Colon/Rectal Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 7 different clustering approaches and 9 clinical features across 275 patients, 17 significant findings detected with P value < 0.05.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'PRIMARY.SITE.OF.DISEASE', 'HISTOLOGICAL.TYPE', 'PATHOLOGY.N', and 'PATHOLOGICSPREAD(M)'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE', 'PRIMARY.SITE.OF.DISEASE', and 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'AGE', 'PRIMARY.SITE.OF.DISEASE', 'HISTOLOGICAL.TYPE', and 'NEOADJUVANT.THERAPY'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 5 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 9 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 17 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRseq CNMF subtypes	MIRseq cHierClus subtypes
Time to Death	logrank test	0.131	0.718	0.259	0.193	0.227	0.301	0.0217
AGE	ANOVA	0.712	0.194	0.00215	0.273	0.849	0.0018	0.112
PRIMARY SITE OF DISEASE	Chi-square test	0.0108	0.0338	0.00323	0.173	0.147	0.00435	0.422
GENDER	Chi-square test	0.0658	0.783	0.179	0.918	0.98	0.745	0.288
HISTOLOGICAL TYPE	Chi-square test	6.22e-07	4.54e-08	1.69e-08	0.0126	0.0103	0.0222	0.0209
PATHOLOGY T	Chi-square test	0.182	0.885	0.45	0.466	0.56	0.522	0.247
PATHOLOGY N	Chi-square test	0.0046	0.637	0.886	0.961	0.897	0.32	0.162
PATHOLOGICSPREAD(M)	Chi-square test	0.00144	0.492	0.0617	0.482	0.763	0.191	0.212
NEOADJUVANT THERAPY	Chi-square test	0.128	0.71	0.901	0.217	1	0.0361	0.363

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	62	48	73	40

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.131 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	15	0.9 - 52.0 (5.0)
subtype1	35	9	1.0 - 52.0 (13.0)
subtype2	26	2	1.0 - 30.0 (1.0)
subtype3	28	2	0.9 - 49.9 (1.0)
subtype4	24	2	0.9 - 52.0 (12.5)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.712 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	223	69.5 (11.4)
subtype1	62	68.1 (9.3)
subtype2	48	70.3 (11.8)
subtype3	73	69.6 (12.3)
subtype4	40	70.2 (12.2)

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

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

P value = 0.0108 (Fisher's exact test)

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

nPatients	COLON	RECTUM
ALL	153	68
subtype1	36	26
subtype2	41	7
subtype3	46	25
subtype4	30	10

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0658 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	107	116
subtype1	33	29
subtype2	29	19
subtype3	31	42
subtype4	14	26

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.22e-07 (Chi-square test)

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	127	24	58	7
subtype1	35	1	26	0
subtype2	27	13	4	2
subtype3	45	1	21	2
subtype4	20	9	7	3

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.182 (Chi-square test)

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

nPatients	T1	T2	T3	T4
ALL	9	46	149	17
subtype1	1	12	41	8
subtype2	1	9	32	4
subtype3	5	16	52	0
subtype4	2	9	24	5

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0046 (Chi-square test)

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

nPatients	N0	N1	N2
ALL	136	43	44
subtype1	27	18	17
subtype2	30	6	12
subtype3	47	12	14
subtype4	32	7	1

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

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

P value = 0.00144 (Chi-square test)

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

nPatients	M0	M1	M1A
ALL	185	34	1
subtype1	43	19	0
subtype2	41	6	0
subtype3	64	8	0
subtype4	37	1	1

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.128 (Fisher's exact test)

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

nPatients	NO	YES
ALL	215	8
subtype1	59	3
subtype2	44	4
subtype3	72	1
subtype4	40	0

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S11. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	46	64	113

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.718 (logrank test)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	15	0.9 - 52.0 (5.0)
subtype1	11	1	0.9 - 17.0 (1.0)
subtype2	35	3	1.0 - 41.0 (1.0)
subtype3	67	11	0.9 - 52.0 (12.0)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.194 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	223	69.5 (11.4)
subtype1	46	68.0 (12.7)
subtype2	64	71.6 (11.3)
subtype3	113	68.8 (10.8)

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

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

P value = 0.0338 (Fisher's exact test)

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	153	68
subtype1	33	12
subtype2	51	13
subtype3	69	43

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.783 (Fisher's exact test)

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	116
subtype1	22	24
subtype2	33	31
subtype3	52	61

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 4.54e-08 (Chi-square test)

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	127	24	58	7
subtype1	32	1	11	1
subtype2	29	20	9	3
subtype3	66	3	38	3

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.885 (Chi-square test)

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	9	46	149	17
subtype1	1	9	34	2
subtype2	2	13	41	6
subtype3	6	24	74	9

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.637 (Chi-square test)

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	136	43	44
subtype1	27	11	8
subtype2	43	11	10
subtype3	66	21	26

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.492 (Chi-square test)

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	185	34	1
subtype1	37	8	0
subtype2	57	6	0
subtype3	91	20	1

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.71 (Fisher's exact test)

