Correlation between aggregated molecular cancer subtypes and selected clinical features

Rectum Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18S4N0V

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 11 clinical features across 166 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'DISTANT.METASTASIS'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.


Clinical Features	Time to Death	AGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	DISTANT METASTASIS	LYMPH NODE METASTASIS	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
Statistical Tests	logrank test	ANOVA	Chi-square test	Chi-square test	Chi-square test	Chi-square test	Chi-square test	Chi-square test	ANOVA	ANOVA	Chi-square test
mRNA CNMF subtypes	0.241 (1.00)	0.419 (1.00)	0.953 (1.00)	0.612 (1.00)	0.638 (1.00)	0.322 (1.00)	0.447 (1.00)	0.4 (1.00)	0.426 (1.00)		0.502 (1.00)
mRNA cHierClus subtypes	0.0203 (1.00)	0.43 (1.00)	0.743 (1.00)	1 (1.00)	1 (1.00)	0.92 (1.00)	0.745 (1.00)	0.753 (1.00)	0.446 (1.00)		0.4 (1.00)
Copy Number Ratio CNMF subtypes	0.007 (0.826)	0.373 (1.00)	0.568 (1.00)	0.0316 (1.00)	0.238 (1.00)	0.0929 (1.00)	0.565 (1.00)	0.862 (1.00)	0.0855 (1.00)		0.199 (1.00)
METHLYATION CNMF	0.352 (1.00)	0.155 (1.00)	0.838 (1.00)	0.234 (1.00)	0.262 (1.00)	0.266 (1.00)	0.706 (1.00)	0.409 (1.00)	0.569 (1.00)		0.0828 (1.00)
RPPA CNMF subtypes	0.438 (1.00)	0.237 (1.00)	0.184 (1.00)	0.275 (1.00)	1 (1.00)	0.0644 (1.00)	0.508 (1.00)	0.403 (1.00)	0.528 (1.00)		0.302 (1.00)
RPPA cHierClus subtypes	0.00276 (0.328)	0.0107 (1.00)	0.237 (1.00)	0.424 (1.00)	0.453 (1.00)	0.0151 (1.00)	0.00859 (1.00)	0.132 (1.00)	0.424 (1.00)		0.0349 (1.00)
RNAseq CNMF subtypes	100 (1.00)	0.155 (1.00)	0.107 (1.00)	0.334 (1.00)	0.591 (1.00)	0.442 (1.00)	0.89 (1.00)	0.221 (1.00)	0.652 (1.00)		0.728 (1.00)
RNAseq cHierClus subtypes	100 (1.00)	0.387 (1.00)	0.878 (1.00)	0.184 (1.00)	0.172 (1.00)	0.172 (1.00)	0.677 (1.00)	0.267 (1.00)	0.272 (1.00)		0.366 (1.00)
MIRSEQ CNMF	0.309 (1.00)	0.892 (1.00)	0.461 (1.00)	0.313 (1.00)	0.283 (1.00)	0.0461 (1.00)	0.011 (1.00)	0.607 (1.00)	0.667 (1.00)		0.0192 (1.00)
MIRSEQ CHIERARCHICAL	0.767 (1.00)	0.582 (1.00)	0.973 (1.00)	0.037 (1.00)	0.298 (1.00)	0.000442 (0.0531)	0.0473 (1.00)	0.0617 (1.00)	0.834 (1.00)		0.0999 (1.00)
MIRseq Mature CNMF subtypes	0.228 (1.00)	0.703 (1.00)	0.233 (1.00)	0.822 (1.00)	0.762 (1.00)	0.212 (1.00)	0.0888 (1.00)	0.316 (1.00)	0.639 (1.00)		0.481 (1.00)
MIRseq Mature cHierClus subtypes	0.977 (1.00)	0.201 (1.00)	0.133 (1.00)	0.624 (1.00)	0.575 (1.00)	0.267 (1.00)	0.386 (1.00)	0.71 (1.00)	0.545 (1.00)		0.715 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	25	22	22

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.241 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	44	4	0.9 - 52.0 (4.0)
subtype1	21	1	0.9 - 49.9 (13.0)
subtype2	10	0	1.0 - 12.7 (1.0)
subtype3	13	3	1.0 - 52.0 (1.0)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.419 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	69	66.6 (10.7)
subtype1	25	64.6 (12.3)
subtype2	22	66.8 (10.1)
subtype3	22	68.7 (9.1)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.953 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	31	38
subtype1	12	13
subtype2	9	13
subtype3	10	12

