Correlation between aggregated molecular cancer subtypes and selected clinical features

Pancreatic Adenocarcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GH9GMV

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 8 different clustering approaches and 12 clinical features across 73 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

3 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 sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
4 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 do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 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 8 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.104 (1.00)	0.188 (1.00)	0.929 (1.00)	0.256 (1.00)	0.589 (1.00)	0.633 (1.00)	0.504 (1.00)	0.196 (1.00)
AGE	ANOVA	0.922 (1.00)	0.455 (1.00)	0.827 (1.00)	0.706 (1.00)	0.632 (1.00)	0.672 (1.00)	0.688 (1.00)	0.807 (1.00)
NEOPLASM DISEASESTAGE	Chi-square test	0.0408 (1.00)	0.479 (1.00)	0.0575 (1.00)	0.257 (1.00)	0.14 (1.00)	0.217 (1.00)	0.085 (1.00)	0.184 (1.00)
PATHOLOGY T STAGE	Fisher's exact test	0.239 (1.00)	0.0983 (1.00)	0.0368 (1.00)	0.226 (1.00)	0.0465 (1.00)	0.102 (1.00)	0.145 (1.00)	0.243 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	0.159 (1.00)	1 (1.00)	0.0188 (1.00)	0.786 (1.00)	0.158 (1.00)	0.083 (1.00)	0.0871 (1.00)	0.0213 (1.00)
PATHOLOGY M STAGE	Chi-square test	0.0172 (1.00)	0.251 (1.00)	0.565 (1.00)	0.452 (1.00)	0.431 (1.00)	0.406 (1.00)	0.49 (1.00)	0.458 (1.00)
GENDER	Fisher's exact test	0.393 (1.00)	0.017 (1.00)	0.709 (1.00)	1 (1.00)	0.669 (1.00)	0.913 (1.00)	1 (1.00)	0.73 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.645 (1.00)	0.422 (1.00)	0.587 (1.00)	0.429 (1.00)	0.225 (1.00)	0.807 (1.00)	0.63 (1.00)	0.471 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.24 (1.00)	0.573 (1.00)	0.9 (1.00)	0.94 (1.00)	0.604 (1.00)	0.789 (1.00)	0.415 (1.00)	0.689 (1.00)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.849 (1.00)	0.585 (1.00)	0.597 (1.00)	0.343 (1.00)	0.594 (1.00)	0.497 (1.00)	0.54 (1.00)	0.877 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.0485 (1.00)	0.0554 (1.00)	0.0473 (1.00)	0.0724 (1.00)	0.0238 (1.00)	0.228 (1.00)	0.0344 (1.00)	0.0387 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.0107 (1.00)	0.154 (1.00)	0.2 (1.00)	0.844 (1.00)	0.477 (1.00)	0.309 (1.00)	0.874 (1.00)	0.0986 (1.00)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	17	40	15

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

P value = 0.104 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	69	29	0.0 - 49.4 (7.2)
subtype1	16	6	0.0 - 20.8 (6.6)
subtype2	38	15	0.1 - 49.4 (8.7)
subtype3	15	8	0.1 - 19.7 (4.8)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.922 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	72	65.9 (10.7)
subtype1	17	65.0 (10.3)
subtype2	40	66.1 (11.2)
subtype3	15	66.4 (10.4)

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

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

P value = 0.0408 (Chi-square test), Q value = 1

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	4	11	50	2	3
subtype1	0	0	4	10	0	3
subtype2	2	3	7	26	2	0
subtype3	0	1	0	14	0	0

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.239 (Fisher's exact test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3+T4
ALL	7	65
subtype1	0	17
subtype2	6	34
subtype3	1	14

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.159 (Fisher's exact test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	18	53
subtype1	5	12
subtype2	12	27
subtype3	1	14

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

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

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	39	3	30
subtype1	10	3	4
subtype2	20	0	20
subtype3	9	0	6

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.393 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	36	36
subtype1	9	8
subtype2	22	18
subtype3	5	10

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

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

P value = 0.645 (Chi-square test), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	64	5	2
subtype1	14	2	0
subtype2	36	2	2
subtype3	14	1	0

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.24 (ANOVA), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	21	24.5 (14.5)
subtype1	6	19.2 (13.4)
subtype2	10	23.0 (16.4)
subtype3	5	33.8 (8.2)

