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 12 clinical features across 452 patients, 37 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 'NEOPLASM_DISEASESTAGE', '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 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', 'COMPLETENESS_OF_RESECTION', 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 4 subtypes that correlate to 'HISTOLOGICAL_TYPE' and 'COMPLETENESS_OF_RESECTION'.
Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that correlate to 'NEOPLASM_DISEASESTAGE' and 'NUMBER_OF_LYMPH_NODES'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'COMPLETENESS_OF_RESECTION'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'COMPLETENESS_OF_RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', and 'COMPLETENESS_OF_RESECTION'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'COMPLETENESS_OF_RESECTION'.
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 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 37 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES	RACE
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test
mRNA CNMF subtypes	0.866 (0.959)	0.597 (0.782)	0.00307 (0.0211)	0.679 (0.822)	0.00854 (0.0492)	0.095 (0.277)	0.0359 (0.157)	2e-05 (0.00048)		0.162 (0.376)	0.00264 (0.019)	0.455 (0.727)
mRNA cHierClus subtypes	0.367 (0.653)	0.0658 (0.236)	0.632 (0.813)	0.647 (0.817)	0.787 (0.922)	0.598 (0.782)	0.0837 (0.268)	1e-05 (0.000288)		0.546 (0.779)	0.765 (0.903)	1 (1.00)
Copy Number Ratio CNMF subtypes	0.833 (0.94)	0.121 (0.318)	0.00179 (0.0136)	0.878 (0.96)	0.0012 (0.0096)	0.00015 (0.00196)	0.56 (0.782)	1e-05 (0.000288)	0.202 (0.434)	0.026 (0.129)	0.00101 (0.00909)	0.966 (1.00)
METHLYATION CNMF	0.957 (1.00)	0.000552 (0.00543)	0.385 (0.667)	0.26 (0.519)	0.423 (0.709)	0.0531 (0.201)	0.069 (0.236)	0.0098 (0.0543)	0.478 (0.748)	0.536 (0.775)	0.524 (0.775)	0.574 (0.782)
RPPA CNMF subtypes	0.894 (0.96)	0.88 (0.96)	0.396 (0.671)	0.647 (0.817)	0.528 (0.775)	0.0982 (0.277)	0.827 (0.94)	0.0353 (0.157)	0.498 (0.763)	0.00669 (0.0419)	0.16 (0.376)	0.176 (0.396)
RPPA cHierClus subtypes	0.154 (0.376)	0.446 (0.721)	0.0111 (0.0594)	0.0755 (0.248)	0.181 (0.402)	0.667 (0.821)	0.598 (0.782)	0.446 (0.721)	0.28 (0.537)	0.0934 (0.277)	0.0472 (0.184)	0.264 (0.52)
RNAseq CNMF subtypes	0.658 (0.821)	0.00769 (0.0461)	0.102 (0.277)	0.505 (0.765)	0.391 (0.671)	0.0336 (0.156)	0.104 (0.277)	3e-05 (0.00054)	0.232 (0.478)	3e-05 (0.00054)	0.341 (0.622)	0.332 (0.621)
RNAseq cHierClus subtypes	0.733 (0.872)	0.000119 (0.00171)	0.0004 (0.00443)	0.829 (0.94)	0.0986 (0.277)	0.0392 (0.166)	0.216 (0.457)	1e-05 (0.000288)	0.159 (0.376)	1e-05 (0.000288)	0.189 (0.413)	0.932 (0.994)
MIRSEQ CNMF	0.104 (0.277)	0.000566 (0.00543)	0.0451 (0.184)	0.384 (0.667)	0.348 (0.627)	0.566 (0.782)	0.444 (0.721)	0.0982 (0.277)	0.0689 (0.236)	1e-05 (0.000288)	0.517 (0.775)	0.675 (0.822)
MIRSEQ CHIERARCHICAL	0.853 (0.952)	0.000101 (0.00162)	0.135 (0.342)	0.893 (0.96)	0.099 (0.277)	0.538 (0.775)	0.477 (0.748)	0.31 (0.587)	0.0613 (0.226)	0.00024 (0.00288)	0.162 (0.376)	0.279 (0.537)
MIRseq Mature CNMF subtypes	0.165 (0.377)	0.00416 (0.0272)	0.556 (0.782)	0.582 (0.782)	0.239 (0.485)	0.227 (0.474)	0.73 (0.872)	0.033 (0.156)		0.487 (0.753)	0.0241 (0.124)	0.662 (0.821)
MIRseq Mature cHierClus subtypes	0.603 (0.782)	0.589 (0.782)	0.816 (0.94)	0.836 (0.94)	0.0757 (0.248)	0.128 (0.33)	0.00115 (0.0096)	0.339 (0.622)		0.942 (0.997)	0.0469 (0.184)	0.993 (1.00)

