Correlation between aggregated molecular cancer subtypes and selected clinical features

Pancreatic Adenocarcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1TQ610F

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 15 clinical features across 185 patients, 24 significant findings detected with P value < 0.05 and Q value < 0.25.

4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'RADIATION_THERAPY'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'PATHOLOGY_T_STAGE', and 'GENDER'.
CNMF clustering analysis on RPPA data identified 5 subtypes that correlate to 'GENDER'.
Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that correlate to 'GENDER'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', and 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', and 'HISTOLOGICAL_TYPE'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', and 'HISTOLOGICAL_TYPE'.
7 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL_TYPE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', and 'HISTOLOGICAL_TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 15 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 24 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.182 (0.507)	0.00993 (0.0827)	0.552 (0.801)	0.262 (0.606)	0.00138 (0.0206)	0.0147 (0.116)	0.12 (0.429)	0.112 (0.421)	0.143 (0.477)	0.00203 (0.0277)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.567 (0.801)	0.0536 (0.309)	0.278 (0.619)	0.615 (0.824)	0.309 (0.621)	0.174 (0.507)	0.998 (1.00)	0.577 (0.801)	0.694 (0.867)	0.293 (0.621)
PATHOLOGIC STAGE	Fisher's exact test	0.86 (0.942)	0.352 (0.65)	0.311 (0.621)	0.228 (0.584)	0.0245 (0.167)	6e-05 (0.003)	0.0663 (0.355)	0.00689 (0.0646)	0.0716 (0.37)	0.00056 (0.012)
PATHOLOGY T STAGE	Fisher's exact test	0.561 (0.801)	0.00378 (0.0405)	0.257 (0.606)	0.31 (0.621)	0.00055 (0.012)	0.00013 (0.00487)	0.102 (0.421)	0.00083 (0.0156)	0.0822 (0.398)	0.00042 (0.012)
PATHOLOGY N STAGE	Fisher's exact test	0.35 (0.65)	0.898 (0.969)	0.261 (0.606)	0.28 (0.619)	0.104 (0.421)	0.00302 (0.0348)	0.127 (0.444)	0.11 (0.421)	0.742 (0.891)	0.108 (0.421)
PATHOLOGY M STAGE	Fisher's exact test	0.272 (0.617)	0.368 (0.651)	0.358 (0.65)	0.297 (0.621)	0.0802 (0.398)	0.102 (0.421)	0.0532 (0.309)	0.339 (0.65)	0.313 (0.621)	0.18 (0.507)
GENDER	Fisher's exact test	0.153 (0.477)	0.00123 (0.0205)	0.00434 (0.0434)	0.0239 (0.167)	0.399 (0.672)	0.979 (0.999)	0.93 (0.976)	0.315 (0.621)	0.243 (0.588)	0.448 (0.704)
RADIATION THERAPY	Fisher's exact test	0.02 (0.15)	0.721 (0.883)	0.106 (0.421)	0.657 (0.842)	0.675 (0.858)	0.631 (0.831)	0.0562 (0.312)	0.591 (0.813)	0.652 (0.842)	0.385 (0.671)
HISTOLOGICAL TYPE	Fisher's exact test	0.427 (0.696)	0.202 (0.548)	0.169 (0.507)	0.239 (0.587)	1e-05 (0.00075)	0.00906 (0.0799)	0.0342 (0.223)	0.0397 (0.248)	0.00279 (0.0348)	1e-05 (0.00075)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.73 (0.883)	0.566 (0.801)	0.291 (0.621)	0.963 (0.989)	0.61 (0.824)	0.205 (0.548)	1 (1.00)	0.23 (0.584)	0.165 (0.504)	0.32 (0.622)
YEAR OF TOBACCO SMOKING ONSET	Kruskal-Wallis (anova)	0.525 (0.79)	0.913 (0.972)	0.147 (0.477)	0.65 (0.842)	0.928 (0.976)	0.908 (0.972)	0.565 (0.801)	0.827 (0.94)	0.814 (0.933)	0.512 (0.784)
RESIDUAL TUMOR	Fisher's exact test	0.36 (0.65)	0.236 (0.587)	0.949 (0.987)	0.435 (0.696)	0.369 (0.651)	0.145 (0.477)	0.793 (0.93)	0.757 (0.902)	0.885 (0.962)	0.631 (0.831)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.554 (0.801)	0.852 (0.941)	0.436 (0.696)	0.152 (0.477)	0.119 (0.429)	0.0856 (0.401)	0.342 (0.65)	0.451 (0.704)	0.729 (0.883)	0.493 (0.763)
RACE	Fisher's exact test	0.226 (0.584)	0.954 (0.987)	0.802 (0.932)	0.845 (0.941)	0.432 (0.696)	0.0965 (0.421)	0.837 (0.941)	0.416 (0.694)	0.395 (0.672)	0.714 (0.883)
ETHNICITY	Fisher's exact test	0.781 (0.922)	0.815 (0.933)	0.853 (0.941)	1 (1.00)	0.182 (0.507)	0.398 (0.672)	0.575 (0.801)	0.607 (0.824)	0.526 (0.79)	0.686 (0.865)

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	4
Number of samples	26	74	46	38

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

P value = 0.182 (logrank test), Q value = 0.51

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	183	100	0.1 - 90.1 (15.3)
subtype1	25	18	2.2 - 75.1 (12.9)
subtype2	74	44	0.1 - 90.1 (15.1)
subtype3	46	19	0.3 - 63.9 (14.2)
subtype4	38	19	1.1 - 71.7 (17.0)

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 'YEARS_TO_BIRTH'

P value = 0.567 (Kruskal-Wallis (anova)), Q value = 0.8

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

	nPatients	Mean (Std.Dev)
ALL	184	64.9 (11.1)
subtype1	26	67.9 (9.7)
subtype2	74	63.9 (12.0)
subtype3	46	65.3 (9.1)
subtype4	38	64.4 (12.1)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.86 (Fisher's exact test), Q value = 0.94