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.849 (ANOVA), Q value = 1

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	18	1970.9 (13.8)
subtype1	5	1973.6 (11.1)
subtype2	8	1968.9 (16.5)
subtype3	5	1971.4 (14.0)

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.0485 (Chi-square test), Q value = 1

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	43	24	2
subtype1	7	8	0
subtype2	30	8	1
subtype3	6	8	1

Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

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

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	71	2.9 (3.1)
subtype1	17	1.9 (1.5)
subtype2	39	2.5 (2.9)
subtype3	15	4.9 (4.2)

Figure S12. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #2: 'METHLYATION CNMF'

Table S14. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	23	31	19

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.188 (logrank test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	70	29	0.0 - 49.4 (7.4)
subtype1	22	7	0.0 - 36.2 (9.0)
subtype2	29	10	0.1 - 49.4 (7.7)
subtype3	19	12	0.3 - 21.9 (5.0)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.455 (ANOVA), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	73	65.7 (10.7)
subtype1	23	63.5 (10.3)
subtype2	31	66.4 (10.3)
subtype3	19	67.4 (12.0)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.479 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	4	11	51	2	3
subtype1	0	0	5	16	0	2
subtype2	2	2	4	22	1	0
subtype3	0	2	2	13	1	1

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0983 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3+T4
ALL	7	66
subtype1	0	23
subtype2	5	26
subtype3	2	17

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

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

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	18	54
subtype1	6	17
subtype2	8	23
subtype3	4	14

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

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

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	39	3	31
subtype1	11	2	10
subtype2	15	0	16
subtype3	13	1	5

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.017 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	36	37
subtype1	14	9
subtype2	18	13
subtype3	4	15

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.422 (Chi-square test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	65	5	2
subtype1	19	3	0
subtype2	28	2	1
subtype3	18	0	1

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.573 (ANOVA), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	22	24.5 (14.1)
subtype1	7	20.8 (14.1)
subtype2	8	23.9 (18.5)
subtype3	7	29.0 (7.8)

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.585 (ANOVA), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1971.7 (13.9)
subtype1	6	1975.8 (13.6)
subtype2	6	1967.2 (14.0)
subtype3	7	1972.1 (15.1)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.0554 (Chi-square test), Q value = 1

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	43	24	2
subtype1	10	10	1
subtype2	24	5	0
subtype3	9	9	1

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.154 (ANOVA), Q value = 1

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	72	2.8 (3.1)
subtype1	23	2.1 (1.8)
subtype2	31	2.7 (2.8)
subtype3	18	4.0 (4.5)

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S27. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	32	18	20

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.929 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	67	26	0.0 - 49.4 (7.2)
subtype1	31	11	0.1 - 28.0 (8.1)
subtype2	18	4	0.0 - 19.3 (1.4)
subtype3	18	11	0.1 - 49.4 (7.2)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.827 (ANOVA), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	70	65.6 (10.7)
subtype1	32	65.2 (10.7)
subtype2	18	65.1 (10.1)
subtype3	20	66.9 (11.5)

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0575 (Chi-square test), Q value = 1

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	4	10	50	2	2
subtype1	1	0	6	23	0	2
subtype2	0	0	1	16	1	0
subtype3	1	4	3	11	1	0

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0368 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3+T4
ALL	7	63
subtype1	1	31
subtype2	1	17
subtype3	5	15

Figure S28. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0188 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	17	52
subtype1	7	24
subtype2	1	17
subtype3	9	11

Figure S29. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

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

Table S33. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	37	2	31
subtype1	16	2	14
subtype2	11	0	7
subtype3	10	0	10

Figure S30. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.709 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	36	34
subtype1	18	14
subtype2	8	10
subtype3	10	10

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.587 (Chi-square test), Q value = 1

Table S35. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	62	5	2
subtype1	27	2	2
subtype2	17	1	0
subtype3	18	2	0

Figure S32. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.9 (ANOVA), Q value = 1

Table S36. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	22	24.5 (14.1)
subtype1	12	25.7 (10.5)
subtype2	7	23.7 (19.0)
subtype3	3	21.7 (19.6)

Figure S33. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.597 (ANOVA), Q value = 1

Table S37. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1971.7 (13.9)
subtype1	11	1974.5 (14.2)
subtype2	6	1970.5 (14.3)
subtype3	2	1960.5 (10.6)

Figure S34. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

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

Table S38. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	40	24	2
subtype1	15	14	1
subtype2	8	8	1
subtype3	17	2	0