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 = 0.866 (logrank test), Q value = 0.96

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 = 0.78

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 'NEOPLASM_DISEASESTAGE'

P value = 0.00307 (Fisher's exact test), Q value = 0.021

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

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: 'NEOPLASM_DISEASESTAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.679 (Fisher's exact test), Q value = 0.82

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 = 0.00854 (Fisher's exact test), Q value = 0.049

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 = 0.095 (Fisher's exact test), Q value = 0.28

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 = 0.0359 (Fisher's exact test), Q value = 0.16

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 'HISTOLOGICAL_TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00048

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

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

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

'mRNA CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.162 (Fisher's exact test), Q value = 0.38

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

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 S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'mRNA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

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

Table S11. 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 S10. 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 = 0.73

Table S12. 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 S11. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S13. 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 = 0.367 (logrank test), Q value = 0.65

Table S14. 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 S12. 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 = 0.24

Table S15. 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 S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.632 (Fisher's exact test), Q value = 0.81

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

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 S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.647 (Fisher's exact test), Q value = 0.82

Table S17. 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 S15. 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 = 0.787 (Fisher's exact test), Q value = 0.92

Table S18. 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 S16. 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 = 0.598 (Fisher's exact test), Q value = 0.78

Table S19. 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 S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0837 (Fisher's exact test), Q value = 0.27

Table S20. 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 S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00029

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

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

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

'mRNA cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.546 (Fisher's exact test), Q value = 0.78

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

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

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'mRNA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.765 (Kruskal-Wallis (anova)), Q value = 0.9

Table S23. 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 S21. 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 S24. 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 S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

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

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

Cluster Labels	1	2	3
Number of samples	201	165	77

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

P value = 0.833 (logrank test), Q value = 0.94

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	430	93	0.0 - 140.4 (20.4)
subtype1	194	45	0.0 - 140.4 (20.2)
subtype2	159	32	0.1 - 131.5 (22.0)
subtype3	77	16	0.0 - 119.7 (18.1)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.121 (Kruskal-Wallis (anova)), Q value = 0.32

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

	nPatients	Mean (Std.Dev)
ALL	441	67.0 (13.0)
subtype1	199	68.1 (14.1)
subtype2	165	66.3 (12.1)
subtype3	77	65.5 (11.8)

Figure S24. 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 'NEOPLASM_DISEASESTAGE'

P value = 0.00179 (Fisher's exact test), Q value = 0.014

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	74	1	28	131	8	1	21	12	56	39	43	18	2
subtype1	39	1	13	75	6	0	10	3	21	14	11	3	1
subtype2	21	0	5	40	2	1	7	7	23	18	23	12	1
subtype3	14	0	10	16	0	0	4	2	12	7	9	3	0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.878 (Fisher's exact test), Q value = 0.96

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

nPatients	T1	T2	T3	T4
ALL	11	78	301	52
subtype1	5	38	131	26
subtype2	5	25	116	19
subtype3	1	15	54	7

Figure S26. 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 = 0.0012 (Fisher's exact test), Q value = 0.0096

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

nPatients	N0	N1	N2
ALL	257	104	81
subtype1	137	35	29
subtype2	76	51	37
subtype3	44	18	15

Figure S27. 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 = 0.00015 (Fisher's exact test), Q value = 0.002