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	1	5	15	30	121	5	5
subtype1	0	0	0	4	22	0	0
subtype2	1	2	6	13	47	2	3
subtype3	0	2	6	7	28	1	0
subtype4	0	1	3	6	24	2	2

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.561 (Fisher's exact test), Q value = 0.8

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	7	24	147	4
subtype1	0	2	24	0
subtype2	3	8	62	1
subtype3	3	9	31	1
subtype4	1	5	30	2

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 = 0.35 (Fisher's exact test), Q value = 0.65

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

nPatients	0	1
ALL	50	129
subtype1	4	22
subtype2	22	50
subtype3	15	29
subtype4	9	28

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 = 0.272 (Fisher's exact test), Q value = 0.62

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

nPatients	0	1
ALL	85	5
subtype1	10	0
subtype2	35	3
subtype3	26	0
subtype4	14	2

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 = 0.153 (Fisher's exact test), Q value = 0.48

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

nPatients	FEMALE	MALE
ALL	83	101
subtype1	13	13
subtype2	26	48
subtype3	23	23
subtype4	21	17

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 'RADIATION_THERAPY'

P value = 0.02 (Fisher's exact test), Q value = 0.15

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

nPatients	NO	YES
ALL	125	44
subtype1	18	6
subtype2	57	11
subtype3	32	12
subtype4	18	15

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.427 (Fisher's exact test), Q value = 0.7

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

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA	PANCREAS-UNDIFFERENTIATED CARCINOMA
ALL	153	25	4	1
subtype1	22	3	0	1
subtype2	64	9	1	0
subtype3	36	7	3	0
subtype4	31	6	0	0

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.73 (Kruskal-Wallis (anova)), Q value = 0.88

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

	nPatients	Mean (Std.Dev)
ALL	56	26.9 (18.1)
subtype1	12	24.5 (18.3)
subtype2	24	29.6 (19.7)
subtype3	13	23.7 (13.2)
subtype4	7	27.5 (22.1)

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

'Copy Number Ratio CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.525 (Kruskal-Wallis (anova)), Q value = 0.79

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

	nPatients	Mean (Std.Dev)
ALL	46	1970.4 (13.1)
subtype1	9	1964.4 (9.4)
subtype2	19	1971.9 (12.1)
subtype3	12	1971.1 (16.5)
subtype4	6	1973.0 (13.8)

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.36 (Fisher's exact test), Q value = 0.65

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	111	53	5	4
subtype1	15	6	2	0
subtype2	41	25	2	3
subtype3	35	10	1	0
subtype4	20	12	0	1

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.554 (Kruskal-Wallis (anova)), Q value = 0.8

Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	180	3.0 (3.5)
subtype1	26	3.4 (3.2)
subtype2	73	3.0 (3.6)
subtype3	44	2.7 (3.1)
subtype4	37	3.1 (3.8)

Figure S13. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.226 (Fisher's exact test), Q value = 0.58

Table S15. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	7	161
subtype1	3	1	21
subtype2	6	1	66
subtype3	2	3	41
subtype4	0	2	33

Figure S14. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.781 (Fisher's exact test), Q value = 0.92

Table S16. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	136
subtype1	0	17
subtype2	2	53
subtype3	1	39
subtype4	2	27

Figure S15. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S17. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	45	50	52	37

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00993 (logrank test), Q value = 0.083

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	183	99	0.1 - 90.1 (15.3)
subtype1	44	28	0.2 - 71.7 (12.6)
subtype2	50	21	0.3 - 90.1 (19.4)
subtype3	52	34	0.1 - 66.9 (15.4)
subtype4	37	16	0.3 - 49.4 (13.8)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0536 (Kruskal-Wallis (anova)), Q value = 0.31

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	184	64.8 (11.0)
subtype1	45	68.1 (10.4)
subtype2	50	65.0 (11.1)
subtype3	52	63.5 (11.3)
subtype4	37	62.2 (10.6)

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.352 (Fisher's exact test), Q value = 0.65

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	1	5	15	30	121	5	5
subtype1	0	2	4	6	31	1	0
subtype2	1	3	6	6	30	2	1
subtype3	0	0	1	13	34	1	3
subtype4	0	0	4	5	26	1	1

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00378 (Fisher's exact test), Q value = 0.04

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	7	24	147	4
subtype1	2	4	37	1
subtype2	5	12	30	2
subtype3	0	3	49	0
subtype4	0	5	31	1

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.898 (Fisher's exact test), Q value = 0.97

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	50	129
subtype1	14	31
subtype2	14	33
subtype3	13	38
subtype4	9	27

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.368 (Fisher's exact test), Q value = 0.65

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	84	5
subtype1	20	0
subtype2	23	1
subtype3	20	3
subtype4	21	1

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00123 (Fisher's exact test), Q value = 0.02

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	82	102
subtype1	10	35
subtype2	24	26
subtype3	24	28
subtype4	24	13

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.721 (Fisher's exact test), Q value = 0.88

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	125	44
subtype1	30	9
subtype2	34	16
subtype3	35	12
subtype4	26	7

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.202 (Fisher's exact test), Q value = 0.55

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA	PANCREAS-UNDIFFERENTIATED CARCINOMA
ALL	153	25	4	1
subtype1	40	5	0	0
subtype2	37	11	2	0
subtype3	43	7	1	0
subtype4	33	2	1	1

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.566 (Kruskal-Wallis (anova)), Q value = 0.8

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	57	26.8 (17.9)
subtype1	17	25.0 (12.3)
subtype2	12	23.2 (23.1)
subtype3	16	28.9 (17.3)
subtype4	12	30.3 (20.9)

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.913 (Kruskal-Wallis (anova)), Q value = 0.97