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.612 (Fisher's exact test), Q value = 1

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

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	58	7
subtype1	19	3
subtype2	18	3
subtype3	21	1

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

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

P value = 0.638 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	1	68
subtype1	0	25
subtype2	1	21
subtype3	0	22

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

'mRNA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.322 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1
ALL	57	12
subtype1	21	4
subtype2	20	2
subtype3	16	6

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

'mRNA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.447 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N1A	N2
ALL	42	14	1	12
subtype1	16	5	0	4
subtype2	16	4	0	2
subtype3	10	5	1	6

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

'mRNA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.4 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2
ALL	57	1	10
subtype1	22	0	3
subtype2	19	0	2
subtype3	16	1	5

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

'mRNA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.426 (ANOVA), Q value = 1

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	69	2.2 (5.1)
subtype1	25	1.4 (2.3)
subtype2	22	2.0 (6.6)
subtype3	22	3.3 (5.8)

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.502 (Chi-square test), Q value = 1

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE III	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	18	3	20	2	10	4	11
subtype1	8	1	7	2	3	0	4
subtype2	7	1	8	0	2	2	2
subtype3	3	1	5	0	5	2	5

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S12. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	24	29	16

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0203 (logrank test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	44	4	0.9 - 52.0 (4.0)
subtype1	21	2	0.9 - 52.0 (13.6)
subtype2	15	2	1.0 - 38.0 (1.0)
subtype3	8	0	1.0 - 17.0 (1.5)

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.43 (ANOVA), Q value = 1

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	69	66.6 (10.7)
subtype1	24	64.4 (12.9)
subtype2	29	68.2 (8.7)
subtype3	16	67.0 (10.3)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.743 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	31	38
subtype1	12	12
subtype2	13	16
subtype3	6	10

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	58	7
subtype1	19	2
subtype2	25	3
subtype3	14	2

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

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

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	68
subtype1	0	24
subtype2	1	28
subtype3	0	16

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.92 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1
ALL	57	12
subtype1	20	4
subtype2	23	6
subtype3	14	2

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'mRNA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.745 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N2
ALL	42	14	1	12
subtype1	16	4	0	4
subtype2	15	8	1	5
subtype3	11	2	0	3

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'mRNA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.753 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	57	1	10
subtype1	21	0	3
subtype2	22	1	5
subtype3	14	0	2

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'mRNA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.446 (ANOVA), Q value = 1

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	69	2.2 (5.1)
subtype1	24	1.2 (2.0)
subtype2	29	2.5 (5.2)
subtype3	16	3.2 (7.8)

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.4 (Chi-square test), Q value = 1

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE III	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	18	3	20	2	10	4	11
subtype1	9	1	6	2	2	0	4
subtype2	5	1	8	0	7	2	5
subtype3	4	1	6	0	1	2	2

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

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

Table S23. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	7	32	46	55	22

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

P value = 0.007 (logrank test), Q value = 0.83

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	130	11	0.2 - 121.1 (6.2)
subtype1	7	0	0.4 - 121.1 (9.4)
subtype2	24	4	1.0 - 51.5 (5.0)
subtype3	34	3	0.5 - 119.5 (5.6)
subtype4	47	1	0.2 - 70.0 (5.9)
subtype5	18	3	0.8 - 38.9 (12.5)

Figure S21. 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 'AGE'

P value = 0.373 (ANOVA), Q value = 1

Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	162	64.7 (11.8)
subtype1	7	61.9 (9.9)
subtype2	32	64.4 (11.8)
subtype3	46	66.4 (12.5)
subtype4	55	62.7 (11.9)
subtype5	22	67.5 (10.3)

Figure S22. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.568 (Chi-square test), Q value = 1

Table S26. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	74	88
subtype1	1	6
subtype2	15	17
subtype3	22	24
subtype4	26	29
subtype5	10	12

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0316 (Chi-square test), Q value = 1

Table S27. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	143	13
subtype1	7	0
subtype2	32	0
subtype3	40	4
subtype4	44	9
subtype5	20	0

Figure S24. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	6	156
subtype1	1	6
subtype2	0	32
subtype3	1	45
subtype4	2	53
subtype5	2	20

Figure S25. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0929 (Chi-square test), Q value = 1

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	123	22	2	13
subtype1	7	0	0	0
subtype2	22	9	0	0
subtype3	34	7	0	5
subtype4	45	4	1	4
subtype5	15	2	1	4