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

nPatients	0	1
ALL	322	62
subtype1	159	14
subtype2	108	36
subtype3	55	12

Figure S28. 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 = 0.56 (Fisher's exact test), Q value = 0.78

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

nPatients	FEMALE	MALE
ALL	210	233
subtype1	101	100
subtype2	74	91
subtype3	35	42

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00029

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	381	60
subtype1	153	47
subtype2	158	6
subtype3	70	7

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.202 (Fisher's exact test), Q value = 0.43

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

nPatients	NO	YES
ALL	3	440
subtype1	0	201
subtype2	2	163
subtype3	1	76

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.026 (Fisher's exact test), Q value = 0.13

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	316	3	24	25
subtype1	146	2	4	16
subtype2	118	1	13	6
subtype3	52	0	7	3

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00101 (Kruskal-Wallis (anova)), Q value = 0.0091

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	418	2.1 (4.5)
subtype1	188	1.7 (4.1)
subtype2	157	2.6 (5.3)
subtype3	73	1.9 (3.3)

Figure S33. 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 = 0.966 (Fisher's exact test), Q value = 1

Table S37. 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	54	211
subtype1	1	6	24	98
subtype2	0	3	22	78
subtype3	0	2	8	35

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S38. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	115	99	74

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.957 (logrank test), Q value = 1

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	284	62	0.1 - 140.4 (19.8)
subtype1	113	27	0.1 - 140.4 (23.5)
subtype2	97	21	0.1 - 135.7 (19.3)
subtype3	74	14	0.1 - 102.4 (16.2)

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000552 (Kruskal-Wallis (anova)), Q value = 0.0054

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	286	64.9 (13.2)
subtype1	115	66.1 (12.5)
subtype2	99	67.2 (13.2)
subtype3	72	59.9 (13.0)

Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.385 (Fisher's exact test), Q value = 0.67

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	43	1	14	92	5	1	7	9	44	24	21	17	2
subtype1	16	0	6	29	1	1	3	4	20	9	11	10	1
subtype2	21	1	3	33	3	0	4	2	13	8	4	4	1
subtype3	6	0	5	30	1	0	0	3	11	7	6	3	0

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.26 (Fisher's exact test), Q value = 0.52

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

nPatients	T1	T2	T3	T4
ALL	7	44	198	38
subtype1	3	19	78	15
subtype2	3	20	63	12
subtype3	1	5	57	11

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.423 (Fisher's exact test), Q value = 0.71

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	167	72	48
subtype1	59	33	22
subtype2	64	20	15
subtype3	44	19	11

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0531 (Fisher's exact test), Q value = 0.2

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	192	39
subtype1	69	22
subtype2	68	8
subtype3	55	9

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.069 (Fisher's exact test), Q value = 0.24

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	133	155
subtype1	48	67
subtype2	55	44
subtype3	30	44

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0098 (Fisher's exact test), Q value = 0.054

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	250	38
subtype1	108	7
subtype2	82	17
subtype3	60	14

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.478 (Fisher's exact test), Q value = 0.75

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	3	285
subtype1	2	113
subtype2	0	99
subtype3	1	73

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'METHLYATION CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.536 (Fisher's exact test), Q value = 0.78

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	187	2	4	24
subtype1	74	0	3	8
subtype2	64	2	1	10
subtype3	49	0	0	6

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.524 (Kruskal-Wallis (anova)), Q value = 0.78

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	264	2.0 (4.5)
subtype1	105	2.4 (5.9)
subtype2	91	1.5 (2.6)
subtype3	68	2.0 (3.7)

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.574 (Fisher's exact test), Q value = 0.78

Table S50. 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	53	205
subtype1	0	4	20	80
subtype2	0	2	19	73
subtype3	1	5	14	52

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S51. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	80	128	27	96

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.894 (logrank test), Q value = 0.96

Table S52. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	324	69	0.0 - 140.4 (21.0)
subtype1	79	13	0.0 - 131.5 (23.0)
subtype2	122	29	0.1 - 140.4 (21.2)
subtype3	27	5	0.0 - 52.0 (24.0)
subtype4	96	22	0.0 - 135.7 (19.5)