Figure S35. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.2 (ANOVA), Q value = 1

Table S39. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	69	2.9 (3.2)
subtype1	31	2.6 (2.0)
subtype2	18	4.1 (4.0)
subtype3	20	2.4 (3.6)

Figure S36. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S40. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	1	30	39

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.256 (logrank test), Q value = 1

Table S41. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	66	25	0.0 - 49.4 (7.2)
subtype2	28	11	0.1 - 49.4 (7.4)
subtype3	38	14	0.0 - 28.0 (5.2)

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.706 (t-test), Q value = 1

Table S42. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	69	65.7 (10.7)
subtype2	30	65.1 (11.2)
subtype3	39	66.1 (10.5)

Figure S38. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.257 (Chi-square test), Q value = 1

Table S43. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	4	10	49	2	2
subtype2	1	3	3	21	2	0
subtype3	1	1	7	28	0	2

Figure S39. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.226 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3+T4
ALL	7	62
subtype2	5	25
subtype3	2	37

Figure S40. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.786 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	17	51
subtype2	8	22
subtype3	9	29

Figure S41. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.452 (Chi-square test), Q value = 1

Table S46. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	36	2	31
subtype2	16	0	14
subtype3	20	2	17

Figure S42. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

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

Table S47. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	34
subtype2	15	15
subtype3	20	19

Figure S43. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.429 (Chi-square test), Q value = 1

Table S48. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	61	5	2
subtype2	28	2	0
subtype3	33	3	2

Figure S44. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.94 (t-test), Q value = 1

Table S49. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	21	24.7 (14.4)
subtype2	8	24.4 (18.4)
subtype3	13	24.9 (12.2)

Figure S45. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.343 (t-test), Q value = 1

Table S50. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	18	1970.8 (13.7)
subtype2	6	1966.0 (14.9)
subtype3	12	1973.2 (13.0)

Figure S46. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.0724 (Chi-square test), Q value = 1

Table S51. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	39	24	2
subtype2	21	7	0
subtype3	18	17	2

Figure S47. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.844 (t-test), Q value = 1

Table S52. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	68	2.9 (3.2)
subtype2	30	3.0 (3.5)
subtype3	38	2.8 (2.9)

Figure S48. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S53. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	22	16	11	21

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.589 (logrank test), Q value = 1

Table S54. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	67	26	0.0 - 49.4 (7.2)
subtype1	21	8	0.0 - 17.2 (8.1)
subtype2	16	5	0.1 - 19.8 (10.8)
subtype3	9	5	0.1 - 21.9 (7.7)
subtype4	21	8	0.1 - 49.4 (4.8)

Figure S49. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.632 (ANOVA), Q value = 1

Table S55. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	70	65.6 (10.7)
subtype1	22	64.9 (10.5)
subtype2	16	63.3 (9.4)
subtype3	11	68.2 (12.5)
subtype4	21	66.9 (11.0)

Figure S50. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.14 (Chi-square test), Q value = 1

Table S56. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	4	10	50	2	2
subtype1	0	0	4	16	0	2
subtype2	0	0	1	15	0	0
subtype3	1	2	1	7	0	0
subtype4	1	2	4	12	2	0

Figure S51. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0465 (Fisher's exact test), Q value = 1

Table S57. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3+T4
ALL	7	63
subtype1	0	22
subtype2	1	15
subtype3	3	8
subtype4	3	18

Figure S52. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.158 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	17	52
subtype1	5	17
subtype2	1	15
subtype3	4	7
subtype4	7	13

Figure S53. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.431 (Chi-square test), Q value = 1

Table S59. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	37	2	31
subtype1	12	2	8
subtype2	10	0	6
subtype3	5	0	6
subtype4	10	0	11

Figure S54. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.669 (Fisher's exact test), Q value = 1

Table S60. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	36	34
subtype1	13	9
subtype2	8	8
subtype3	4	7
subtype4	11	10

Figure S55. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.225 (Chi-square test), Q value = 1

Table S61. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	62	5	2
subtype1	19	2	0
subtype2	16	0	0
subtype3	9	2	0
subtype4	18	1	2

Figure S56. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.604 (ANOVA), Q value = 1

Table S62. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	22	24.5 (14.1)
subtype1	10	25.1 (13.6)
subtype2	6	30.0 (14.8)
subtype3	1	1.0 (NA)
subtype4	5	21.6 (12.8)