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	215	8
subtype1	44	2
subtype2	61	3
subtype3	110	3

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S21. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	104	48	83

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.259 (logrank test)

Table S22. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	125	18	0.9 - 52.0 (10.3)
subtype1	54	9	0.9 - 52.0 (6.6)
subtype2	30	5	0.9 - 52.0 (1.0)
subtype3	41	4	0.9 - 49.9 (12.0)

Figure S19. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00215 (ANOVA)

Table S23. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	235	69.4 (11.6)
subtype1	104	66.8 (11.5)
subtype2	48	73.6 (12.1)
subtype3	83	70.3 (10.6)

Figure S20. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.00323 (Fisher's exact test)

Table S24. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	165	68
subtype1	67	36
subtype2	43	5
subtype3	55	27

Figure S21. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.179 (Fisher's exact test)

Table S25. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	114	121
subtype1	48	56
subtype2	29	19
subtype3	37	46

Figure S22. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.69e-08 (Chi-square test)

Table S26. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	137	26	58	7
subtype1	64	3	32	4
subtype2	23	18	5	0
subtype3	50	5	21	3

Figure S23. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.45 (Chi-square test)

Table S27. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	9	50	155	19
subtype1	4	27	64	9
subtype2	1	7	32	6
subtype3	4	16	59	4

Figure S24. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.886 (Chi-square test)

Table S28. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	141	47	47
subtype1	60	22	22
subtype2	32	8	8
subtype3	49	17	17

Figure S25. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0617 (Chi-square test)

Table S29. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	196	35	1
subtype1	82	21	0
subtype2	44	3	1
subtype3	70	11	0

Figure S26. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.901 (Fisher's exact test)

Table S30. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	227	8
subtype1	101	3
subtype2	46	2
subtype3	80	3

Figure S27. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	18	19	26	20

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.193 (logrank test)

Table S32. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	46	4	0.9 - 72.1 (8.2)
subtype1	7	1	1.0 - 64.0 (10.4)
subtype2	10	1	1.0 - 52.0 (12.1)
subtype3	15	0	0.9 - 61.9 (6.0)
subtype4	14	2	0.9 - 72.1 (1.5)

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.273 (ANOVA)

Table S33. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	83	67.6 (10.5)
subtype1	18	64.7 (9.3)
subtype2	19	65.9 (9.3)
subtype3	26	68.2 (12.5)
subtype4	20	70.9 (9.3)

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.173 (Fisher's exact test)

Table S34. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	11	71
subtype1	3	15
subtype2	3	15
subtype3	5	21
subtype4	0	20

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.918 (Fisher's exact test)

Table S35. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	39	44
subtype1	9	9
subtype2	10	9
subtype3	11	15
subtype4	9	11

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0126 (Chi-square test)

Table S36. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	11	60	8
subtype1	3	12	2
subtype2	3	15	0
subtype3	5	13	6
subtype4	0	20	0

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.466 (Chi-square test)

Table S37. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	5	19	54	5
subtype1	0	2	15	1
subtype2	2	4	12	1
subtype3	3	6	16	1
subtype4	0	7	11	2

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.961 (Chi-square test)

Table S38. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	52	17	14
subtype1	10	4	4
subtype2	12	4	3
subtype3	17	6	3
subtype4	13	3	4

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.482 (Chi-square test)

Table S39. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	68	13	1
subtype1	14	4	0
subtype2	17	2	0
subtype3	22	2	1
subtype4	15	5	0

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.217 (Fisher's exact test)

Table S40. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	82	1
subtype1	17	1
subtype2	19	0
subtype3	26	0
subtype4	20	0

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S41. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	27	16	17	23

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.227 (logrank test)

Table S42. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	46	4	0.9 - 72.1 (8.2)
subtype1	14	0	0.9 - 61.9 (8.5)
subtype2	10	1	0.9 - 49.9 (7.8)
subtype3	8	1	1.0 - 52.0 (11.3)
subtype4	14	2	1.0 - 72.1 (1.5)

Figure S37. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.849 (ANOVA)

Table S43. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	83	67.6 (10.5)
subtype1	27	67.7 (13.2)
subtype2	16	68.8 (9.5)
subtype3	17	65.6 (9.4)
subtype4	23	68.0 (8.7)

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.147 (Fisher's exact test)

Table S44. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	11	71
subtype1	7	20
subtype2	1	14
subtype3	2	15
subtype4	1	22

Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.98 (Fisher's exact test)

Table S45. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	39	44
subtype1	13	14
subtype2	8	8
subtype3	8	9
subtype4	10	13

Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0103 (Chi-square test)

Table S46. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	11	60	8
subtype1	7	12	6
subtype2	1	12	1
subtype3	2	15	0
subtype4	1	21	1

Figure S41. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.56 (Chi-square test)

Table S47. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	5	19	54	5
subtype1	2	6	18	1
subtype2	2	3	11	0
subtype3	1	6	9	1
subtype4	0	4	16	3

Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.897 (Chi-square test)

Table S48. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	52	17	14
subtype1	19	4	4
subtype2	10	3	3
subtype3	11	4	2
subtype4	12	6	5