Figure S47. 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 = 0.88 (Kruskal-Wallis (anova)), Q value = 0.96

Table S53. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	330	67.3 (13.0)
subtype1	80	66.8 (12.9)
subtype2	127	68.1 (12.3)
subtype3	27	66.9 (11.9)
subtype4	96	66.7 (14.3)

Figure S48. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.396 (Fisher's exact test), Q value = 0.67

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	49	1	20	108	8	1	18	8	43	29	28	14	1
subtype1	10	0	6	23	3	0	2	3	14	10	5	4	0
subtype2	18	0	10	34	3	1	8	2	18	8	18	5	1
subtype3	3	1	1	12	0	0	2	0	3	3	1	1	0
subtype4	18	0	3	39	2	0	6	3	8	8	4	4	0

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.647 (Fisher's exact test), Q value = 0.82

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	6	53	233	38
subtype1	0	12	59	9
subtype2	2	21	90	15
subtype3	0	3	22	1
subtype4	4	17	62	13

Figure S50. 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 = 0.528 (Fisher's exact test), Q value = 0.78

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	196	80	55
subtype1	42	23	15
subtype2	72	32	24
subtype3	17	6	4
subtype4	65	19	12

Figure S51. 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 = 0.0982 (Fisher's exact test), Q value = 0.28

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	259	43
subtype1	65	9
subtype2	92	24
subtype3	24	2
subtype4	78	8

Figure S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.827 (Fisher's exact test), Q value = 0.94

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	155	176
subtype1	38	42
subtype2	56	72
subtype3	13	14
subtype4	48	48

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0353 (Fisher's exact test), Q value = 0.16

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	293	38
subtype1	68	12
subtype2	121	7
subtype3	23	4
subtype4	81	15

Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.498 (Fisher's exact test), Q value = 0.76

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	3	328
subtype1	2	78
subtype2	1	127
subtype3	0	27
subtype4	0	96

Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.00669 (Fisher's exact test), Q value = 0.042

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R2	RX
ALL	241	16	16
subtype1	61	1	6
subtype2	78	13	6
subtype3	26	1	0
subtype4	76	1	4

Figure S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.16 (Kruskal-Wallis (anova)), Q value = 0.38

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	311	2.0 (4.5)
subtype1	74	2.9 (6.8)
subtype2	120	2.0 (3.8)
subtype3	27	1.7 (3.3)
subtype4	90	1.2 (2.6)

Figure S57. 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 = 0.176 (Fisher's exact test), Q value = 0.4

Table S63. 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	21	181
subtype1	1	5	7	43
subtype2	0	5	6	63
subtype3	0	0	2	6
subtype4	0	1	6	69

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S64. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	60	49	56	68	69	29

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.154 (logrank test), Q value = 0.38

Table S65. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	324	69	0.0 - 140.4 (21.0)
subtype1	59	8	0.1 - 131.5 (20.0)
subtype2	47	10	0.0 - 85.0 (24.3)
subtype3	55	18	0.1 - 119.9 (18.0)
subtype4	65	11	0.2 - 140.4 (23.9)
subtype5	69	12	0.0 - 135.7 (20.0)
subtype6	29	10	2.0 - 107.7 (18.1)

Figure S59. 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 = 0.446 (Kruskal-Wallis (anova)), Q value = 0.72

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	330	67.3 (13.0)
subtype1	60	66.2 (11.5)
subtype2	49	70.1 (12.9)
subtype3	55	67.5 (14.0)
subtype4	68	66.5 (12.4)
subtype5	69	66.1 (14.0)
subtype6	29	68.9 (13.1)

Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0111 (Fisher's exact test), Q value = 0.059

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	49	1	20	108	8	1	18	8	43	29	28	14	1
subtype1	7	0	1	22	1	0	0	2	10	7	4	6	0
subtype2	10	0	7	7	1	0	6	1	5	4	7	1	0
subtype3	6	0	2	16	3	1	1	0	11	7	6	2	1
subtype4	7	1	6	20	1	0	7	2	10	4	6	2	0
subtype5	17	0	2	28	1	0	2	3	4	6	3	3	0
subtype6	2	0	2	15	1	0	2	0	3	1	2	0	0