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	47	1970.7 (13.2)
subtype1	16	1969.5 (12.2)
subtype2	9	1973.9 (16.0)
subtype3	11	1970.0 (13.0)
subtype4	11	1970.7 (13.7)

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.236 (Fisher's exact test), Q value = 0.59

Table S29. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	111	53	5	4
subtype1	22	18	1	2
subtype2	33	13	0	0
subtype3	30	14	2	2
subtype4	26	8	2	0

Figure S27. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.852 (Kruskal-Wallis (anova)), Q value = 0.94

Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	180	3.0 (3.5)
subtype1	44	3.5 (4.0)
subtype2	48	3.0 (3.7)
subtype3	52	2.7 (2.9)
subtype4	36	2.9 (3.3)

Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.954 (Fisher's exact test), Q value = 0.99

Table S31. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	7	162
subtype1	3	2	39
subtype2	2	2	45
subtype3	4	1	47
subtype4	2	2	31

Figure S29. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.815 (Fisher's exact test), Q value = 0.93

Table S32. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	137
subtype1	1	31
subtype2	1	39
subtype3	1	38
subtype4	2	29

Figure S30. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S33. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	40	25	20	15	23

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.552 (logrank test), Q value = 0.8

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	123	71	0.1 - 71.7 (15.3)
subtype1	40	22	2.0 - 71.7 (15.9)
subtype2	25	16	5.0 - 66.9 (15.3)
subtype3	20	10	0.1 - 68.5 (14.3)
subtype4	15	8	0.3 - 45.5 (17.8)
subtype5	23	15	3.1 - 43.8 (12.0)

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.278 (Kruskal-Wallis (anova)), Q value = 0.62

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	123	65.3 (11.5)
subtype1	40	64.9 (11.0)
subtype2	25	67.0 (12.0)
subtype3	20	60.5 (10.8)
subtype4	15	68.1 (9.4)
subtype5	23	66.8 (13.1)

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.311 (Fisher's exact test), Q value = 0.62

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	5	8	19	83	5	2
subtype1	1	5	7	27	0	0
subtype2	1	0	4	16	3	1
subtype3	1	2	4	12	0	0
subtype4	1	0	0	12	1	1
subtype5	1	1	4	16	1	0

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.257 (Fisher's exact test), Q value = 0.61

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	6	11	101	4
subtype1	1	7	32	0
subtype2	1	0	22	2
subtype3	2	2	15	0
subtype4	1	1	12	1
subtype5	1	1	20	1

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.261 (Fisher's exact test), Q value = 0.61

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	32	90
subtype1	13	27
subtype2	6	19
subtype3	7	12
subtype4	1	14
subtype5	5	18

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.358 (Fisher's exact test), Q value = 0.65

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	60	2
subtype1	21	0
subtype2	11	1
subtype3	8	0
subtype4	8	1
subtype5	12	0

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.00434 (Fisher's exact test), Q value = 0.043

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	57	66
subtype1	14	26
subtype2	20	5
subtype3	7	13
subtype4	6	9
subtype5	10	13

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.106 (Fisher's exact test), Q value = 0.42

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	85	29
subtype1	24	11
subtype2	17	8
subtype3	12	6
subtype4	12	3
subtype5	20	1

Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.169 (Fisher's exact test), Q value = 0.51

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	108	12	2
subtype1	36	3	1
subtype2	20	3	1
subtype3	18	2	0
subtype4	11	4	0
subtype5	23	0	0

Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.291 (Kruskal-Wallis (anova)), Q value = 0.62

Table S43. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	39	26.0 (16.7)
subtype1	15	31.7 (12.0)
subtype2	8	25.1 (21.3)
subtype3	7	20.1 (21.6)
subtype4	6	17.8 (15.3)
subtype5	3	30.0 (10.0)

Figure S40. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.147 (Kruskal-Wallis (anova)), Q value = 0.48

Table S44. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	32	1970.9 (13.4)
subtype1	11	1968.0 (11.5)
subtype2	9	1966.3 (12.0)
subtype3	6	1978.2 (16.0)
subtype4	4	1980.5 (6.6)
subtype5	2	1966.0 (25.5)

Figure S41. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.949 (Fisher's exact test), Q value = 0.99

Table S45. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	72	39	2	2
subtype1	22	14	1	1
subtype2	12	9	0	0
subtype3	15	5	0	0
subtype4	10	4	0	0
subtype5	13	7	1	1

Figure S42. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.436 (Kruskal-Wallis (anova)), Q value = 0.7

Table S46. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	122	2.9 (3.2)
subtype1	40	2.2 (2.5)
subtype2	25	3.5 (3.9)
subtype3	19	2.5 (2.6)
subtype4	15	3.8 (4.2)
subtype5	23	3.3 (3.4)

Figure S43. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.802 (Fisher's exact test), Q value = 0.93

Table S47. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	113
subtype1	1	3	35
subtype2	1	0	21
subtype3	0	0	20
subtype4	0	0	15
subtype5	0	1	22

Figure S44. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.853 (Fisher's exact test), Q value = 0.94

Table S48. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	95
subtype1	2	29
subtype2	0	17
subtype3	0	17
subtype4	0	12
subtype5	1	20

Figure S45. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S49. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	48	25	16	16	18

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.262 (logrank test), Q value = 0.61

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	123	71	0.1 - 71.7 (15.3)
subtype1	48	29	2.0 - 71.7 (14.0)
subtype2	25	15	4.8 - 66.9 (15.3)
subtype3	16	7	3.1 - 59.0 (14.1)
subtype4	16	7	0.3 - 68.5 (20.0)
subtype5	18	13	0.1 - 43.8 (13.1)

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.615 (Kruskal-Wallis (anova)), Q value = 0.82

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	123	65.3 (11.5)
subtype1	48	65.3 (12.0)
subtype2	25	67.8 (11.6)
subtype3	16	63.0 (10.1)
subtype4	16	66.6 (7.7)
subtype5	18	63.1 (14.1)