Figure S57. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.594 (ANOVA), Q value = 1

Table S63. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1971.7 (13.9)
subtype1	9	1973.0 (12.6)
subtype2	5	1975.0 (15.7)
subtype4	5	1966.2 (16.0)

Figure S58. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.0238 (Chi-square test), Q value = 1

Table S64. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	40	24	2
subtype1	7	13	0
subtype2	10	5	0
subtype3	8	2	0
subtype4	15	4	2

Figure S59. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.477 (ANOVA), Q value = 1

Table S65. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	69	2.9 (3.2)
subtype1	22	2.5 (1.8)
subtype2	16	3.9 (4.2)
subtype3	11	3.1 (4.5)
subtype4	20	2.5 (2.4)

Figure S60. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S66. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	29	16	25

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.633 (logrank test), Q value = 1

Table S67. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	67	26	0.0 - 49.4 (7.2)
subtype1	28	12	0.2 - 28.0 (9.6)
subtype2	14	8	0.1 - 49.4 (7.4)
subtype3	25	6	0.0 - 19.8 (1.5)

Figure S61. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.672 (ANOVA), Q value = 1

Table S68. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	70	65.6 (10.7)
subtype1	29	66.2 (10.4)
subtype2	16	67.0 (12.7)
subtype3	25	64.2 (9.7)

Figure S62. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.217 (Chi-square test), Q value = 1

Table S69. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	4	10	50	2	2
subtype1	1	1	4	21	0	2
subtype2	1	3	2	9	1	0
subtype3	0	0	4	20	1	0

Figure S63. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.102 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3+T4
ALL	7	63
subtype1	2	27
subtype2	4	12
subtype3	1	24

Figure S64. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.083 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	17	52
subtype1	7	22
subtype2	7	9
subtype3	3	21

Figure S65. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.406 (Chi-square test), Q value = 1

Table S72. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	37	2	31
subtype1	15	2	12
subtype2	7	0	9
subtype3	15	0	10

Figure S66. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.913 (Fisher's exact test), Q value = 1

Table S73. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	36	34
subtype1	15	14
subtype2	9	7
subtype3	12	13

Figure S67. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.807 (Chi-square test), Q value = 1

Table S74. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	62	5	2
subtype1	25	2	1
subtype2	14	2	0
subtype3	23	1	1

Figure S68. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.789 (ANOVA), Q value = 1

Table S75. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	22	24.5 (14.1)
subtype1	10	26.9 (11.2)
subtype2	3	21.7 (19.6)
subtype3	9	22.9 (16.6)

Figure S69. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.497 (ANOVA), Q value = 1

Table S76. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1971.7 (13.9)
subtype1	9	1970.8 (13.0)
subtype2	2	1960.5 (10.6)
subtype3	8	1975.6 (15.4)

Figure S70. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.228 (Chi-square test), Q value = 1

Table S77. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	40	24	2
subtype1	14	12	1
subtype2	13	2	0
subtype3	13	10	1

Figure S71. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.309 (ANOVA), Q value = 1

Table S78. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	69	2.9 (3.2)
subtype1	29	2.6 (2.1)
subtype2	16	2.4 (3.6)
subtype3	24	3.7 (3.8)

Figure S72. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S79. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	28	16	26

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

P value = 0.504 (logrank test), Q value = 1

Table S80. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	67	26	0.0 - 49.4 (7.2)
subtype1	27	10	0.0 - 19.3 (7.9)
subtype2	16	4	0.1 - 19.8 (9.8)
subtype3	24	12	0.1 - 49.4 (6.7)

Figure S73. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.688 (ANOVA), Q value = 1

Table S81. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	70	65.6 (10.7)
subtype1	28	66.0 (10.4)
subtype2	16	63.6 (10.2)
subtype3	26	66.5 (11.4)

Figure S74. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.085 (Chi-square test), Q value = 1

Table S82. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	4	10	50	2	2
subtype1	1	0	5	20	0	2
subtype2	0	0	1	15	0	0
subtype3	1	4	4	15	2	0

Figure S75. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.145 (Fisher's exact test), Q value = 1

Table S83. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3+T4
ALL	7	63
subtype1	1	27
subtype2	1	15
subtype3	5	21

Figure S76. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0871 (Fisher's exact test), Q value = 1

Table S84. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	17	52
subtype1	7	21
subtype2	1	15
subtype3	9	16