Figure S26. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.565 (Chi-square test), Q value = 1

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	83	35	4	5	1	23	2	7	1
subtype1	5	0	0	0	0	2	0	0	0
subtype2	14	8	3	1	0	4	0	1	0
subtype3	23	8	0	2	1	7	1	3	1
subtype4	34	13	1	1	0	4	1	1	0
subtype5	7	6	0	1	0	6	0	2	0

Figure S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.862 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	121	2	12	3
subtype1	5	0	0	0
subtype2	24	1	4	0
subtype3	32	0	4	2
subtype4	43	1	3	1
subtype5	17	0	1	0

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0855 (ANOVA), Q value = 1

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	152	2.6 (5.4)
subtype1	6	3.0 (5.0)
subtype2	32	1.7 (2.3)
subtype3	42	3.0 (6.2)
subtype4	50	1.7 (4.5)
subtype5	22	5.2 (7.7)

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.199 (Chi-square test), Q value = 1

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	30	8	38	2	2	5	9	23	13	17	9
subtype1	1	0	4	0	0	0	0	0	2	0	0
subtype2	4	1	8	0	0	0	0	9	0	6	2
subtype3	6	2	13	1	1	1	3	3	6	6	1
subtype4	16	4	10	1	0	3	5	6	2	3	4
subtype5	3	1	3	0	1	1	1	5	3	2	2

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S34. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	25	39	31

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.352 (logrank test), Q value = 1

Table S35. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	8	0.2 - 121.1 (6.2)
subtype1	25	1	0.2 - 121.1 (6.0)
subtype2	36	3	0.2 - 60.0 (7.5)
subtype3	27	4	0.3 - 72.1 (3.5)

Figure S31. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.155 (ANOVA), Q value = 1

Table S36. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	95	63.3 (12.3)
subtype1	25	59.4 (9.1)
subtype2	39	64.1 (12.7)
subtype3	31	65.5 (13.4)

Figure S32. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.838 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	42	53
subtype1	11	14
subtype2	16	23
subtype3	15	16

Figure S33. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.234 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	87	6
subtype1	24	1
subtype2	36	1
subtype3	27	4

Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.262 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	90
subtype1	2	23
subtype2	3	36
subtype3	0	31

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.266 (Chi-square test), Q value = 1

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	67	10	2	14
subtype1	16	5	1	3
subtype2	27	1	1	8
subtype3	24	4	0	3

Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.706 (Chi-square test), Q value = 1

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	41	21	3	5	1	12	2	7	2
subtype1	11	4	1	1	1	4	0	2	1
subtype2	14	12	1	3	0	3	2	3	0
subtype3	16	5	1	1	0	5	0	2	1

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.409 (Chi-square test), Q value = 1

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	64	2	2	3
subtype1	18	0	0	0
subtype2	25	1	0	1
subtype3	21	1	2	2

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.569 (ANOVA), Q value = 1

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	84	3.1 (5.7)
subtype1	21	2.3 (3.1)
subtype2	36	2.9 (4.8)
subtype3	27	4.0 (8.0)

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0828 (Chi-square test), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	6	18	2	2	4	9	13	9	6	9
subtype1	4	2	4	1	0	0	1	3	3	3	3
subtype2	5	0	6	1	0	1	8	7	4	1	2
subtype3	2	4	8	0	2	3	0	3	2	2	4

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S45. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	33	43	54

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.438 (logrank test), Q value = 1

Table S46. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	105	11	0.2 - 121.1 (6.0)
subtype1	23	1	0.3 - 47.1 (1.0)
subtype2	37	3	0.2 - 72.1 (7.6)
subtype3	45	7	0.2 - 121.1 (7.1)

Figure S41. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.237 (ANOVA), Q value = 1

Table S47. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	130	65.6 (11.7)
subtype1	33	68.5 (11.2)
subtype2	43	65.1 (10.9)
subtype3	54	64.1 (12.5)

Figure S42. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.184 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	60	70
subtype1	14	19
subtype2	16	27
subtype3	30	24

Figure S43. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.275 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	117	10
subtype1	28	3
subtype2	42	1
subtype3	47	6

Figure S44. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

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

Table S50. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	125
subtype1	1	32
subtype2	2	41
subtype3	2	52

Figure S45. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0644 (Chi-square test), Q value = 1