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.228 (Fisher's exact test), Q value = 0.58

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	5	8	19	83	5	2
subtype1	3	4	8	33	0	0
subtype2	1	0	4	15	4	1
subtype3	0	2	4	9	0	0
subtype4	1	1	1	11	1	1
subtype5	0	1	2	15	0	0

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.31 (Fisher's exact test), Q value = 0.62

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	6	11	101	4
subtype1	3	6	39	0
subtype2	1	0	21	3
subtype3	0	1	14	0
subtype4	1	2	12	1
subtype5	1	2	15	0

Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.28 (Fisher's exact test), Q value = 0.62

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	32	90
subtype1	15	33
subtype2	7	18
subtype3	6	10
subtype4	2	13
subtype5	2	16

Figure S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.297 (Fisher's exact test), Q value = 0.62

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	60	2
subtype1	24	0
subtype2	13	1
subtype3	6	0
subtype4	7	1
subtype5	10	0

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0239 (Fisher's exact test), Q value = 0.17

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	57	66
subtype1	21	27
subtype2	18	7
subtype3	4	12
subtype4	5	11
subtype5	9	9

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.657 (Fisher's exact test), Q value = 0.84

Table S57. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	85	29
subtype1	31	11
subtype2	17	8
subtype3	12	3
subtype4	11	5
subtype5	14	2

Figure S53. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.239 (Fisher's exact test), Q value = 0.59

Table S58. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	108	12	2
subtype1	42	5	1
subtype2	22	2	0
subtype3	15	1	0
subtype4	11	4	1
subtype5	18	0	0

Figure S54. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.963 (Kruskal-Wallis (anova)), Q value = 0.99

Table S59. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	39	26.0 (16.7)
subtype1	18	27.8 (14.7)
subtype2	8	25.1 (21.3)
subtype3	6	24.7 (22.1)
subtype4	4	21.5 (16.5)
subtype5	3	26.7 (11.5)

Figure S55. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.65 (Kruskal-Wallis (anova)), Q value = 0.84

Table S60. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	32	1970.9 (13.4)
subtype1	13	1971.2 (13.1)
subtype2	8	1964.8 (11.8)
subtype3	5	1974.8 (15.8)
subtype4	3	1977.7 (4.0)
subtype5	3	1972.7 (22.2)

Figure S56. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.435 (Fisher's exact test), Q value = 0.7

Table S61. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	72	39	2	2
subtype1	25	18	1	1
subtype2	13	8	0	0
subtype3	13	2	0	1
subtype4	12	4	0	0
subtype5	9	7	1	0

Figure S57. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.152 (Kruskal-Wallis (anova)), Q value = 0.48

Table S62. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	122	2.9 (3.2)
subtype1	48	2.0 (2.0)
subtype2	25	3.3 (3.7)
subtype3	16	2.5 (2.9)
subtype4	15	4.3 (4.6)
subtype5	18	4.1 (3.7)

Figure S58. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.845 (Fisher's exact test), Q value = 0.94

Table S63. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	113
subtype1	1	3	44
subtype2	1	0	21
subtype3	0	0	16
subtype4	0	0	16
subtype5	0	1	16

Figure S59. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

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

Table S64. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	95
subtype1	2	36
subtype2	0	17
subtype3	0	12
subtype4	0	14
subtype5	1	16

Figure S60. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S65. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	39	26	43	40	30

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00138 (logrank test), Q value = 0.021

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	177	93	0.1 - 90.1 (15.3)
subtype1	39	25	0.2 - 66.3 (15.3)
subtype2	26	6	6.0 - 90.1 (23.0)
subtype3	43	28	3.1 - 66.9 (15.1)
subtype4	39	23	0.3 - 75.1 (15.7)
subtype5	30	11	0.1 - 71.7 (13.9)

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.309 (Kruskal-Wallis (anova)), Q value = 0.62

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	178	64.6 (10.9)
subtype1	39	66.8 (11.9)
subtype2	26	62.1 (11.1)
subtype3	43	64.0 (10.6)
subtype4	40	65.5 (9.5)
subtype5	30	63.4 (11.8)

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0245 (Fisher's exact test), Q value = 0.17

Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	1	5	15	28	119	4	4
subtype1	0	2	2	8	24	1	2
subtype2	1	2	7	1	11	1	1
subtype3	0	0	2	10	29	1	1
subtype4	0	1	3	4	31	1	0
subtype5	0	0	1	5	24	0	0

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00055 (Fisher's exact test), Q value = 0.012

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	7	24	142	3
subtype1	2	3	33	1
subtype2	4	8	11	1
subtype3	0	3	40	0
subtype4	1	8	30	1
subtype5	0	2	28	0

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.104 (Fisher's exact test), Q value = 0.42

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	49	124
subtype1	13	26
subtype2	11	12
subtype3	12	30
subtype4	8	32
subtype5	5	24

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0802 (Fisher's exact test), Q value = 0.4

Table S71. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	80	4
subtype1	9	2
subtype2	8	1
subtype3	23	1
subtype4	24	0
subtype5	16	0

Figure S66. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.399 (Fisher's exact test), Q value = 0.67

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

nPatients	FEMALE	MALE
ALL	80	98
subtype1	15	24
subtype2	10	16
subtype3	18	25
subtype4	19	21
subtype5	18	12

Figure S67. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.675 (Fisher's exact test), Q value = 0.86

Table S73. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	119	44
subtype1	26	9
subtype2	17	9
subtype3	31	10
subtype4	26	12
subtype5	19	4

Figure S68. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00075

Table S74. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA	PANCREAS-UNDIFFERENTIATED CARCINOMA
ALL	147	25	4	1
subtype1	28	9	2	0
subtype2	14	12	0	0
subtype3	41	1	0	0
subtype4	39	0	1	0
subtype5	25	3	1	1

