Correlation between aggregated molecular cancer subtypes and selected clinical features

Stomach Adenocarcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1125RSC

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 13 clinical features across 438 patients, 39 significant findings detected with P value < 0.05 and Q value < 0.25.

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

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 39 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	0.941 (0.971)	0.234 (0.41)	0.764 (0.878)	0.0255 (0.128)	0.0325 (0.146)	0.083 (0.22)	0.108 (0.27)	0.18 (0.361)	0.104 (0.266)	0.414 (0.598)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.3 (0.489)	0.000132 (0.0026)	0.0595 (0.18)	0.328 (0.508)	0.043 (0.153)	0.000276 (0.00358)	0.0056 (0.0428)	0.0364 (0.15)	0.0393 (0.153)	0.087 (0.226)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.322 (0.505)	0.736 (0.87)	0.219 (0.401)	0.0079 (0.0467)	0.0228 (0.123)	0.00496 (0.0428)	0.584 (0.728)	0.0579 (0.18)	0.0771 (0.213)	0.694 (0.835)
PATHOLOGY T STAGE	Fisher's exact test	0.186 (0.361)	0.211 (0.394)	0.00769 (0.0467)	0.00021 (0.00303)	0.00014 (0.0026)	0.0443 (0.153)	0.00065 (0.00704)	0.0403 (0.153)	0.00418 (0.0388)	0.302 (0.489)
PATHOLOGY N STAGE	Fisher's exact test	0.307 (0.489)	0.434 (0.6)	0.874 (0.93)	0.964 (0.972)	0.0369 (0.15)	0.00632 (0.0447)	0.183 (0.361)	0.17 (0.361)	0.179 (0.361)	0.424 (0.599)
PATHOLOGY M STAGE	Fisher's exact test	0.294 (0.489)	0.0768 (0.213)	0.898 (0.934)	0.0556 (0.18)	0.125 (0.301)	0.308 (0.489)	0.403 (0.589)	0.79 (0.878)	0.958 (0.972)	0.538 (0.707)
GENDER	Fisher's exact test	0.0347 (0.15)	0.787 (0.878)	0.265 (0.45)	0.801 (0.883)	0.831 (0.908)	0.0253 (0.128)	0.858 (0.922)	0.557 (0.709)	0.949 (0.971)	0.267 (0.45)
HISTOLOGICAL TYPE	Fisher's exact test	0.00225 (0.0225)	1e-05 (0.000433)	0.00756 (0.0467)	0.227 (0.408)	0.00018 (0.00292)	1e-05 (0.000433)	2e-05 (0.00065)	0.0004 (0.00473)	5e-05 (0.0013)	1e-05 (0.000433)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.212 (0.394)	0.461 (0.63)	0.124 (0.301)	0.00653 (0.0447)	0.00829 (0.0469)	0.148 (0.336)	0.77 (0.878)	0.353 (0.536)	0.588 (0.728)	0.229 (0.408)
COMPLETENESS OF RESECTION	Fisher's exact test	0.887 (0.93)	0.0831 (0.22)	0.551 (0.709)	0.0422 (0.153)	0.55 (0.709)	0.759 (0.878)	0.154 (0.339)	0.786 (0.878)	0.471 (0.638)	0.0594 (0.18)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.128 (0.302)	0.181 (0.361)	0.43 (0.6)	0.716 (0.855)	0.00544 (0.0428)	0.366 (0.547)	0.0761 (0.213)	0.0284 (0.132)	0.18 (0.361)	0.147 (0.336)
RACE	Fisher's exact test	0.392 (0.579)	0.562 (0.709)	0.151 (0.339)	0.182 (0.361)	0.608 (0.745)	0.645 (0.784)	0.028 (0.132)	0.245 (0.425)	0.532 (0.706)	0.0452 (0.153)
ETHNICITY	Fisher's exact test	0.882 (0.93)	0.529 (0.706)	0.763 (0.878)	0.354 (0.536)	0.423 (0.599)	0.046 (0.153)	0.839 (0.909)	1 (1.00)	0.0714 (0.211)	0.19 (0.364)

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	5
Number of samples	121	186	36	74	19

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

P value = 0.941 (logrank test), Q value = 0.97

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	404	150	0.0 - 122.3 (12.8)
subtype1	114	43	0.1 - 72.2 (12.9)
subtype2	173	62	0.0 - 116.4 (12.8)
subtype3	34	16	0.1 - 79.1 (14.1)
subtype4	67	24	0.1 - 122.3 (11.9)
subtype5	16	5	0.8 - 24.2 (12.7)

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 = 0.3 (Kruskal-Wallis (anova)), Q value = 0.49

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

	nPatients	Mean (Std.Dev)
ALL	429	65.7 (10.8)
subtype1	119	67.2 (10.0)
subtype2	185	64.7 (11.7)
subtype3	35	67.6 (10.1)
subtype4	72	64.6 (9.8)
subtype5	18	67.7 (9.3)

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 'NEOPLASM_DISEASESTAGE'