Figure S61. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0755 (Fisher's exact test), Q value = 0.25

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	6	53	233	38
subtype1	0	7	45	8
subtype2	0	13	33	3
subtype3	0	7	38	11
subtype4	2	8	51	6
subtype5	4	16	42	7
subtype6	0	2	24	3

Figure S62. 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 = 0.181 (Fisher's exact test), Q value = 0.4

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	196	80	55
subtype1	32	15	13
subtype2	28	13	8
subtype3	28	15	13
subtype4	37	22	9
subtype5	51	10	8
subtype6	20	5	4

Figure S63. 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 = 0.667 (Fisher's exact test), Q value = 0.82

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

nPatients	0	1
ALL	259	43
subtype1	46	10
subtype2	40	8
subtype3	42	9
subtype4	51	8
subtype5	57	6
subtype6	23	2

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.598 (Fisher's exact test), Q value = 0.78

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	155	176
subtype1	27	33
subtype2	25	24
subtype3	23	33
subtype4	34	34
subtype5	29	40
subtype6	17	12

Figure S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.446 (Fisher's exact test), Q value = 0.72

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	293	38
subtype1	51	9
subtype2	46	3
subtype3	52	4
subtype4	61	7
subtype5	58	11
subtype6	25	4

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.28 (Fisher's exact test), Q value = 0.54

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	3	328
subtype1	2	58
subtype2	0	49
subtype3	1	55
subtype4	0	68
subtype5	0	69
subtype6	0	29

Figure S67. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0934 (Fisher's exact test), Q value = 0.28

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R2	RX
ALL	241	16	16
subtype1	44	2	2
subtype2	40	5	1
subtype3	30	1	7
subtype4	49	5	2
subtype5	56	1	3
subtype6	22	2	1

Figure S68. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0472 (Kruskal-Wallis (anova)), Q value = 0.18

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	311	2.0 (4.5)
subtype1	56	3.2 (7.6)
subtype2	48	1.5 (2.3)
subtype3	51	2.8 (5.0)
subtype4	65	1.6 (3.0)
subtype5	64	1.2 (3.0)
subtype6	27	1.2 (2.5)

Figure S69. 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 = 0.264 (Fisher's exact test), Q value = 0.52

Table S76. 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	21	181
subtype1	0	3	5	43
subtype2	0	0	1	14
subtype3	1	4	4	41
subtype4	0	1	7	19
subtype5	0	1	3	50
subtype6	0	2	1	14

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S77. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	117	108	90	135

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.658 (logrank test), Q value = 0.82

Table S78. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	438	94	0.0 - 140.4 (21.0)
subtype1	114	29	0.1 - 140.4 (24.5)
subtype2	106	20	0.0 - 135.7 (19.8)
subtype3	89	20	0.1 - 100.0 (15.6)
subtype4	129	25	0.0 - 83.8 (22.0)

Figure S71. 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 = 0.00769 (Kruskal-Wallis (anova)), Q value = 0.046

Table S79. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	448	67.0 (13.0)
subtype1	117	64.9 (12.9)
subtype2	107	67.2 (13.4)
subtype3	89	65.1 (12.9)
subtype4	135	69.9 (12.4)

Figure S72. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.102 (Fisher's exact test), Q value = 0.28

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	74	1	29	136	9	1	21	12	56	39	43	18	2
subtype1	19	0	6	28	0	1	4	3	18	9	13	11	0
subtype2	22	1	9	36	3	0	5	3	12	7	5	3	2
subtype3	10	0	4	33	1	0	2	2	13	9	10	3	0
subtype4	23	0	10	39	5	0	10	4	13	14	15	1	0

Figure S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.505 (Fisher's exact test), Q value = 0.76

Table S81. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	78	306	54
subtype1	3	24	76	14
subtype2	2	21	72	12
subtype3	1	9	65	15
subtype4	5	24	93	13