Figure S69. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.61 (Kruskal-Wallis (anova)), Q value = 0.82

Table S75. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	57	26.8 (17.9)
subtype1	13	27.5 (8.0)
subtype2	4	25.6 (30.6)
subtype3	16	30.4 (19.8)
subtype4	16	23.3 (20.9)
subtype5	8	26.3 (14.8)

Figure S70. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.928 (Kruskal-Wallis (anova)), Q value = 0.98

Table S76. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	47	1970.7 (13.2)
subtype1	10	1968.0 (11.4)
subtype2	4	1970.2 (16.1)
subtype3	11	1969.5 (14.7)
subtype4	13	1973.5 (13.3)
subtype5	9	1971.7 (14.0)

Figure S71. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.369 (Fisher's exact test), Q value = 0.65

Table S77. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	106	53	5	4
subtype1	23	11	1	2
subtype2	17	5	1	1
subtype3	21	16	3	0
subtype4	23	14	0	1
subtype5	22	7	0	0

Figure S72. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.119 (Kruskal-Wallis (anova)), Q value = 0.43

Table S78. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	174	3.0 (3.4)
subtype1	39	2.3 (2.4)
subtype2	24	2.5 (4.2)
subtype3	42	3.0 (3.1)
subtype4	40	4.0 (4.1)
subtype5	29	3.0 (3.3)

Figure S73. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.432 (Fisher's exact test), Q value = 0.7

Table S79. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	6	157
subtype1	4	0	35
subtype2	1	0	25
subtype3	4	1	36
subtype4	1	3	35
subtype5	1	2	26

Figure S74. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.182 (Fisher's exact test), Q value = 0.51

Table S80. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	131
subtype1	0	29
subtype2	0	19
subtype3	1	28
subtype4	1	33
subtype5	3	22

Figure S75. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S81. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	75	75	28

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0147 (logrank test), Q value = 0.12

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	177	93	0.1 - 90.1 (15.3)
subtype1	74	42	0.1 - 75.1 (15.1)
subtype2	75	41	0.2 - 71.7 (14.2)
subtype3	28	10	6.4 - 90.1 (20.3)

Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.174 (Kruskal-Wallis (anova)), Q value = 0.51

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	178	64.6 (10.9)
subtype1	75	64.9 (11.0)
subtype2	75	65.7 (10.6)
subtype3	28	60.8 (11.3)

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 6e-05 (Fisher's exact test), Q value = 0.003

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	1	5	15	28	119	4	4
subtype1	0	1	3	9	61	1	0
subtype2	0	3	3	17	47	2	3
subtype3	1	1	9	2	11	1	1

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00013 (Fisher's exact test), Q value = 0.0049

Table S85. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	7	24	142	3
subtype1	1	12	61	1
subtype2	3	3	68	1
subtype3	3	9	13	1

Figure S79. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00302 (Fisher's exact test), Q value = 0.035

Table S86. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	49	124
subtype1	13	62
subtype2	23	50
subtype3	13	12

Figure S80. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.102 (Fisher's exact test), Q value = 0.42

Table S87. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	80	4
subtype1	39	0
subtype2	31	3
subtype3	10	1

Figure S81. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.979 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	98
subtype1	34	41
subtype2	33	42
subtype3	13	15

Figure S82. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.631 (Fisher's exact test), Q value = 0.83

Table S89. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	119	44
subtype1	48	20
subtype2	53	16
subtype3	18	8

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00906 (Fisher's exact test), Q value = 0.08

Table S90. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA	PANCREAS-UNDIFFERENTIATED CARCINOMA
ALL	147	25	4	1
subtype1	68	6	1	0
subtype2	61	9	3	1
subtype3	18	10	0	0

Figure S84. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.205 (Kruskal-Wallis (anova)), Q value = 0.55

Table S91. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	57	26.8 (17.9)
subtype1	31	22.9 (17.5)
subtype2	22	32.1 (18.3)
subtype3	4	28.6 (15.0)

Figure S85. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.908 (Kruskal-Wallis (anova)), Q value = 0.97

Table S92. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	47	1970.7 (13.2)
subtype1	26	1971.5 (12.8)
subtype2	16	1969.8 (13.7)
subtype3	5	1970.0 (16.0)

Figure S86. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.145 (Fisher's exact test), Q value = 0.48

Table S93. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	106	53	5	4
subtype1	42	27	0	2
subtype2	42	21	4	2
subtype3	22	5	1	0

Figure S87. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0856 (Kruskal-Wallis (anova)), Q value = 0.4

Table S94. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	174	3.0 (3.4)
subtype1	75	3.3 (3.5)
subtype2	73	2.8 (3.2)
subtype3	26	2.5 (4.2)

Figure S88. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0965 (Fisher's exact test), Q value = 0.42

Table S95. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	6	157
subtype1	2	3	69
subtype2	9	2	62
subtype3	0	1	26

Figure S89. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.398 (Fisher's exact test), Q value = 0.67

Table S96. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	131
subtype1	2	56
subtype2	1	53
subtype3	2	22

Figure S90. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S97. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	52	47	26	31	22

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.12 (logrank test), Q value = 0.43

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	177	93	0.1 - 90.1 (15.3)
subtype1	51	29	0.1 - 66.3 (14.1)
subtype2	47	25	0.3 - 71.7 (13.3)
subtype3	26	13	0.3 - 75.1 (19.4)
subtype4	31	15	0.4 - 90.1 (21.7)
subtype5	22	11	5.0 - 68.5 (14.4)

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.998 (Kruskal-Wallis (anova)), Q value = 1

Table S99. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	178	64.6 (10.9)
subtype1	52	64.3 (11.9)
subtype2	47	64.9 (10.8)
subtype3	26	64.3 (10.2)
subtype4	31	64.3 (10.2)
subtype5	22	65.2 (11.8)

Figure S92. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.0663 (Fisher's exact test), Q value = 0.36

