0)

Figure S77. 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 = 0.00915 (Kruskal-Wallis (anova)), Q value = 0.053

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	452	67.0 (13.0)
subtype1	120	65.2 (12.9)
subtype2	107	67.2 (13.4)
subtype3	90	64.8 (13.1)
subtype4	135	69.9 (12.4)

Figure S78. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0794 (Fisher's exact test), Q value = 0.28

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: '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	74	1	29	138	9	1	21	9	59	40	45	17	2
subtype1	19	0	6	30	0	1	5	2	18	9	14	11	0
subtype2	22	1	9	36	3	0	4	2	14	8	5	2	2
subtype3	10	0	4	33	1	0	2	1	14	9	11	3	0
subtype4	23	0	10	39	5	0	10	4	13	14	15	1	0

Figure S79. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.493 (Fisher's exact test), Q value = 0.83

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	77	310	55
subtype1	3	23	80	14
subtype2	2	21	72	12
subtype3	1	9	65	16
subtype4	5	24	93	13

Figure S80. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.365 (Fisher's exact test), Q value = 0.71

Table S88. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	267	105	82
subtype1	66	33	21
subtype2	73	21	14
subtype3	49	22	20
subtype4	79	29	27

Figure S81. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0255 (Fisher's exact test), Q value = 0.13

Table S89. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	331	64
subtype1	75	25
subtype2	77	9
subtype3	63	14
subtype4	116	16

Figure S82. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0801 (Fisher's exact test), Q value = 0.28

Table S90. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	214	240
subtype1	47	73
subtype2	56	52
subtype3	39	52
subtype4	72	63

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.839 (Fisher's exact test), Q value = 0.96

Table S91. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	361	9
subtype1	90	2
subtype2	86	2
subtype3	78	1
subtype4	107	4

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00026

Table S92. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	388	61
subtype1	116	2
subtype2	81	25
subtype3	77	14
subtype4	114	20

Figure S85. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.00013 (Fisher's exact test), Q value = 0.0017

Table S93. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	326	3	24	25
subtype1	71	0	8	7
subtype2	71	2	2	11
subtype3	67	0	2	7
subtype4	117	1	12	0

Figure S86. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.309 (Kruskal-Wallis (anova)), Q value = 0.65

Table S94. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	428	2.1 (4.4)
subtype1	111	2.0 (4.1)
subtype2	100	1.4 (3.0)
subtype3	84	3.0 (6.8)
subtype4	133	2.0 (3.6)

Figure S87. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.452 (Fisher's exact test), Q value = 0.8

Table S95. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	58	213
subtype1	0	4	18	69
subtype2	0	4	24	57
subtype3	1	3	13	69
subtype4	0	0	3	18

Figure S88. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.46 (Fisher's exact test), Q value = 0.8

Table S96. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	267
subtype1	3	83
subtype2	0	81
subtype3	1	82
subtype4	0	21

Figure S89. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S97. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	79	107	96	133	39

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.825 (logrank test), Q value = 0.96

Table S98. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	441	100	0.0 - 148.0 (22.0)
subtype1	79	19	0.5 - 91.8 (22.0)
subtype2	105	24	0.1 - 148.0 (22.4)
subtype3	93	24	0.1 - 135.7 (19.3)
subtype4	126	25	0.0 - 109.3 (24.0)
subtype5	38	8	0.0 - 54.0 (23.5)

Figure S90. 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 = 0.000153 (Kruskal-Wallis (anova)), Q value = 0.0018

Table S99. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	452	67.0 (13.0)
subtype1	79	65.4 (13.1)
subtype2	107	65.4 (12.1)
subtype3	94	64.6 (14.7)
subtype4	133	68.6 (12.3)
subtype5	39	74.9 (10.4)

Figure S91. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00034 (Fisher's exact test), Q value = 0.0033

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: '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	74	1	29	138	9	1	21	9	59	40	45	17	2
subtype1	9	0	3	25	1	0	2	1	16	5	7	8	0
subtype2	15	0	8	34	0	1	4	1	18	7	8	7	0
subtype3	20	1	4	31	2	0	3	2	10	12	6	1	2
subtype4	24	0	9	35	2	0	8	5	10	16	23	1	0
subtype5	6	0	5	13	4	0	4	0	5	0	1	0	0

Figure S92. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.75 (Fisher's exact test), Q value = 0.95

Table S101. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	77	310	55
subtype1	1	10	55	13
subtype2	2	16	78	11
subtype3	3	18	60	14
subtype4	4	27	91	11
subtype5	1	6	26	6

Figure S93. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.091 (Fisher's exact test), Q value = 0.31

Table S102. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	267	105	82
subtype1	39	28	12
subtype2	66	24	17
subtype3	60	18	18
subtype4	73	29	31
subtype5	29	6	4

