Rectum Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Rectum Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 10 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 'PATHOLOGICSPREAD(M)'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 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	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
Time to Death	logrank test	0.274 (1.00)	0.0075 (0.803)	0.00743 (0.802)	0.58 (1.00)	0.438 (1.00)	0.00276 (0.301)	100 (1.00)	100 (1.00)	0.314 (1.00)	0.774 (1.00)
AGE	ANOVA	0.499 (1.00)	0.394 (1.00)	0.373 (1.00)	0.291 (1.00)	0.237 (1.00)	0.0107 (1.00)	0.155 (1.00)	0.387 (1.00)	0.892 (1.00)	0.582 (1.00)
GENDER	Fisher's exact test	0.953 (1.00)	0.865 (1.00)	0.568 (1.00)	0.494 (1.00)	0.184 (1.00)	0.237 (1.00)	0.107 (1.00)	0.878 (1.00)	0.461 (1.00)	0.973 (1.00)
HISTOLOGICAL TYPE	Fisher's exact test	0.365 (1.00)	0.692 (1.00)	0.0316 (1.00)	0.274 (1.00)	0.275 (1.00)	0.424 (1.00)	0.334 (1.00)	0.184 (1.00)	0.313 (1.00)	0.037 (1.00)
PATHOLOGY T	Chi-square test	0.0987 (1.00)	0.245 (1.00)	0.564 (1.00)	0.524 (1.00)	0.0394 (1.00)	0.357 (1.00)	0.506 (1.00)	0.0694 (1.00)	0.251 (1.00)	0.726 (1.00)
PATHOLOGY N	Chi-square test	0.66 (1.00)	0.574 (1.00)	0.104 (1.00)	0.708 (1.00)	0.151 (1.00)	0.0478 (1.00)	0.874 (1.00)	0.197 (1.00)	0.0172 (1.00)	0.244 (1.00)
PATHOLOGICSPREAD(M)	Chi-square test	0.407 (1.00)	0.212 (1.00)	0.0929 (1.00)	0.261 (1.00)	0.0644 (1.00)	0.0151 (1.00)	0.442 (1.00)	0.172 (1.00)	0.0461 (1.00)	0.000442 (0.0487)
TUMOR STAGE	Chi-square test	0.639 (1.00)	0.504 (1.00)	0.477 (1.00)	0.0317 (1.00)	0.455 (1.00)	0.163 (1.00)	0.494 (1.00)	0.156 (1.00)	0.117 (1.00)	0.361 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.29 (1.00)	1 (1.00)	0.238 (1.00)	0.857 (1.00)	1 (1.00)	0.453 (1.00)	0.591 (1.00)	0.172 (1.00)	0.283 (1.00)	0.298 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.534 (1.00)	0.36 (1.00)	0.862 (1.00)	0.361 (1.00)	0.403 (1.00)	0.132 (1.00)	0.221 (1.00)	0.267 (1.00)	0.607 (1.00)	0.0617 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.561 (1.00)	0.546 (1.00)	0.0886 (1.00)	0.43 (1.00)	0.426 (1.00)	0.334 (1.00)	0.541 (1.00)	0.317 (1.00)	0.752 (1.00)	0.797 (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	20	24

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.274 (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	39	4	0.9 - 52.0 (6.0)
subtype1	20	1	0.9 - 49.9 (13.3)
subtype2	7	0	1.0 - 12.7 (1.0)
subtype3	12	3	1.0 - 52.0 (1.5)

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 = 0.499 (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	20	67.9 (9.9)
subtype3	24	67.7 (9.5)

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	11
subtype3	10	14

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 = 0.365 (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	16	3
subtype3	23	1

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0987 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	5	15	45	4
subtype1	3	6	16	0
subtype2	2	5	13	0
subtype3	0	4	16	4

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.66 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	42	15	12
subtype1	16	5	4
subtype2	14	4	2
subtype3	12	6	6

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

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

P value = 0.407 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	57	12
subtype1	21	4
subtype2	18	2
subtype3	18	6

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.639 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	18	23	16	10
subtype1	8	8	5	3
subtype2	7	7	4	2
subtype3	3	8	7	5

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

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

P value = 0.29 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	1	68
subtype1	0	25
subtype2	1	19
subtype3	0	24

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

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

P value = 0.534 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2
ALL	57	1	10
subtype1	22	0	3
subtype2	17	0	2
subtype3	18	1	5