Table S100. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	1	5	15	28	119	4	4
subtype1	0	1	3	13	32	2	1
subtype2	0	0	3	4	38	2	0
subtype3	0	1	1	6	18	0	0
subtype4	1	1	3	3	20	0	2
subtype5	0	2	5	2	11	0	1

Figure S93. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.102 (Fisher's exact test), Q value = 0.42

Table S101. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	7	24	142	3
subtype1	1	6	44	1
subtype2	0	4	41	2
subtype3	1	6	19	0
subtype4	2	3	25	0
subtype5	3	5	13	0

Figure S94. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.127 (Fisher's exact test), Q value = 0.44

Table S102. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	49	124
subtype1	17	34
subtype2	7	39
subtype3	8	18
subtype4	8	21
subtype5	9	12

Figure S95. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0532 (Fisher's exact test), Q value = 0.31

Table S103. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	80	4
subtype1	18	1
subtype2	29	0
subtype3	16	0
subtype4	9	2
subtype5	8	1

Figure S96. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.93 (Fisher's exact test), Q value = 0.98

Table S104. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	98
subtype1	24	28
subtype2	23	24
subtype3	10	16
subtype4	13	18
subtype5	10	12

Figure S97. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.0562 (Fisher's exact test), Q value = 0.31

Table S105. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	119	44
subtype1	31	15
subtype2	35	4
subtype3	16	9
subtype4	23	8
subtype5	14	8

Figure S98. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0342 (Fisher's exact test), Q value = 0.22

Table S106. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA	PANCREAS-UNDIFFERENTIATED CARCINOMA
ALL	147	25	4	1
subtype1	43	8	0	0
subtype2	42	3	1	1
subtype3	24	1	1	0
subtype4	20	9	2	0
subtype5	18	4	0	0

Figure S99. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 1 (Kruskal-Wallis (anova)), Q value = 1

Table S107. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	57	26.8 (17.9)
subtype1	21	26.2 (14.3)
subtype2	14	26.9 (17.0)
subtype3	10	28.2 (24.4)
subtype4	8	25.1 (18.0)
subtype5	4	30.0 (28.4)

Figure S100. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.565 (Kruskal-Wallis (anova)), Q value = 0.8

Table S108. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	47	1970.7 (13.2)
subtype1	16	1969.6 (13.2)
subtype2	13	1967.2 (13.5)
subtype3	9	1973.4 (13.8)
subtype4	6	1977.2 (11.5)
subtype5	3	1971.7 (15.5)

Figure S101. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.793 (Fisher's exact test), Q value = 0.93

Table S109. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	106	53	5	4
subtype1	29	16	2	1
subtype2	31	12	1	1
subtype3	12	12	0	0
subtype4	21	8	1	1
subtype5	13	5	1	1

Figure S102. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.342 (Kruskal-Wallis (anova)), Q value = 0.65

Table S110. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	174	3.0 (3.4)
subtype1	51	2.8 (3.5)
subtype2	46	2.9 (2.6)
subtype3	26	3.3 (3.7)
subtype4	30	3.4 (3.9)
subtype5	21	2.4 (4.1)

Figure S103. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.837 (Fisher's exact test), Q value = 0.94

Table S111. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	6	157
subtype1	5	1	45
subtype2	1	2	43
subtype3	1	1	23
subtype4	3	1	27
subtype5	1	1	19

Figure S104. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.575 (Fisher's exact test), Q value = 0.8

Table S112. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	131
subtype1	0	37
subtype2	2	36
subtype3	1	18
subtype4	1	23
subtype5	1	17

Figure S105. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S113. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	102	43	33

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.112 (logrank test), Q value = 0.42

Table S114. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	177	93	0.1 - 90.1 (15.3)
subtype1	101	57	0.1 - 71.7 (15.1)
subtype2	43	22	0.3 - 75.1 (15.3)
subtype3	33	14	5.0 - 90.1 (16.4)

Figure S106. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.577 (Kruskal-Wallis (anova)), Q value = 0.8

Table S115. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	178	64.6 (10.9)
subtype1	102	64.6 (11.2)
subtype2	43	65.9 (8.8)
subtype3	33	62.7 (12.6)

Figure S107. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.00689 (Fisher's exact test), Q value = 0.065

Table S116. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	1	5	15	28	119	4	4
subtype1	0	2	4	21	70	2	3
subtype2	0	1	3	5	33	1	0
subtype3	1	2	8	2	16	1	1

Figure S108. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00083 (Fisher's exact test), Q value = 0.016

Table S117. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	7	24	142	3
subtype1	2	7	92	1
subtype2	1	9	32	1
subtype3	4	8	18	1

Figure S109. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.11 (Fisher's exact test), Q value = 0.42

Table S118. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	49	124
subtype1	27	73
subtype2	9	34
subtype3	13	17

Figure S110. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.339 (Fisher's exact test), Q value = 0.65

Table S119. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	80	4
subtype1	45	3
subtype2	25	0
subtype3	10	1

Figure S111. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.315 (Fisher's exact test), Q value = 0.62

Table S120. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	98
subtype1	41	61
subtype2	21	22
subtype3	18	15

Figure S112. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.591 (Fisher's exact test), Q value = 0.81

Table S121. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	119	44
subtype1	68	22
subtype2	29	14
subtype3	22	8

Figure S113. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.0397 (Fisher's exact test), Q value = 0.25

Table S122. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA	PANCREAS-UNDIFFERENTIATED CARCINOMA
ALL	147	25	4	1
subtype1	84	13	3	1
subtype2	40	2	1	0
subtype3	23	10	0	0

Figure S114. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.23 (Kruskal-Wallis (anova)), Q value = 0.58

Table S123. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	57	26.8 (17.9)
subtype1	37	29.0 (16.3)
subtype2	15	21.9 (21.4)
subtype3	5	26.0 (19.5)