Figure S94. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0481 (Fisher's exact test), Q value = 0.21

Table S103. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	331	64
subtype1	52	15
subtype2	70	15
subtype3	65	9
subtype4	107	24
subtype5	37	1

Figure S95. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.182 (Fisher's exact test), Q value = 0.46

Table S104. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	214	240
subtype1	33	46
subtype2	46	61
subtype3	47	49
subtype4	63	70
subtype5	25	14

Figure S96. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.944 (Fisher's exact test), Q value = 1

Table S105. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	361	9
subtype1	73	2
subtype2	80	1
subtype3	74	2
subtype4	105	3
subtype5	29	1

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00026

Table S106. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	388	61
subtype1	73	6
subtype2	98	7
subtype3	72	23
subtype4	124	8
subtype5	21	17

Figure S98. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 1e-05 (Fisher's exact test), Q value = 0.00026

Table S107. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	326	3	24	25
subtype1	59	0	0	5
subtype2	62	0	2	9
subtype3	59	2	2	11
subtype4	110	1	20	0
subtype5	36	0	0	0

Figure S99. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.186 (Kruskal-Wallis (anova)), Q value = 0.46

Table S108. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	428	2.1 (4.4)
subtype1	74	2.5 (6.5)
subtype2	97	1.7 (3.1)
subtype3	87	1.9 (3.4)
subtype4	131	2.5 (4.8)
subtype5	39	1.0 (2.2)

Figure S100. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.838 (Fisher's exact test), Q value = 0.96

Table S109. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	58	213
subtype1	0	3	13	60
subtype2	0	3	24	65
subtype3	1	5	17	68
subtype4	0	0	4	15
subtype5	0	0	0	5

Figure S101. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.89 (Fisher's exact test), Q value = 0.99

Table S110. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	267
subtype1	1	72
subtype2	2	84
subtype3	1	87
subtype4	0	19
subtype5	0	5

Figure S102. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S111. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	160	66	180

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0816 (logrank test), Q value = 0.28

Table S112. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	393	89	0.0 - 148.0 (22.5)
subtype1	157	32	0.1 - 148.0 (22.1)
subtype2	66	17	0.5 - 100.0 (17.9)
subtype3	170	40	0.0 - 109.3 (24.2)

Figure S103. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000566 (Kruskal-Wallis (anova)), Q value = 0.0049

Table S113. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	405	67.3 (13.0)
subtype1	159	66.5 (12.4)
subtype2	66	62.6 (14.4)
subtype3	180	69.9 (12.5)

Figure S104. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.0339 (Fisher's exact test), Q value = 0.17

Table S114. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: '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	66	1	28	121	8	1	21	8	52	33	42	15	1
subtype1	24	1	7	56	2	1	7	2	26	12	11	6	1
subtype2	7	0	3	21	0	0	2	2	11	8	4	4	0
subtype3	35	0	18	44	6	0	12	4	15	13	27	5	0

Figure S105. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.344 (Fisher's exact test), Q value = 0.7

Table S115. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	68	280	46
subtype1	5	23	116	15
subtype2	0	9	46	11
subtype3	6	36	118	20

Figure S106. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.338 (Fisher's exact test), Q value = 0.69

Table S116. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	238	96	72
subtype1	97	40	23
subtype2	33	19	14
subtype3	108	37	35

Figure S107. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.565 (Fisher's exact test), Q value = 0.87

Table S117. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	305	58
subtype1	113	18
subtype2	48	8
subtype3	144	32

Figure S108. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.446 (Fisher's exact test), Q value = 0.8

Table S118. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	215
subtype1	74	86
subtype2	27	39
subtype3	90	90

Figure S109. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.157 (Fisher's exact test), Q value = 0.44

Table S119. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	319	8
subtype1	118	1
subtype2	57	3
subtype3	144	4

Figure S110. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0428 (Fisher's exact test), Q value = 0.2

Table S120. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	348	53
subtype1	143	14
subtype2	52	14
subtype3	153	25

Figure S111. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 1e-05 (Fisher's exact test), Q value = 0.00026

Table S121. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	282	3	25	22
subtype1	94	1	2	15
subtype2	43	0	0	5
subtype3	145	2	23	2

Figure S112. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.517 (Kruskal-Wallis (anova)), Q value = 0.84

Table S122. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	382	2.0 (4.5)
subtype1	146	1.6 (3.0)
subtype2	61	3.2 (8.0)
subtype3	175	2.0 (3.8)

Figure S113. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.676 (Fisher's exact test), Q value = 0.92

Table S123. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	23	213
subtype1	0	7	12	132
subtype2	1	3	7	51
subtype3	0	1	4	30

Figure S114. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

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

Table S124. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	239
subtype1	1	146
subtype2	0	59
subtype3	0	34