Figure S74. 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 = 0.391 (Fisher's exact test), Q value = 0.67

Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	264	104	81
subtype1	63	32	21
subtype2	73	21	14
subtype3	49	22	19
subtype4	79	29	27

Figure S75. 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 = 0.0336 (Fisher's exact test), Q value = 0.16

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	329	62
subtype1	73	24
subtype2	77	9
subtype3	63	13
subtype4	116	16

Figure S76. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.104 (Fisher's exact test), Q value = 0.28

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	213	237
subtype1	47	70
subtype2	56	52
subtype3	38	52
subtype4	72	63

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00054

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	387	61
subtype1	114	3
subtype2	82	25
subtype3	76	14
subtype4	115	19

Figure S78. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.232 (Fisher's exact test), Q value = 0.48

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	3	447
subtype1	2	115
subtype2	0	108
subtype3	1	89
subtype4	0	135

Figure S79. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 3e-05 (Fisher's exact test), Q value = 0.00054

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	323	3	23	25
subtype1	68	0	8	7
subtype2	71	2	2	11
subtype3	67	0	1	7
subtype4	117	1	12	0

Figure S80. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.341 (Kruskal-Wallis (anova)), Q value = 0.62

Table S88. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	425	2.1 (4.4)
subtype1	109	2.0 (4.1)
subtype2	100	1.4 (3.0)
subtype3	83	2.9 (6.8)
subtype4	133	2.0 (3.6)

Figure S81. 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 = 0.332 (Fisher's exact test), Q value = 0.62

Table S89. 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	54	213
subtype1	0	4	15	69
subtype2	0	4	24	57
subtype3	1	3	12	69
subtype4	0	0	3	18

Figure S82. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S90. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	79	104	95	133	39

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.733 (logrank test), Q value = 0.87

Table S91. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	438	94	0.0 - 140.4 (21.0)
subtype1	79	17	0.3 - 91.8 (16.3)
subtype2	102	22	0.1 - 140.4 (22.0)
subtype3	93	22	0.1 - 135.7 (19.3)
subtype4	126	25	0.0 - 83.8 (24.0)
subtype5	38	8	0.0 - 54.0 (23.5)

Figure S83. 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 = 0.000119 (Kruskal-Wallis (anova)), Q value = 0.0017

Table S92. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	448	67.0 (13.0)
subtype1	79	65.4 (13.1)
subtype2	104	65.0 (12.0)
subtype3	93	64.9 (14.5)
subtype4	133	68.6 (12.3)
subtype5	39	74.9 (10.4)

Figure S84. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 4e-04 (Fisher's exact test), Q value = 0.0044

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	74	1	29	136	9	1	21	12	56	39	43	18	2
subtype1	9	0	3	25	1	0	2	2	15	5	7	8	0
subtype2	15	0	8	32	0	1	3	2	17	7	7	8	0
subtype3	20	1	4	31	2	0	4	3	9	11	5	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 S85. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.829 (Fisher's exact test), Q value = 0.94

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	78	306	54
subtype1	1	10	55	13
subtype2	2	17	74	11
subtype3	3	18	60	13
subtype4	4	27	91	11
subtype5	1	6	26	6

Figure S86. 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 = 0.0986 (Fisher's exact test), Q value = 0.28

Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	264	104	81
subtype1	39	28	12
subtype2	63	23	17
subtype3	60	18	17
subtype4	73	29	31
subtype5	29	6	4

Figure S87. 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 = 0.0392 (Fisher's exact test), Q value = 0.17

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	329	62
subtype1	52	15
subtype2	68	14
subtype3	65	8
subtype4	107	24
subtype5	37	1

Figure S88. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.216 (Fisher's exact test), Q value = 0.46

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

nPatients	FEMALE	MALE
ALL	213	237
subtype1	33	46
subtype2	46	58
subtype3	46	49
subtype4	63	70
subtype5	25	14

Figure S89. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00029

Table S98. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	387	61
subtype1	73	6
subtype2	96	8
subtype3	72	23
subtype4	125	7
subtype5	21	17

Figure S90. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.159 (Fisher's exact test), Q value = 0.38