Figure S115. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.827 (Kruskal-Wallis (anova)), Q value = 0.94

Table S124. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	47	1970.7 (13.2)
subtype1	29	1970.4 (13.2)
subtype2	12	1969.9 (14.3)
subtype3	6	1973.8 (12.4)

Figure S116. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.757 (Fisher's exact test), Q value = 0.9

Table S125. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	106	53	5	4
subtype1	57	32	4	3
subtype2	27	13	0	0
subtype3	22	8	1	1

Figure S117. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.451 (Kruskal-Wallis (anova)), Q value = 0.7

Table S126. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	174	3.0 (3.4)
subtype1	100	2.8 (3.1)
subtype2	43	3.4 (3.7)
subtype3	31	3.0 (4.2)

Figure S118. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.416 (Fisher's exact test), Q value = 0.69

Table S127. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	6	157
subtype1	9	3	88
subtype2	2	2	38
subtype3	0	1	31

Figure S119. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.607 (Fisher's exact test), Q value = 0.82

Table S128. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	131
subtype1	2	72
subtype2	1	32
subtype3	2	27

Figure S120. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S129. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	28	11	27	13	51	27	17

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

P value = 0.143 (logrank test), Q value = 0.48

Table S130. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	173	91	0.1 - 90.1 (15.3)
subtype1	28	14	0.3 - 42.3 (15.2)
subtype2	11	6	2.6 - 31.9 (12.9)
subtype3	27	17	0.1 - 49.4 (16.0)
subtype4	13	5	5.0 - 41.3 (15.2)
subtype5	51	33	0.2 - 90.1 (15.1)
subtype6	26	10	1.1 - 75.1 (13.1)
subtype7	17	6	3.4 - 68.5 (23.2)

Figure S121. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.694 (Kruskal-Wallis (anova)), Q value = 0.87

Table S131. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	174	64.4 (10.9)
subtype1	28	64.9 (9.6)
subtype2	11	61.6 (9.8)
subtype3	27	62.0 (10.7)
subtype4	13	66.6 (13.0)
subtype5	51	65.1 (12.2)
subtype6	27	64.4 (11.7)
subtype7	17	64.9 (7.7)

Figure S122. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0716 (Fisher's exact test), Q value = 0.37

Table S132. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	1	5	15	28	115	4	4
subtype1	0	1	1	4	22	0	0
subtype2	0	0	0	4	7	0	0
subtype3	0	1	2	3	19	0	2
subtype4	0	1	0	3	8	1	0
subtype5	1	1	3	12	32	1	1
subtype6	0	1	3	2	20	1	0
subtype7	0	0	6	0	7	1	1

Figure S123. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0822 (Fisher's exact test), Q value = 0.4

Table S133. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	7	24	138	3
subtype1	1	6	21	0
subtype2	0	0	11	0
subtype3	1	3	23	0
subtype4	1	0	11	1
subtype5	2	5	44	0
subtype6	2	4	20	1
subtype7	0	6	8	1

Figure S124. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.742 (Fisher's exact test), Q value = 0.89

Table S134. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	49	120
subtype1	6	22
subtype2	4	7
subtype3	8	19
subtype4	3	9
subtype5	14	35
subtype6	7	20
subtype7	7	8

Figure S125. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.313 (Fisher's exact test), Q value = 0.62

Table S135. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	77	4
subtype1	14	0
subtype2	9	0
subtype3	17	2
subtype4	7	0
subtype5	18	1
subtype6	10	0
subtype7	2	1

Figure S126. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.243 (Fisher's exact test), Q value = 0.59

Table S136. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	94
subtype1	11	17
subtype2	2	9
subtype3	14	13
subtype4	9	4
subtype5	26	25
subtype6	11	16
subtype7	7	10

Figure S127. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.652 (Fisher's exact test), Q value = 0.84

Table S137. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	116	43
subtype1	16	11
subtype2	7	3
subtype3	18	4
subtype4	11	2
subtype5	35	13
subtype6	16	6
subtype7	13	4

Figure S128. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00279 (Fisher's exact test), Q value = 0.035

Table S138. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA	PANCREAS-UNDIFFERENTIATED CARCINOMA
ALL	144	24	4	1
subtype1	23	4	1	0
subtype2	9	2	0	0
subtype3	25	2	0	0
subtype4	9	1	2	1
subtype5	46	3	1	0
subtype6	23	4	0	0
subtype7	9	8	0	0

Figure S129. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.165 (Kruskal-Wallis (anova)), Q value = 0.5

Table S139. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	55	27.0 (18.2)
subtype1	12	17.0 (13.7)
subtype2	6	38.0 (28.6)
subtype3	5	30.7 (18.9)
subtype4	3	14.7 (9.2)
subtype5	17	31.7 (17.4)
subtype6	11	26.5 (15.7)
subtype7	1	25.0 (NA)

Figure S130. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.814 (Kruskal-Wallis (anova)), Q value = 0.93

Table S140. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	47	1970.7 (13.2)
subtype1	11	1971.8 (12.5)
subtype2	4	1970.0 (7.6)
subtype3	6	1977.2 (15.0)
subtype4	3	1970.0 (20.0)
subtype5	11	1970.3 (13.4)
subtype6	10	1966.6 (12.7)
subtype7	2	1971.5 (24.7)

Figure S131. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.885 (Fisher's exact test), Q value = 0.96

Table S141. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	104	51	5	4
subtype1	17	8	0	0
subtype2	6	4	1	0
subtype3	19	7	0	1
subtype4	10	3	0	0
subtype5	25	17	3	1
subtype6	16	8	0	1
subtype7	11	4	1	1

Figure S132. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.729 (Kruskal-Wallis (anova)), Q value = 0.88

Table S142. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	170	2.9 (3.4)
subtype1	28	2.9 (3.4)
subtype2	11	2.5 (3.7)
subtype3	27	3.1 (3.3)
subtype4	12	2.9 (3.9)
subtype5	50	3.0 (3.0)
subtype6	27	2.8 (3.4)
subtype7	15	2.5 (4.4)