Table S51. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	98	18	2	10
subtype1	24	6	2	0
subtype2	34	3	0	5
subtype3	40	9	0	5

Figure S46. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RPPA CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.508 (Chi-square test), Q value = 1

Table S52. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	NX
ALL	64	29	4	4	19	3	4	2
subtype1	14	6	0	1	8	2	1	0
subtype2	25	10	1	0	4	1	1	1
subtype3	25	13	3	3	7	0	2	1

Figure S47. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.403 (Chi-square test), Q value = 1

Table S53. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	97	2	12	2
subtype1	24	0	5	1
subtype2	36	1	1	1
subtype3	37	1	6	0

Figure S48. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.528 (ANOVA), Q value = 1

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	124	2.4 (4.9)
subtype1	32	3.3 (5.8)
subtype2	41	2.1 (5.2)
subtype3	51	2.2 (4.0)

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.302 (Chi-square test), Q value = 1

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	21	7	31	1	1	6	8	19	9	15	5
subtype1	5	2	7	0	0	0	0	4	5	5	3
subtype2	7	4	11	0	1	2	4	7	2	2	0
subtype3	9	1	13	1	0	4	4	8	2	8	2

Figure S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S56. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	6	19	33	26	46

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00276 (logrank test), Q value = 0.33

Table S57. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	105	11	0.2 - 121.1 (6.0)
subtype1	4	1	0.5 - 24.1 (5.2)
subtype2	17	0	0.2 - 72.1 (5.9)
subtype3	28	2	0.7 - 70.0 (9.0)
subtype4	13	1	0.9 - 15.8 (1.0)
subtype5	43	7	0.2 - 121.1 (6.0)

Figure S51. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0107 (ANOVA), Q value = 1

Table S58. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	130	65.6 (11.7)
subtype1	6	65.2 (11.9)
subtype2	19	61.5 (12.4)
subtype3	33	66.6 (11.2)
subtype4	26	72.0 (12.0)
subtype5	46	62.9 (10.4)

Figure S52. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.237 (Chi-square test), Q value = 1

Table S59. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	60	70
subtype1	4	2
subtype2	7	12
subtype3	11	22
subtype4	15	11
subtype5	23	23

Figure S53. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.424 (Chi-square test), Q value = 1

Table S60. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	117	10
subtype1	5	1
subtype2	16	3
subtype3	32	1
subtype4	23	1
subtype5	41	4

Figure S54. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.453 (Chi-square test), Q value = 1

Table S61. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	125
subtype1	0	6
subtype2	2	17
subtype3	1	32
subtype4	0	26
subtype5	2	44

Figure S55. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0151 (Chi-square test), Q value = 1

Table S62. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	98	18	2	10
subtype1	6	0	0	0
subtype2	15	1	2	1
subtype3	26	2	0	4
subtype4	18	8	0	0
subtype5	33	7	0	5

Figure S56. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RPPA cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.00859 (Chi-square test), Q value = 1

Table S63. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2A	N2B	NX
ALL	64	29	4	4	19	3	4	2
subtype1	2	0	1	0	2	0	1	0
subtype2	6	6	0	1	2	3	1	0
subtype3	23	6	0	0	2	0	1	1
subtype4	10	7	1	0	8	0	0	0
subtype5	23	10	2	3	5	0	1	1

Figure S57. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.132 (Chi-square test), Q value = 1

Table S64. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	97	2	12	2
subtype1	5	0	0	0
subtype2	17	1	0	1
subtype3	27	0	1	1
subtype4	18	0	7	0
subtype5	30	1	4	0

Figure S58. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.424 (ANOVA), Q value = 1

Table S65. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	124	2.4 (4.9)
subtype1	6	2.8 (2.9)
subtype2	18	3.3 (4.3)
subtype3	31	1.6 (5.1)
subtype4	26	3.7 (6.9)
subtype5	43	1.8 (3.5)

Figure S59. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0349 (Chi-square test), Q value = 1

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	21	7	31	1	1	6	8	19	9	15	5
subtype1	1	0	1	0	0	0	0	1	3	0	0
subtype2	1	1	3	0	0	1	3	5	1	0	3
subtype3	9	3	9	0	1	1	1	4	1	2	0
subtype4	4	1	5	0	0	2	0	3	3	7	0
subtype5	6	2	13	1	0	2	4	6	1	6	2

Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S67. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	10	12	7	7	4	7	4