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

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

P value = 0.561 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	68	2.2 (5.2)
subtype1	24	1.4 (2.4)
subtype2	20	2.2 (6.9)
subtype3	24	3.0 (5.6)

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S13. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	19	24	26

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0075 (logrank test), Q value = 0.8

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	39	4	0.9 - 52.0 (6.0)
subtype1	10	2	1.0 - 38.0 (1.0)
subtype2	20	2	0.9 - 52.0 (13.7)
subtype3	9	0	1.0 - 17.0 (1.0)

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.394 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	69	66.6 (10.7)
subtype1	19	68.4 (8.7)
subtype2	24	64.2 (12.8)
subtype3	26	67.5 (9.7)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.865 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	31	38
subtype1	8	11
subtype2	12	12
subtype3	11	15

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.692 (Fisher's exact test), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

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

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.245 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	5	15	45	4
subtype1	0	3	13	3
subtype2	3	6	14	1
subtype3	2	6	18	0

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.574 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	42	15	12
subtype1	9	6	4
subtype2	15	4	5
subtype3	18	5	3

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.212 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1
ALL	57	12
subtype1	14	5
subtype2	19	5
subtype3	24	2

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.504 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	18	23	16	10
subtype1	2	6	6	4
subtype2	8	7	4	4
subtype3	8	10	6	2

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

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

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

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	68
subtype1	0	19
subtype2	0	24
subtype3	1	25

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.36 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	57	1	10
subtype1	14	1	4
subtype2	20	0	4
subtype3	23	0	2

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.546 (ANOVA), Q value = 1

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	68	2.2 (5.2)
subtype1	19	3.3 (6.2)
subtype2	23	1.6 (2.6)
subtype3	26	2.0 (6.1)

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

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

Table S25. 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 = 0.00743 (logrank test), Q value = 0.8

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	125	11	0.2 - 121.1 (6.3)
subtype1	7	0	0.4 - 121.1 (9.4)
subtype2	24	4	1.0 - 51.5 (5.0)
subtype3	31	3	0.5 - 119.5 (6.3)
subtype4	46	1	0.2 - 70.0 (4.7)
subtype5	17	3	0.8 - 38.9 (13.0)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

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

Table S27. 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 S24. 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 S28. 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 S25. 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 S29. 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 S26. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.564 (Chi-square test), Q value = 1

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	9	29	109	14
subtype1	1	0	5	1
subtype2	1	4	23	3
subtype3	1	8	31	6
subtype4	5	14	34	2
subtype5	1	3	16	2

Figure S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.104 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	83	45	32
subtype1	5	0	2
subtype2	14	12	5
subtype3	23	11	11
subtype4	34	15	6
subtype5	7	7	8

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

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

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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 S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.477 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	30	50	50	25
subtype1	1	4	2	0
subtype2	4	9	9	7
subtype3	6	17	13	7
subtype4	16	15	16	7
subtype5	3	5	10	4

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

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

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

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: '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 S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: '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 S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0886 (ANOVA), Q value = 1

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	149	2.7 (5.4)
subtype1	6	3.0 (5.0)
subtype2	31	1.7 (2.3)
subtype3	40	3.1 (6.3)
subtype4	50	1.7 (4.5)
subtype5	22	5.2 (7.7)

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'METHLYATION CNMF'

Table S37. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	27	38	30

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.58 (logrank test), Q value = 1

Table S38. 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	27	2	0.2 - 121.1 (6.9)
subtype2	35	3	0.2 - 60.0 (7.4)
subtype3	26	3	0.3 - 72.1 (3.0)

Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.291 (ANOVA), Q value = 1

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	95	63.3 (12.3)
subtype1	27	60.3 (9.4)
subtype2	38	63.9 (12.8)
subtype3	30	65.3 (13.6)

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.494 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.274 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	87	6
subtype1	26	1
subtype2	35	1
subtype3	26	4

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.524 (Chi-square test), Q value = 1

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	4	13	66	11
subtype1	2	4	18	3
subtype2	1	7	23	6
subtype3	1	2	25	2

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.708 (Chi-square test), Q value = 1

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	41	30	21
subtype1	12	8	6
subtype2	14	15	8
subtype3	15	7	7