Table S99. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	3	447
subtype1	2	77
subtype2	1	103
subtype3	0	95
subtype4	0	133
subtype5	0	39

Figure S91. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 1e-05 (Fisher's exact test), Q value = 0.00029

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	323	3	23	25
subtype1	59	0	0	5
subtype2	59	0	2	9
subtype3	59	2	1	11
subtype4	110	1	20	0
subtype5	36	0	0	0

Figure S92. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.189 (Kruskal-Wallis (anova)), Q value = 0.41

Table S101. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	425	2.1 (4.4)
subtype1	74	2.5 (6.5)
subtype2	95	1.7 (3.1)
subtype3	86	1.8 (3.4)
subtype4	131	2.5 (4.8)
subtype5	39	1.0 (2.2)

Figure S93. 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 = 0.932 (Fisher's exact test), Q value = 0.99

Table S102. 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	54	213
subtype1	0	3	13	60
subtype2	0	3	21	65
subtype3	1	5	16	68
subtype4	0	0	4	15
subtype5	0	0	0	5

Figure S94. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S103. 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 = 0.104 (logrank test), Q value = 0.28

Table S104. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	394	86	0.0 - 140.4 (21.9)
subtype1	158	31	0.1 - 140.4 (20.9)
subtype2	66	15	0.1 - 100.0 (14.6)
subtype3	170	40	0.0 - 83.8 (24.2)

Figure S95. 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 = 0.0054

Table S105. 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 S96. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0451 (Fisher's exact test), Q value = 0.18

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	66	1	28	121	8	1	21	11	49	32	42	16	1
subtype1	24	1	7	56	2	1	7	4	24	11	11	7	1
subtype2	7	0	3	21	0	0	2	3	10	8	4	4	0
subtype3	35	0	18	44	6	0	12	4	15	13	27	5	0

Figure S97. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.384 (Fisher's exact test), Q value = 0.67

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	69	279	46
subtype1	5	24	115	15
subtype2	0	9	46	11
subtype3	6	36	118	20

Figure S98. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.348 (Fisher's exact test), Q value = 0.63

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	238	95	72
subtype1	97	39	23
subtype2	33	19	14
subtype3	108	37	35

Figure S99. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.566 (Fisher's exact test), Q value = 0.78

Table S109. 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 S100. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.444 (Fisher's exact test), Q value = 0.72

Table S110. 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 S101. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0982 (Fisher's exact test), Q value = 0.28

Table S111. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	351	53
subtype1	145	15
subtype2	53	13
subtype3	153	25

Figure S102. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0689 (Fisher's exact test), Q value = 0.24

Table S112. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	3	403
subtype1	0	160
subtype2	2	64
subtype3	1	179

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

'MIRSEQ CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 1e-05 (Fisher's exact test), Q value = 0.00029

Table S113. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

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 S104. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.517 (Kruskal-Wallis (anova)), Q value = 0.78

Table S114. 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 S105. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.675 (Fisher's exact test), Q value = 0.82

Table S115. 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 S106. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S116. 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 = 0.853 (logrank test), Q value = 0.95

Table S117. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	394	86	0.0 - 140.4 (21.9)
subtype1	134	31	0.2 - 140.4 (20.5)
subtype2	50	9	0.1 - 100.0 (15.5)
subtype3	76	17	0.0 - 139.2 (18.6)
subtype4	134	29	0.0 - 131.5 (27.0)

Figure S107. 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 = 0.0016

Table S118. 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 S108. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.135 (Fisher's exact test), Q value = 0.34

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	66	1	28	121	8	1	21	11	49	32	42	16	1
subtype1	20	0	7	50	4	1	6	3	15	10	9	6	1
subtype2	6	0	2	19	0	0	1	3	8	2	3	4	0
subtype3	11	1	4	19	0	0	4	3	12	8	14	1	0
subtype4	29	0	15	33	4	0	10	2	14	12	16	5	0

Figure S109. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.893 (Fisher's exact test), Q value = 0.96