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S68. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	49	2	0.2 - 121.1 (5.0)
subtype1	10	0	0.7 - 19.5 (6.1)
subtype2	10	0	1.0 - 47.7 (11.0)
subtype3	7	0	0.2 - 10.0 (5.0)
subtype4	7	2	0.5 - 60.0 (9.0)
subtype5	4	0	0.3 - 1.1 (0.6)
subtype6	7	0	0.7 - 119.5 (4.0)
subtype7	4	0	0.5 - 121.1 (0.7)

Figure S61. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.155 (ANOVA), Q value = 1

Table S69. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	51	63.3 (12.0)
subtype1	10	63.3 (12.6)
subtype2	12	64.7 (12.8)
subtype3	7	61.9 (11.5)
subtype4	7	72.4 (13.3)
subtype5	4	54.2 (5.1)
subtype6	7	56.6 (9.0)
subtype7	4	67.0 (7.9)

Figure S62. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.107 (Chi-square test), Q value = 1

Table S70. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	23	28
subtype1	4	6
subtype2	6	6
subtype3	2	5
subtype4	6	1
subtype5	1	3
subtype6	1	6
subtype7	3	1

Figure S63. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.334 (Chi-square test), Q value = 1

Table S71. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	47	2
subtype1	10	0
subtype2	9	1
subtype3	7	0
subtype4	7	0
subtype5	3	1
subtype6	7	0
subtype7	4	0

Figure S64. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.591 (Chi-square test), Q value = 1

Table S72. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	48
subtype1	1	9
subtype2	2	10
subtype3	0	7
subtype4	0	7
subtype5	0	4
subtype6	0	7
subtype7	0	4

Figure S65. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.442 (Chi-square test), Q value = 1

Table S73. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	37	4	1	9
subtype1	6	2	1	1
subtype2	7	0	0	5
subtype3	6	0	0	1
subtype4	4	1	0	2
subtype5	4	0	0	0
subtype6	6	1	0	0
subtype7	4	0	0	0

Figure S66. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

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

Table S74. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2B	NX
ALL	26	6	1	3	8	5	2
subtype1	3	2	0	1	2	1	1
subtype2	6	2	0	1	1	2	0
subtype3	5	0	1	0	0	0	1
subtype4	3	1	0	0	2	1	0
subtype5	3	0	0	0	0	1	0
subtype6	4	0	0	1	2	0	0
subtype7	2	1	0	0	1	0	0

Figure S67. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.221 (Chi-square test), Q value = 1

Table S75. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	35	1	3
subtype1	8	0	0
subtype2	7	1	0
subtype3	5	0	0
subtype4	3	0	2
subtype5	3	0	1
subtype6	6	0	0
subtype7	3	0	0

Figure S68. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.652 (ANOVA), Q value = 1

Table S76. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	44	3.2 (5.9)
subtype1	10	2.2 (2.6)
subtype2	11	6.0 (10.2)
subtype3	5	0.2 (0.4)
subtype4	4	3.5 (3.7)
subtype5	3	2.7 (4.6)
subtype6	7	3.1 (4.7)
subtype7	4	1.8 (2.1)

Figure S69. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.728 (Chi-square test), Q value = 1

Table S77. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	2	10	1	1	4	5	7	2	5
subtype1	1	0	2	0	0	1	0	1	1	2
subtype2	4	1	1	0	0	1	2	1	0	2
subtype3	4	0	1	0	0	0	1	0	0	0
subtype4	1	0	1	1	0	1	0	2	0	1
subtype5	0	1	1	0	0	1	0	1	0	0
subtype6	1	0	2	0	1	0	1	1	1	0
subtype7	0	0	2	0	0	0	1	1	0	0

Figure S70. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S78. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	19	12	20

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S79. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	49	2	0.2 - 121.1 (5.0)
subtype1	19	1	0.3 - 121.1 (0.8)
subtype2	10	0	1.0 - 47.7 (11.0)
subtype3	20	1	0.2 - 119.5 (5.0)

Figure S71. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.387 (ANOVA), Q value = 1

Table S80. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	51	63.3 (12.0)
subtype1	19	61.4 (12.4)
subtype2	12	67.4 (9.4)
subtype3	20	62.7 (12.8)

Figure S72. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.878 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	23	28
subtype1	8	11
subtype2	5	7
subtype3	10	10

Figure S73. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.184 (Fisher's exact test), Q value = 1

Table S82. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	47	2
subtype1	17	2
subtype2	10	0
subtype3	20	0