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.261 (Chi-square test), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	M1A	MX
ALL	67	10	2	14
subtype1	17	6	1	3
subtype2	27	1	1	7
subtype3	23	3	0	4

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.0317 (Chi-square test), Q value = 1

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	11	28	35	15
subtype1	5	7	7	7
subtype2	5	7	19	3
subtype3	1	14	9	5

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.857 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	90
subtype1	2	25
subtype2	2	36
subtype3	1	29

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.361 (Chi-square test), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	64	2	2	3
subtype1	19	0	0	0
subtype2	25	1	0	1
subtype3	20	1	2	2

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

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

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	82	3.2 (5.8)
subtype1	23	2.2 (3.0)
subtype2	34	3.0 (4.9)
subtype3	25	4.4 (8.3)

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S49. 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 S50. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	102	11	0.2 - 121.1 (6.2)
subtype1	23	1	0.3 - 47.1 (1.0)
subtype2	37	3	0.2 - 72.1 (7.6)
subtype3	42	7	0.2 - 121.1 (7.3)

Figure S45. 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 S51. 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 S46. 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 S52. 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 S47. 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 S53. 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 S48. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0394 (Chi-square test), Q value = 1

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	5	23	91	10
subtype1	2	3	21	6
subtype2	0	9	34	0
subtype3	3	11	36	4

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.151 (Chi-square test), Q value = 1

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	64	37	26
subtype1	14	7	11
subtype2	25	11	6
subtype3	25	19	9

Figure S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

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

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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 S51. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.455 (Chi-square test), Q value = 1

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	21	40	42	19
subtype1	5	9	9	7
subtype2	7	16	15	2
subtype3	9	15	18	10

Figure S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: '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 S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

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

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	122	2.5 (4.9)
subtype1	30	3.5 (6.0)
subtype2	41	2.1 (5.2)
subtype3	51	2.2 (4.0)

Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S61. 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 = 0.3

Table S62. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	102	11	0.2 - 121.1 (6.2)
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	11	1	0.9 - 15.8 (1.0)
subtype5	42	7	0.2 - 121.1 (6.6)

Figure S56. 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 S63. 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 S57. 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 S64. 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 S58. 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 S65. 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 S59. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.357 (Chi-square test), Q value = 1

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	5	23	91	10
subtype1	1	0	5	0
subtype2	0	2	14	3
subtype3	1	9	23	0
subtype4	1	3	20	2
subtype5	2	9	29	5

Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0478 (Chi-square test), Q value = 1

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	64	37	26
subtype1	2	1	3
subtype2	6	7	6
subtype3	23	6	3
subtype4	10	8	8
subtype5	23	15	6

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

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

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

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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 S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.163 (Chi-square test), Q value = 1

Table S69. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	21	40	42	19
subtype1	1	1	4	0
subtype2	1	4	10	3
subtype3	9	13	7	2
subtype4	4	6	8	6
subtype5	6	16	13	8

Figure S63. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: '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 S64. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: '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 S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.334 (ANOVA), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	122	2.5 (4.9)
subtype1	6	2.8 (2.9)
subtype2	18	3.3 (4.3)
subtype3	31	1.6 (5.1)
subtype4	24	4.0 (7.1)
subtype5	43	1.8 (3.5)

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S73. 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 S74. 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 S67. 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 S75. 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 S68. 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 S76. 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 S69. 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 S77. 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 S70. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.506 (Chi-square test), Q value = 1

Table S78. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	3	10	31	7
subtype1	0	1	8	1
subtype2	0	4	6	2
subtype3	2	2	3	0
subtype4	1	1	3	2
subtype5	0	1	3	0
subtype6	0	1	5	1
subtype7	0	0	3	1

Figure S71. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.874 (Chi-square test), Q value = 1

Table S79. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	26	10	13
subtype1	3	3	3
subtype2	6	3	3
subtype3	5	1	0
subtype4	3	1	3
subtype5	3	0	1
subtype6	4	1	2
subtype7	2	1	1

Figure S72. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

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

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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 S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.494 (Chi-square test), Q value = 1

Table S81. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	11	14	16	7
subtype1	1	2	2	3
subtype2	4	2	4	2
subtype3	4	1	1	0
subtype4	1	2	3	1
subtype5	0	2	2	0
subtype6	1	3	2	1
subtype7	0	2	2	0