Figure S77. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.49 (Chi-square test), Q value = 1

Table S85. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	37	2	31
subtype1	13	2	13
subtype2	9	0	7
subtype3	15	0	11

Figure S78. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

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

Table S86. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	36	34
subtype1	15	13
subtype2	8	8
subtype3	13	13

Figure S79. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.63 (Chi-square test), Q value = 1

Table S87. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	62	5	2
subtype1	23	3	1
subtype2	16	0	0
subtype3	23	2	1

Figure S80. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.415 (ANOVA), Q value = 1

Table S88. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	22	24.5 (14.1)
subtype1	11	25.8 (13.2)
subtype2	5	29.2 (16.4)
subtype3	6	18.2 (14.2)

Figure S81. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.54 (ANOVA), Q value = 1

Table S89. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1971.7 (13.9)
subtype1	10	1972.5 (12.0)
subtype2	4	1976.8 (17.6)
subtype3	5	1966.2 (16.0)

Figure S82. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.0344 (Chi-square test), Q value = 1

Table S90. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	40	24	2
subtype1	11	14	1
subtype2	8	6	1
subtype3	21	4	0

Figure S83. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.874 (ANOVA), Q value = 1

Table S91. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	69	2.9 (3.2)
subtype1	28	2.9 (3.3)
subtype2	16	3.2 (2.7)
subtype3	25	2.7 (3.4)

Figure S84. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S92. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	21	19	30

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

P value = 0.196 (logrank test), Q value = 1

Table S93. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	67	26	0.0 - 49.4 (7.2)
subtype1	21	6	0.0 - 19.8 (1.4)
subtype2	17	8	0.1 - 49.4 (7.2)
subtype3	29	12	0.2 - 19.7 (8.1)

Figure S85. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.807 (ANOVA), Q value = 1

Table S94. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	70	65.6 (10.7)
subtype1	21	64.4 (9.2)
subtype2	19	65.7 (12.8)
subtype3	30	66.4 (10.4)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.184 (Chi-square test), Q value = 1

Table S95. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	4	10	50	2	2
subtype1	0	0	1	19	1	0
subtype2	1	3	3	11	1	0
subtype3	1	1	6	20	0	2

Figure S87. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.243 (Fisher's exact test), Q value = 1

Table S96. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1+T2	T3+T4
ALL	7	63
subtype1	1	20
subtype2	4	15
subtype3	2	28

Figure S88. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0213 (Fisher's exact test), Q value = 1

Table S97. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	0	1
ALL	17	52
subtype1	1	20
subtype2	7	11
subtype3	9	21

Figure S89. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.458 (Chi-square test), Q value = 1

Table S98. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	37	2	31
subtype1	13	0	8
subtype2	9	0	10
subtype3	15	2	13

Figure S90. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.73 (Fisher's exact test), Q value = 1

Table S99. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	36	34
subtype1	11	10
subtype2	11	8
subtype3	14	16

Figure S91. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.471 (Chi-square test), Q value = 1

Table S100. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	62	5	2
subtype1	21	0	0
subtype2	16	2	1
subtype3	25	3	1

Figure S92. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.689 (ANOVA), Q value = 1

Table S101. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	22	24.5 (14.1)
subtype1	8	23.2 (17.7)
subtype2	5	21.0 (13.9)
subtype3	9	27.6 (11.6)

Figure S93. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.877 (ANOVA), Q value = 1

Table S102. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1971.7 (13.9)
subtype1	7	1973.1 (14.8)
subtype2	4	1968.5 (17.4)
subtype3	8	1972.1 (13.2)

Figure S94. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

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

P value = 0.0387 (Chi-square test), Q value = 1

Table S103. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	40	24	2
subtype1	12	7	1
subtype2	16	2	0
subtype3	12	15	1

Figure S95. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0986 (ANOVA), Q value = 1

Table S104. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	69	2.9 (3.2)
subtype1	21	4.1 (3.9)
subtype2	18	2.4 (3.4)
subtype3	30	2.3 (2.2)

Figure S96. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Cluster data file = PAAD-TP.mergedcluster.txt
Clinical data file = PAAD-TP.merged_data.txt
Number of patients = 73
Number of clustering approaches = 8
Number of selected clinical features = 12
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

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

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

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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.test' function in R

Q value calculation

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

Download Results

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

References

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

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

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

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

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

[6] 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)

[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

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

Made with Nozzle