Figure S74. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.172 (Fisher's exact test), Q value = 1

Table S83. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	48
subtype1	1	18
subtype2	2	10
subtype3	0	20

Figure S75. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.172 (Chi-square test), Q value = 1

Table S84. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	37	4	1	9
subtype1	15	2	1	1
subtype2	7	0	0	5
subtype3	15	2	0	3

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

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.677 (Chi-square test), Q value = 1

Table S85. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N2	N2B	NX
ALL	26	6	1	3	8	5	2
subtype1	6	3	0	1	5	3	1
subtype2	7	2	0	1	1	1	0
subtype3	13	1	1	1	2	1	1

Figure S77. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.267 (Chi-square test), Q value = 1

Table S86. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	35	1	3
subtype1	15	0	1
subtype2	7	1	0
subtype3	13	0	2

Figure S78. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.272 (ANOVA), Q value = 1

Table S87. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	44	3.2 (5.9)
subtype1	18	4.7 (7.2)
subtype2	11	3.3 (6.5)
subtype3	15	1.3 (2.6)

Figure S79. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.366 (Chi-square test), Q value = 1

Table S88. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	2	10	1	1	4	5	7	2	5
subtype1	0	1	5	0	0	1	1	5	1	3
subtype2	5	1	1	0	0	1	2	1	0	1
subtype3	6	0	4	1	1	2	2	1	1	1

Figure S80. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S89. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	18	42	17	10	56

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.309 (logrank test), Q value = 1

Table S90. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	111	10	0.2 - 121.1 (7.0)
subtype1	16	3	0.3 - 121.1 (8.7)
subtype2	38	5	0.5 - 60.0 (7.3)
subtype3	15	0	0.5 - 119.5 (6.0)
subtype4	10	0	0.2 - 72.1 (1.5)
subtype5	32	2	0.9 - 70.0 (1.5)

Figure S81. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.892 (ANOVA), Q value = 1

Table S91. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	143	65.4 (11.5)
subtype1	18	65.2 (9.4)
subtype2	42	64.1 (12.6)
subtype3	17	65.3 (14.5)
subtype4	10	64.8 (7.4)
subtype5	56	66.6 (11.1)

Figure S82. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.461 (Chi-square test), Q value = 1

Table S92. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	66	77
subtype1	8	10
subtype2	18	24
subtype3	11	6
subtype4	3	7
subtype5	26	30

Figure S83. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.313 (Chi-square test), Q value = 1

Table S93. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	127	10
subtype1	15	2
subtype2	40	1
subtype3	17	0
subtype4	9	1
subtype5	46	6

Figure S84. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.283 (Chi-square test), Q value = 1

Table S94. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	6	137
subtype1	2	16
subtype2	3	39
subtype3	0	17
subtype4	0	10
subtype5	1	55

Figure S85. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.0461 (Chi-square test), Q value = 1

Table S95. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	107	18	2	14
subtype1	11	2	1	3
subtype2	28	4	0	9
subtype3	13	2	1	1
subtype4	7	2	0	1
subtype5	48	8	0	0

Figure S86. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.011 (Chi-square test), Q value = 1

Table S96. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	74	30	4	3	1	21	2	5	2
subtype1	5	3	1	0	0	4	2	1	1
subtype2	16	13	2	3	1	4	0	3	0
subtype3	11	3	1	0	0	1	0	1	0
subtype4	6	1	0	0	0	2	0	0	1
subtype5	36	10	0	0	0	10	0	0	0

Figure S87. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.607 (Chi-square test), Q value = 1

Table S97. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	102	2	11	3
subtype1	12	0	0	1
subtype2	23	1	1	1
subtype3	13	0	1	1
subtype4	7	0	2	0
subtype5	47	1	7	0

Figure S88. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.667 (ANOVA), Q value = 1

Table S98. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	132	2.6 (5.3)
subtype1	17	3.9 (5.7)
subtype2	37	2.8 (5.3)
subtype3	13	1.2 (2.2)
subtype4	9	3.2 (6.3)
subtype5	56	2.3 (5.6)

Figure S89. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0192 (Chi-square test), Q value = 1

Table S99. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	28	8	33	2	1	6	5	20	12	14	7
subtype1	0	1	4	0	0	0	0	2	4	0	3
subtype2	6	1	6	1	1	2	5	9	4	3	2
subtype3	6	0	4	1	0	0	0	3	0	1	2
subtype4	1	1	3	0	0	1	0	1	0	2	0
subtype5	15	5	16	0	0	3	0	5	4	8	0