Table S120. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	11	69	279	46
subtype1	4	21	94	17
subtype2	1	6	39	4
subtype3	2	13	56	7
subtype4	4	29	90	18

Figure S110. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.099 (Fisher's exact test), Q value = 0.28

Table S121. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	238	95	72
subtype1	89	26	20
subtype2	27	17	6
subtype3	38	21	20
subtype4	84	31	26

Figure S111. 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 = 0.78

Table S122. 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 S112. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.477 (Fisher's exact test), Q value = 0.75

Table S123. 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 S113. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.31 (Fisher's exact test), Q value = 0.59

Table S124. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	351	53
subtype1	120	16
subtype2	40	10
subtype3	71	7
subtype4	120	20

Figure S114. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0613 (Fisher's exact test), Q value = 0.23

Table S125. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	3	403
subtype1	0	136
subtype2	2	48
subtype3	0	79
subtype4	1	140

Figure S115. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.00024 (Fisher's exact test), Q value = 0.0029

Table S126. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

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 S116. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.162 (Kruskal-Wallis (anova)), Q value = 0.38

Table S127. 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 S117. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.279 (Fisher's exact test), Q value = 0.54

Table S128. 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 S118. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S129. 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 = 0.165 (logrank test), Q value = 0.38

Table S130. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	85	28	0.1 - 140.4 (20.3)
subtype1	29	13	5.1 - 140.4 (12.9)
subtype2	32	10	0.1 - 100.0 (16.1)
subtype3	24	5	0.2 - 131.5 (25.7)

Figure S119. 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 S131. 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 S120. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.556 (Fisher's exact test), Q value = 0.78

Table S132. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

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 S121. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.582 (Fisher's exact test), Q value = 0.78

Table S133. 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 S122. 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 = 0.239 (Fisher's exact test), Q value = 0.48

Table S134. 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 S123. 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 = 0.227 (Fisher's exact test), Q value = 0.47

Table S135. 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 S124. 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 = 0.73 (Fisher's exact test), Q value = 0.87

Table S136. 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 S125. 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 = 0.033 (Fisher's exact test), Q value = 0.16

Table S137. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	75	10
subtype1	26	3
subtype2	25	7
subtype3	24	0

Figure S126. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.487 (Fisher's exact test), Q value = 0.75

Table S138. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

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

Figure S127. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0241 (Kruskal-Wallis (anova)), Q value = 0.12

Table S139. 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 S128. 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 = 0.662 (Fisher's exact test), Q value = 0.82

Table S140. 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 S129. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S141. 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 = 0.603 (logrank test), Q value = 0.78

Table S142. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	85	28	0.1 - 140.4 (20.3)
subtype1	12	5	0.2 - 62.8 (12.8)
subtype2	22	7	1.4 - 67.3 (18.8)
subtype3	17	5	0.2 - 131.5 (28.2)
subtype4	24	7	0.1 - 100.0 (15.4)
subtype5	10	4	1.3 - 140.4 (13.8)

Figure S130. 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 = 0.78

Table S143. 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 S131. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.816 (Fisher's exact test), Q value = 0.94

Table S144. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

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 S132. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.836 (Fisher's exact test), Q value = 0.94

Table S145. 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 S133. 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 = 0.0757 (Fisher's exact test), Q value = 0.25

Table S146. 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 S134. 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 = 0.128 (Fisher's exact test), Q value = 0.33

Table S147. 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 S135. 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 = 0.00115 (Fisher's exact test), Q value = 0.0096

Table S148. 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 S136. 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 = 0.339 (Fisher's exact test), Q value = 0.62

Table S149. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	75	10
subtype1	11	1
subtype2	19	3
subtype3	17	0
subtype4	19	5
subtype5	9	1

Figure S137. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

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

Table S150. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

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

Figure S138. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0469 (Kruskal-Wallis (anova)), Q value = 0.18

Table S151. 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 S139. 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 S152. 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 S140. 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/15098885/COAD-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/COAD-TP/15076851/COAD-TP.merged_data.txt
Number of patients = 452
Number of clustering approaches = 12
Number of selected clinical features = 12
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

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)

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