Figure S133. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.395 (Fisher's exact test), Q value = 0.67

Table S143. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	10	6	154
subtype1	1	1	26
subtype2	1	0	10
subtype3	1	3	20
subtype4	1	0	12
subtype5	6	1	43
subtype6	0	1	26
subtype7	0	0	17

Figure S134. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.526 (Fisher's exact test), Q value = 0.79

Table S144. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	130
subtype1	1	18
subtype2	0	4
subtype3	0	22
subtype4	1	11
subtype5	1	40
subtype6	2	21
subtype7	0	14

Figure S135. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S145. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	23	44	19	54	26	8

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

P value = 0.00203 (logrank test), Q value = 0.028

Table S146. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	173	91	0.1 - 90.1 (15.3)
subtype1	23	11	0.2 - 37.1 (16.8)
subtype2	44	29	3.1 - 66.9 (13.5)
subtype3	19	7	1.1 - 71.7 (15.2)
subtype4	53	32	0.1 - 75.1 (15.2)
subtype5	26	12	5.9 - 59.0 (13.7)
subtype6	8	0	32.8 - 90.1 (55.8)

Figure S136. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.293 (Kruskal-Wallis (anova)), Q value = 0.62

Table S147. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	174	64.4 (10.9)
subtype1	23	62.6 (13.8)
subtype2	44	63.7 (10.7)
subtype3	19	68.4 (9.7)
subtype4	54	65.6 (9.4)
subtype5	26	63.5 (12.3)
subtype6	8	57.9 (9.2)

Figure S137. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00056 (Fisher's exact test), Q value = 0.012

Table S148. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	1	5	15	28	115	4	4
subtype1	0	1	1	4	15	0	2
subtype2	0	0	2	11	29	2	0
subtype3	0	1	0	5	12	0	1
subtype4	0	1	3	6	42	1	1
subtype5	0	2	4	2	16	1	0
subtype6	1	0	5	0	1	0	0

Figure S138. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00042 (Fisher's exact test), Q value = 0.012

Table S149. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	7	24	138	3
subtype1	1	3	19	0
subtype2	0	3	40	1
subtype3	1	0	18	0
subtype4	1	8	44	1
subtype5	3	5	16	1
subtype6	1	5	1	0

Figure S139. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.108 (Fisher's exact test), Q value = 0.42

Table S150. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	49	120
subtype1	7	16
subtype2	12	31
subtype3	5	13
subtype4	11	43
subtype5	10	16
subtype6	4	1

Figure S140. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.18 (Fisher's exact test), Q value = 0.51

Table S151. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	77	4
subtype1	10	2
subtype2	18	0
subtype3	8	1
subtype4	29	1
subtype5	12	0
subtype6	0	0

Figure S141. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.448 (Fisher's exact test), Q value = 0.7

Table S152. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	94
subtype1	6	17
subtype2	20	24
subtype3	9	10
subtype4	28	26
subtype5	13	13
subtype6	4	4

Figure S142. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

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

Table S153. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	116	43
subtype1	15	5
subtype2	30	14
subtype3	14	1
subtype4	35	14
subtype5	15	8
subtype6	7	1

Figure S143. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00075

Table S154. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA	PANCREAS-UNDIFFERENTIATED CARCINOMA
ALL	144	24	4	1
subtype1	19	4	0	0
subtype2	38	5	0	0
subtype3	12	3	3	1
subtype4	51	2	1	0
subtype5	24	2	0	0
subtype6	0	8	0	0

Figure S144. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.32 (Kruskal-Wallis (anova)), Q value = 0.62

Table S155. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	55	27.0 (18.2)
subtype1	12	28.8 (20.6)
subtype2	13	32.2 (16.5)
subtype3	5	24.0 (8.2)
subtype4	18	22.0 (21.0)
subtype5	6	29.9 (15.8)
subtype6	1	25.0 (NA)

Figure S145. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.512 (Kruskal-Wallis (anova)), Q value = 0.78

Table S156. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	47	1970.7 (13.2)
subtype1	8	1969.2 (13.9)
subtype2	10	1968.8 (13.4)
subtype3	5	1978.4 (12.2)
subtype4	15	1972.4 (12.8)
subtype5	7	1966.0 (12.7)
subtype6	2	1971.5 (24.7)

Figure S146. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.631 (Fisher's exact test), Q value = 0.83

Table S157. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	104	51	5	4
subtype1	15	6	1	0
subtype2	24	12	3	1
subtype3	14	3	0	1
subtype4	28	21	0	1
subtype5	17	7	1	1
subtype6	6	2	0	0

Figure S147. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.493 (Kruskal-Wallis (anova)), Q value = 0.76

Table S158. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	170	2.9 (3.4)
subtype1	23	2.4 (3.1)
subtype2	43	2.9 (3.1)
subtype3	18	1.8 (1.6)
subtype4	54	3.3 (3.5)
subtype5	26	3.0 (3.7)
subtype6	6	3.5 (6.4)

Figure S148. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.714 (Fisher's exact test), Q value = 0.88

Table S159. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	10	6	154
subtype1	3	0	20
subtype2	4	2	36
subtype3	1	0	18
subtype4	2	3	48
subtype5	0	1	24
subtype6	0	0	8

Figure S149. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.686 (Fisher's exact test), Q value = 0.86

Table S160. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	130
subtype1	0	13
subtype2	1	30
subtype3	1	15
subtype4	1	44
subtype5	2	21
subtype6	0	7

Figure S150. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/PAAD-TP/22541963/PAAD-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/PAAD-TP/22507097/PAAD-TP.merged_data.txt
Number of patients = 185
Number of clustering approaches = 10
Number of selected clinical features = 15
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

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] 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)

[5] 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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