Figure S90. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S100. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	20	64	59

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.767 (logrank test), Q value = 1

Table S101. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	111	10	0.2 - 121.1 (7.0)
subtype1	18	1	0.2 - 64.0 (3.4)
subtype2	40	3	0.9 - 72.1 (7.0)
subtype3	53	6	0.2 - 121.1 (7.4)

Figure S91. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.582 (ANOVA), Q value = 1

Table S102. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	143	65.4 (11.5)
subtype1	20	62.9 (13.5)
subtype2	64	65.8 (10.4)
subtype3	59	65.8 (12.0)

Figure S92. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.973 (Fisher's exact test), Q value = 1

Table S103. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	66	77
subtype1	9	11
subtype2	29	35
subtype3	28	31

Figure S93. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.037 (Fisher's exact test), Q value = 1

Table S104. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	127	10
subtype1	19	1
subtype2	52	8
subtype3	56	1

Figure S94. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.298 (Fisher's exact test), Q value = 1

Table S105. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	6	137
subtype1	1	19
subtype2	1	63
subtype3	4	55

Figure S95. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.000442 (Chi-square test), Q value = 0.053

Table S106. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	107	18	2	14
subtype1	13	5	0	2
subtype2	53	11	0	0
subtype3	41	2	2	12

Figure S96. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0473 (Chi-square test), Q value = 1

Table S107. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	74	30	4	3	1	21	2	5	2
subtype1	7	7	1	0	1	2	0	1	1
subtype2	38	13	1	0	0	12	0	0	0
subtype3	29	10	2	3	0	7	2	4	1

Figure S97. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0617 (Chi-square test), Q value = 1

Table S108. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	102	2	11	3
subtype1	13	0	2	0
subtype2	53	1	9	0
subtype3	36	1	0	3

Figure S98. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.834 (ANOVA), Q value = 1

Table S109. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	132	2.6 (5.3)
subtype1	15	1.9 (2.5)
subtype2	64	2.5 (5.6)
subtype3	53	2.8 (5.5)

Figure S99. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0999 (Chi-square test), Q value = 1

Table S110. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	28	8	33	2	1	6	5	20	12	14	7
subtype1	2	1	3	1	0	0	1	4	2	3	2
subtype2	17	5	15	0	0	4	0	8	4	10	0
subtype3	9	2	15	1	1	2	4	8	6	1	5

Figure S100. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S111. Get Full Table Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	15	19	27	14

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

P value = 0.228 (logrank test), Q value = 1

Table S112. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	68	7	0.2 - 121.1 (7.0)
subtype1	14	0	0.2 - 60.0 (6.0)
subtype2	18	3	0.4 - 121.1 (5.5)
subtype3	24	4	0.5 - 51.5 (7.9)
subtype4	12	0	0.5 - 119.5 (7.0)

Figure S101. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.703 (ANOVA), Q value = 1

Table S113. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	75	64.8 (12.3)
subtype1	15	61.5 (11.0)
subtype2	19	65.8 (10.1)
subtype3	27	65.9 (12.1)
subtype4	14	65.0 (16.6)

Figure S102. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.233 (Fisher's exact test), Q value = 1

Table S114. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	41
subtype1	4	11
subtype2	11	8
subtype3	11	16
subtype4	8	6

Figure S103. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.822 (Fisher's exact test), Q value = 1

Table S115. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	71	2
subtype1	14	1
subtype2	18	0
subtype3	25	1
subtype4	14	0

Figure S104. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.762 (Fisher's exact test), Q value = 1

Table S116. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	70
subtype1	1	14
subtype2	1	18
subtype3	3	24
subtype4	0	14

Figure S105. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.212 (Chi-square test), Q value = 1

Table S117. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	50	7	2	14
subtype1	8	4	0	3
subtype2	14	0	1	3
subtype3	17	2	0	7
subtype4	11	1	1	1

Figure S106. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0888 (Chi-square test), Q value = 1

Table S118. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	33	16	3	3	1	9	2	5	2
subtype1	5	6	0	0	1	2	0	0	1
subtype2	8	1	0	1	0	4	2	1	1
subtype3	11	9	1	1	0	2	0	3	0
subtype4	9	0	2	1	0	1	0	1	0

Figure S107. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.316 (Chi-square test), Q value = 1