Figure S74. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S82. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: '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 S75. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Table S83. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: '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 S76. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.541 (ANOVA), Q value = 1

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	42	3.3 (6.0)
subtype1	10	2.2 (2.6)
subtype2	10	6.6 (10.5)
subtype3	5	0.2 (0.4)
subtype4	3	4.7 (3.5)
subtype5	3	2.7 (4.6)
subtype6	7	3.1 (4.7)
subtype7	4	1.8 (2.1)

Figure S77. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S85. 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 S86. 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 S78. 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 S87. 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 S79. 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 S88. 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 S80. 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 S89. 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 S81. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0694 (Chi-square test), Q value = 1

Table S90. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	3	10	31	7
subtype1	0	1	14	4
subtype2	0	5	6	1
subtype3	3	4	11	2

Figure S82. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.197 (Chi-square test), Q value = 1

Table S91. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	26	10	13
subtype1	6	4	8
subtype2	7	3	2
subtype3	13	3	3

Figure S83. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

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

Table S92. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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 S84. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.156 (Chi-square test), Q value = 1

Table S93. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	11	14	16	7
subtype1	0	6	7	4
subtype2	5	2	4	1
subtype3	6	6	5	2

Figure S85. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S94. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S86. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Table S95. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

Figure S87. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.317 (ANOVA), Q value = 1

Table S96. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	42	3.3 (6.0)
subtype1	18	4.7 (7.2)
subtype2	10	3.6 (6.7)
subtype3	14	1.4 (2.7)

Figure S88. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S97. 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 = 0.314 (logrank test), Q value = 1

Table S98. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	106	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	27	2	0.9 - 70.0 (1.9)

Figure S89. 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 S99. 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 S90. 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 S100. 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 S91. 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 S101. 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 S92. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.251 (Chi-square test), Q value = 1

Table S102. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	9	26	97	10
subtype1	0	0	14	3
subtype2	2	9	29	2
subtype3	3	3	9	2
subtype4	0	2	8	0
subtype5	4	12	37	3

Figure S93. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.0172 (Chi-square test), Q value = 1

Table S103. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	74	38	28
subtype1	5	4	7
subtype2	16	19	7
subtype3	11	4	2
subtype4	6	1	2
subtype5	36	10	10

Figure S94. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

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

Table S104. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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 S95. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.117 (Chi-square test), Q value = 1

Table S105. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	28	44	43	20
subtype1	0	5	6	3
subtype2	6	9	20	5
subtype3	6	5	3	3
subtype4	1	4	2	2
subtype5	15	21	12	7

Figure S96. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S106. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: '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 S97. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Table S107. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: '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 S98. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.752 (ANOVA), Q value = 1

Table S108. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	129	2.6 (5.3)
subtype1	17	3.9 (5.7)
subtype2	37	2.8 (5.3)
subtype3	11	1.4 (2.4)
subtype4	9	3.2 (6.3)
subtype5	55	2.3 (5.6)

Figure S99. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S109. 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 = 0.774 (logrank test), Q value = 1

Table S110. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	106	10	0.2 - 121.1 (7.0)
subtype1	18	1	0.2 - 64.0 (3.4)
subtype2	35	3	0.9 - 72.1 (10.4)
subtype3	53	6	0.2 - 121.1 (7.4)

Figure S100. 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 S111. 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 S101. 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 S112. 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 S102. 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 S113. 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 S103. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.726 (Chi-square test), Q value = 1

Table S114. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	9	26	97	10
subtype1	1	2	16	1
subtype2	5	14	42	3
subtype3	3	10	39	6

Figure S104. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.244 (Chi-square test), Q value = 1

Table S115. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2
ALL	74	38	28
subtype1	7	9	3
subtype2	38	14	12
subtype3	29	15	13

Figure S105. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.000442 (Chi-square test), Q value = 0.049

Table S116. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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 S106. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.361 (Chi-square test), Q value = 1

Table S117. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	28	44	43	20
subtype1	2	5	7	5
subtype2	17	20	16	9
subtype3	9	19	20	6

Figure S107. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

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

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

Table S118. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S108. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Table S119. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: '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 S109. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.797 (ANOVA), Q value = 1

Table S120. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	129	2.6 (5.3)
subtype1	15	1.9 (2.5)
subtype2	63	2.6 (5.7)
subtype3	51	3.0 (5.6)

Figure S110. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

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 = 10
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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