Figure S115. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S125. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	136	50	79	141

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.805 (logrank test), Q value = 0.96

Table S126. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	393	89	0.0 - 148.0 (22.5)
subtype1	133	32	0.8 - 140.4 (22.5)
subtype2	50	10	0.1 - 100.0 (16.5)
subtype3	76	18	0.0 - 148.0 (19.5)
subtype4	134	29	0.0 - 131.5 (27.0)

Figure S116. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.000101 (Kruskal-Wallis (anova)), Q value = 0.0014

Table S127. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	405	67.3 (13.0)
subtype1	135	66.3 (13.3)
subtype2	50	63.2 (13.1)
subtype3	79	64.9 (13.1)
subtype4	141	71.2 (11.9)

Figure S117. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.147 (Fisher's exact test), Q value = 0.44

Table S128. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: '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	66	1	28	121	8	1	21	8	52	33	42	15	1
subtype1	20	0	7	50	4	1	5	2	17	11	9	5	1
subtype2	6	0	2	19	0	0	1	2	9	2	3	4	0
subtype3	11	1	4	19	0	0	5	2	12	8	14	1	0
subtype4	29	0	15	33	4	0	10	2	14	12	16	5	0

Figure S118. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.872 (Fisher's exact test), Q value = 0.99

Table S129. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	68	280	46
subtype1	4	20	95	17
subtype2	1	6	39	4
subtype3	2	13	56	7
subtype4	4	29	90	18

Figure S119. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.113 (Fisher's exact test), Q value = 0.37

Table S130. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	238	96	72
subtype1	89	27	20
subtype2	27	17	6
subtype3	38	21	20
subtype4	84	31	26

Figure S120. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.538 (Fisher's exact test), Q value = 0.86

Table S131. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	305	58
subtype1	97	15
subtype2	36	7
subtype3	55	15
subtype4	117	21

Figure S121. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.475 (Fisher's exact test), Q value = 0.81

Table S132. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	215
subtype1	57	79
subtype2	23	27
subtype3	40	39
subtype4	71	70

Figure S122. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.726 (Fisher's exact test), Q value = 0.95

Table S133. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	319	8
subtype1	105	2
subtype2	40	2
subtype3	59	1
subtype4	115	3

Figure S123. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.207 (Fisher's exact test), Q value = 0.5

Table S134. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	348	53
subtype1	118	16
subtype2	40	10
subtype3	71	6
subtype4	119	21

Figure S124. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.00027 (Fisher's exact test), Q value = 0.003

Table S135. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	282	3	25	22
subtype1	76	1	3	11
subtype2	37	0	0	5
subtype3	51	1	9	5
subtype4	118	1	13	1

Figure S125. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.162 (Kruskal-Wallis (anova)), Q value = 0.44

Table S136. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	382	2.0 (4.5)
subtype1	128	1.6 (3.2)
subtype2	44	1.6 (2.9)
subtype3	72	2.9 (6.5)
subtype4	138	2.2 (4.7)

Figure S126. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.278 (Fisher's exact test), Q value = 0.6

Table S137. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	23	213
subtype1	0	4	11	106
subtype2	1	3	3	43
subtype3	0	4	6	32
subtype4	0	0	3	32

Figure S127. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.519 (Fisher's exact test), Q value = 0.84

Table S138. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	239
subtype1	0	116
subtype2	1	48
subtype3	0	40
subtype4	0	35

Figure S128. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S139. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	29	32	24

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

P value = 0.131 (logrank test), Q value = 0.41

Table S140. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	84	30	0.1 - 140.4 (22.1)
subtype1	29	13	5.1 - 140.4 (12.9)
subtype2	31	12	0.1 - 100.0 (21.8)
subtype3	24	5	0.2 - 131.5 (25.7)

Figure S129. 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 = 0.00416 (Kruskal-Wallis (anova)), Q value = 0.027

Table S141. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	84	65.1 (13.0)
subtype1	29	68.1 (13.2)
subtype2	32	59.5 (13.1)
subtype3	23	69.1 (10.3)

Figure S130. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.553 (Fisher's exact test), Q value = 0.86

Table S142. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	9	6	27	1	1	14	9	9	3
subtype1	2	1	13	1	0	2	2	2	1
subtype2	3	3	7	0	0	7	5	5	2
subtype3	4	2	7	0	1	5	2	2	0

Figure S131. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.58 (Fisher's exact test), Q value = 0.88

Table S143. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	2	9	63	11
subtype1	1	2	21	5
subtype2	1	2	25	4
subtype3	0	5	17	2

Figure S132. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.24 (Fisher's exact test), Q value = 0.55

Table S144. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	45	22	18
subtype1	18	4	7
subtype2	13	11	8
subtype3	14	7	3