Table S119. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	45	1	2	3
subtype1	7	0	1	2
subtype2	12	0	0	0
subtype3	15	1	0	0
subtype4	11	0	1	1

Figure S108. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.639 (ANOVA), Q value = 1

Table S120. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	64	2.8 (5.1)
subtype1	9	1.6 (2.0)
subtype2	19	3.4 (5.5)
subtype3	25	3.3 (6.3)
subtype4	11	1.5 (2.4)

Figure S109. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.481 (Chi-square test), Q value = 1

Table S121. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	2	16	2	1	2	5	11	8	4	7
subtype1	2	1	2	0	0	1	1	1	2	2	2
subtype2	0	1	7	0	0	0	0	2	4	0	2
subtype3	4	0	4	1	1	1	3	6	2	1	2
subtype4	5	0	3	1	0	0	1	2	0	1	1

Figure S110. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S122. Get Full Table Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	9	13	8	22	23

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

P value = 0.977 (logrank test), Q value = 1

Table S123. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	68	7	0.2 - 121.1 (7.0)
subtype1	8	0	0.5 - 20.0 (8.0)
subtype2	13	0	0.2 - 20.9 (2.2)
subtype3	7	0	1.0 - 27.9 (7.0)
subtype4	20	3	0.3 - 121.1 (5.5)
subtype5	20	4	0.5 - 119.5 (9.8)

Figure S111. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.201 (ANOVA), Q value = 1

Table S124. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	75	64.8 (12.3)
subtype1	9	69.6 (7.8)
subtype2	13	57.9 (10.2)
subtype3	8	67.8 (20.2)
subtype4	22	65.3 (11.2)
subtype5	23	65.4 (11.7)

Figure S112. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.133 (Chi-square test), Q value = 1

Table S125. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	41
subtype1	5	4
subtype2	3	10
subtype3	4	4
subtype4	14	8
subtype5	8	15

Figure S113. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.624 (Chi-square test), Q value = 1

Table S126. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	71	2
subtype1	9	0
subtype2	12	1
subtype3	8	0
subtype4	20	1
subtype5	22	0

Figure S114. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

P value = 0.575 (Chi-square test), Q value = 1

Table S127. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	70
subtype1	0	9
subtype2	1	12
subtype3	0	8
subtype4	1	21
subtype5	3	20

Figure S115. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.267 (Chi-square test), Q value = 1

Table S128. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	M1A	MX
ALL	50	7	2	14
subtype1	7	0	0	2
subtype2	7	4	0	2
subtype3	5	1	1	1
subtype4	16	0	1	4
subtype5	15	2	0	5

Figure S116. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.386 (Chi-square test), Q value = 1

Table S129. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1A	N1B	N1C	N2	N2A	N2B	NX
ALL	33	16	3	3	1	9	2	5	2
subtype1	6	2	1	0	0	0	0	0	0
subtype2	4	5	1	0	1	1	0	0	1
subtype3	5	0	0	1	0	1	0	1	0
subtype4	10	1	1	1	0	4	2	1	1
subtype5	8	8	0	1	0	3	0	3	0

Figure S117. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.71 (Chi-square test), Q value = 1

Table S130. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	45	1	2	3
subtype1	7	0	0	0
subtype2	8	0	1	0
subtype3	6	0	1	1
subtype4	13	0	0	1
subtype5	11	1	0	1

Figure S118. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.545 (ANOVA), Q value = 1

Table S131. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	64	2.8 (5.1)
subtype1	8	0.4 (0.7)
subtype2	8	1.8 (2.0)
subtype3	6	2.5 (3.0)
subtype4	21	3.1 (5.3)
subtype5	21	3.9 (6.8)

Figure S119. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.715 (Chi-square test), Q value = 1

Table S132. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	2	16	2	1	2	5	11	8	4	7
subtype1	3	0	2	0	0	1	1	1	0	0	1
subtype2	1	0	2	1	0	0	1	2	1	2	2
subtype3	3	0	2	0	0	0	1	0	0	1	1
subtype4	1	2	6	0	0	0	0	3	4	0	2
subtype5	3	0	4	1	1	1	2	5	3	1	1

Figure S120. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = READ-TP.mergedcluster.txt
Clinical data file = READ-TP.clin.merged.picked.txt
Number of patients = 166
Number of clustering approaches = 12
Number of selected clinical features = 11
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

ANOVA analysis

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

Fisher's exact test

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

Chi-square test

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

Q value calculation

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

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References

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

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

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

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

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

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

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

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