Figure S43. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.763 (Chi-square test)

Table S49. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	68	13	1
subtype1	22	3	1
subtype2	13	3	0
subtype3	15	2	0
subtype4	18	5	0

Figure S44. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

Table S50. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	82	1
subtype1	26	1
subtype2	16	0
subtype3	17	0
subtype4	23	0

Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S51. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	46	69	63	76

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.301 (logrank test)

Table S52. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	140	20	0.9 - 72.1 (8.6)
subtype1	27	6	1.0 - 72.1 (12.7)
subtype2	37	6	0.9 - 61.9 (16.6)
subtype3	47	7	0.9 - 71.7 (11.0)
subtype4	29	1	0.9 - 20.9 (1.0)

Figure S46. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0018 (ANOVA)

Table S53. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	254	68.7 (12.1)
subtype1	46	70.5 (11.0)
subtype2	69	72.0 (11.5)
subtype3	63	64.3 (11.7)
subtype4	76	68.4 (12.6)

Figure S47. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.00435 (Fisher's exact test)

Table S54. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	185	67
subtype1	36	10
subtype2	59	10
subtype3	36	26
subtype4	54	21

Figure S48. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.745 (Fisher's exact test)

Table S55. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	127	127
subtype1	20	26
subtype2	34	35
subtype3	34	29
subtype4	39	37

Figure S49. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0222 (Chi-square test)

Table S56. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	157	26	56	8
subtype1	31	5	8	1
subtype2	46	12	7	3
subtype3	34	2	22	3
subtype4	46	7	19	1

Figure S50. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.522 (Chi-square test)

Table S57. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	11	52	168	21
subtype1	1	7	30	8
subtype2	3	13	47	5
subtype3	3	13	43	4
subtype4	4	19	48	4

Figure S51. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.32 (Chi-square test)

Table S58. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	151	55	47
subtype1	22	16	7
subtype2	43	13	13
subtype3	37	14	12
subtype4	49	12	15

Figure S52. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.191 (Chi-square test)

Table S59. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	207	41	2
subtype1	33	11	1
subtype2	62	6	0
subtype3	49	13	0
subtype4	63	11	1

Figure S53. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0361 (Fisher's exact test)

Table S60. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	240	14
subtype1	40	6
subtype2	64	5
subtype3	61	2
subtype4	75	1

Figure S54. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S61. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	38	63	26	49	78

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0217 (logrank test)

Table S62. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	140	20	0.9 - 72.1 (8.6)
subtype1	19	1	0.9 - 72.1 (12.0)
subtype2	37	4	0.9 - 61.9 (14.7)
subtype3	12	1	1.0 - 64.0 (1.5)
subtype4	20	3	0.9 - 38.1 (1.0)
subtype5	52	11	0.9 - 71.7 (12.4)

Figure S55. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.112 (ANOVA)

Table S63. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	254	68.7 (12.1)
subtype1	38	66.3 (13.5)
subtype2	63	71.1 (12.3)
subtype3	26	64.9 (10.9)
subtype4	49	70.3 (12.3)
subtype5	78	68.3 (11.2)

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.422 (Chi-square test)

Table S64. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	COLON	RECTUM
ALL	185	67
subtype1	26	11
subtype2	50	13
subtype3	18	8
subtype4	39	10
subtype5	52	25

Figure S57. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.288 (Chi-square test)

Table S65. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	127	127
subtype1	16	22
subtype2	34	29
subtype3	12	14
subtype4	20	29
subtype5	45	33

Figure S58. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0209 (Chi-square test)

Table S66. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	157	26	56	8
subtype1	25	1	11	0
subtype2	36	13	9	3
subtype3	16	2	6	2
subtype4	30	8	8	1
subtype5	50	2	22	2

Figure S59. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.247 (Chi-square test)

Table S67. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	11	52	168	21
subtype1	4	9	25	0
subtype2	2	16	38	6
subtype3	0	3	21	2
subtype4	2	7	36	3
subtype5	3	17	48	10

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.162 (Chi-square test)

Table S68. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	151	55	47
subtype1	23	10	5
subtype2	44	9	10
subtype3	13	4	9
subtype4	30	9	10
subtype5	41	23	13

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'

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

P value = 0.212 (Chi-square test)

Table S69. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A
ALL	207	41	2
subtype1	33	5	0
subtype2	55	6	0
subtype3	20	6	0
subtype4	43	5	1
subtype5	56	19	1

Figure S62. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.363 (Chi-square test)

Table S70. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	240	14
subtype1	37	1
subtype2	60	3
subtype3	23	3
subtype4	48	1
subtype5	72	6

Figure S63. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Methods & Data

Input

Cluster data file = COADREAD.mergedcluster.txt
Clinical data file = COADREAD.clin.merged.picked.txt
Number of patients = 275
Number of clustering approaches = 7
Number of selected clinical features = 9
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Fisher's exact test

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Download Results

This is an experimental feature. Location of data archives could not be determined.

References

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

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

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

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

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

[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)