Figure S133. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.228 (Fisher's exact test), Q value = 0.54

Table S145. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	56	12
subtype1	18	3
subtype2	18	7
subtype3	20	2

Figure S134. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.731 (Fisher's exact test), Q value = 0.95

Table S146. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	38	47
subtype1	14	15
subtype2	15	17
subtype3	9	15

Figure S135. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.025 (Fisher's exact test), Q value = 0.13

Table S147. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	74	9
subtype1	25	2
subtype2	25	7
subtype3	24	0

Figure S136. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.484 (Fisher's exact test), Q value = 0.82

Table S148. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	RX
ALL	42	5
subtype1	12	1
subtype2	19	4
subtype3	11	0

Figure S137. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0241 (Kruskal-Wallis (anova)), Q value = 0.13

Table S149. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	80	2.6 (6.4)
subtype1	29	1.7 (3.6)
subtype2	27	4.8 (10.0)
subtype3	24	1.3 (2.5)

Figure S138. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.663 (Fisher's exact test), Q value = 0.92

Table S150. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	6	73
subtype1	0	0	1	27
subtype2	1	2	3	25
subtype3	0	1	2	21

Figure S139. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S151. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	12	22	17	24	10

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

P value = 0.714 (logrank test), Q value = 0.95

Table S152. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	84	30	0.1 - 140.4 (22.1)
subtype1	11	5	5.2 - 62.8 (20.3)
subtype2	22	8	1.4 - 74.8 (22.8)
subtype3	17	5	0.2 - 131.5 (28.2)
subtype4	24	8	0.1 - 100.0 (15.4)
subtype5	10	4	1.3 - 140.4 (13.8)

Figure S140. 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 = 0.589 (Kruskal-Wallis (anova)), Q value = 0.88

Table S153. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	84	65.1 (13.0)
subtype1	12	65.1 (14.4)
subtype2	22	62.7 (14.6)
subtype3	16	67.9 (11.3)
subtype4	24	63.7 (13.0)
subtype5	10	69.2 (10.6)

Figure S141. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.817 (Fisher's exact test), Q value = 0.96

Table S154. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	9	6	27	1	1	14	9	9	3
subtype1	1	2	4	1	0	1	1	1	0
subtype2	1	0	4	0	1	6	2	5	2
subtype3	2	2	6	0	0	3	2	1	0
subtype4	4	1	8	0	0	3	3	2	1
subtype5	1	1	5	0	0	1	1	0	0

Figure S142. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.836 (Fisher's exact test), Q value = 0.96

Table S155. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	2	9	63	11
subtype1	0	1	8	3
subtype2	0	2	19	1
subtype3	1	2	13	1
subtype4	1	3	16	4
subtype5	0	1	7	2

Figure S143. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0763 (Fisher's exact test), Q value = 0.28

Table S156. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	45	22	18
subtype1	9	2	1
subtype2	5	11	6
subtype3	10	4	3
subtype4	14	4	6
subtype5	7	1	2

Figure S144. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.132 (Fisher's exact test), Q value = 0.41

Table S157. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	56	12
subtype1	6	1
subtype2	12	7
subtype3	14	1
subtype4	16	3
subtype5	8	0

Figure S145. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.00111 (Fisher's exact test), Q value = 0.0091

Table S158. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	38	47
subtype1	1	11
subtype2	17	5
subtype3	7	10
subtype4	8	16
subtype5	5	5

Figure S146. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.247 (Fisher's exact test), Q value = 0.56

Table S159. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	74	9
subtype1	11	1
subtype2	19	3
subtype3	17	0
subtype4	19	5
subtype5	8	0

Figure S147. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.942 (Fisher's exact test), Q value = 1

Table S160. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	RX
ALL	42	5
subtype1	7	0
subtype2	14	2
subtype3	4	0
subtype4	13	2
subtype5	4	1

Figure S148. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0469 (Kruskal-Wallis (anova)), Q value = 0.21

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

	nPatients	Mean (Std.Dev)
ALL	80	2.6 (6.4)
subtype1	12	1.2 (3.4)
subtype2	19	5.6 (11.5)
subtype3	17	1.6 (2.9)
subtype4	22	2.3 (4.1)
subtype5	10	1.2 (2.4)

Figure S149. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.993 (Fisher's exact test), Q value = 1

Table S162. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	3	6	73
subtype1	0	0	1	10
subtype2	0	1	2	19
subtype3	0	1	2	14
subtype4	1	1	1	21
subtype5	0	0	0	9

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

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/COAD-TP/20125559/COAD-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/COAD-TP/19775087/COAD-TP.merged_data.txt
Number of patients = 456
Number of clustering approaches = 12
Number of selected clinical features = 13
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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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)

Made with Nozzle