P value = 0.322 (Fisher's exact test), Q value = 0.5

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	3	14	41	31	38	55	4	85	62	41	46
subtype1	1	5	15	8	10	16	0	28	13	7	14
subtype2	1	3	20	12	18	26	2	31	28	17	20
subtype3	0	2	3	2	4	1	0	7	8	3	5
subtype4	0	2	2	7	5	11	2	17	10	12	4
subtype5	1	2	1	2	1	1	0	2	3	2	3

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 = 0.186 (Fisher's exact test), Q value = 0.36

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

nPatients	T1	T2	T3	T4
ALL	22	90	195	119
subtype1	6	29	56	28
subtype2	7	36	80	58
subtype3	2	7	20	7
subtype4	4	11	35	22
subtype5	3	7	4	4

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 = 0.307 (Fisher's exact test), Q value = 0.49

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

nPatients	N0	N1	N2	N3
ALL	129	118	84	89
subtype1	38	36	23	19
subtype2	58	52	32	37
subtype3	11	5	8	11
subtype4	18	22	18	14
subtype5	4	3	3	8

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 = 0.294 (Fisher's exact test), Q value = 0.49

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

nPatients	0	1
ALL	387	30
subtype1	106	8
subtype2	162	16
subtype3	32	4
subtype4	69	2
subtype5	18	0

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 = 0.0347 (Fisher's exact test), Q value = 0.15

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

nPatients	FEMALE	MALE
ALL	158	278
subtype1	37	84
subtype2	79	107
subtype3	7	29
subtype4	26	48
subtype5	9	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 = 0.00225 (Fisher's exact test), Q value = 0.022

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

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	71	162	81	78	21	8	13
subtype1	7	44	29	30	4	4	2
subtype2	42	74	27	20	12	2	9
subtype3	7	8	9	10	1	1	0
subtype4	12	30	13	13	3	0	2
subtype5	3	6	3	5	1	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 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.212 (Fisher's exact test), Q value = 0.39

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

nPatients	NO	YES
ALL	6	430
subtype1	2	119
subtype2	1	185
subtype3	0	36
subtype4	2	72
subtype5	1	18

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.887 (Fisher's exact test), Q value = 0.93

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

nPatients	R0	R1	R2	RX
ALL	347	18	19	25
subtype1	95	4	8	5
subtype2	149	8	8	13
subtype3	29	2	2	1
subtype4	56	4	1	5
subtype5	18	0	0	1

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.128 (Kruskal-Wallis (anova)), Q value = 0.3

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

	nPatients	Mean (Std.Dev)
ALL	387	5.7 (8.4)
subtype1	106	5.8 (10.0)
subtype2	169	4.7 (6.9)
subtype3	31	8.0 (9.6)
subtype4	63	5.9 (8.2)
subtype5	18	9.1 (9.7)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.392 (Fisher's exact test), Q value = 0.58

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	88	11	1	274
subtype1	26	5	1	69
subtype2	34	2	0	129
subtype3	6	2	0	18
subtype4	18	2	0	49
subtype5	4	0	0	9

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.882 (Fisher's exact test), Q value = 0.93

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	313
subtype1	1	80
subtype2	3	142
subtype3	0	21
subtype4	0	58
subtype5	0	12

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S15. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	121	120	42	107

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.234 (logrank test), Q value = 0.41

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	377	134	0.1 - 122.3 (12.8)
subtype1	117	49	0.1 - 79.1 (12.8)
subtype2	115	34	0.1 - 65.2 (13.6)
subtype3	42	11	0.1 - 55.4 (12.6)
subtype4	103	40	0.1 - 122.3 (12.4)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000132 (Kruskal-Wallis (anova)), Q value = 0.0026

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	383	65.2 (10.7)
subtype1	117	66.7 (9.6)
subtype2	118	67.5 (9.8)
subtype3	41	63.0 (12.8)
subtype4	107	61.7 (10.9)

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

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.736 (Fisher's exact test), Q value = 0.87

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	3	13	35	28	39	54	3	79	61	40	35
subtype1	1	3	10	9	9	18	1	30	18	10	12
subtype2	2	6	14	8	14	19	0	17	20	13	7
subtype3	0	1	2	1	5	8	0	9	8	6	2
subtype4	0	3	9	10	11	9	2	23	15	11	14

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.211 (Fisher's exact test), Q value = 0.39

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	20	75	185	110
subtype1	5	28	60	28
subtype2	10	25	54	31
subtype3	1	3	22	16
subtype4	4	19	49	35

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.434 (Fisher's exact test), Q value = 0.6

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	123	100	78	84
subtype1	30	34	29	26
subtype2	47	23	24	25
subtype3	14	12	6	10
subtype4	32	31	19	23

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0768 (Fisher's exact test), Q value = 0.21

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	351	23
subtype1	106	5
subtype2	115	4
subtype3	38	2
subtype4	92	12

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.787 (Fisher's exact test), Q value = 0.88

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	136	254
subtype1	44	77
subtype2	44	76
subtype3	12	30
subtype4	36	71

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00043

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	66	134	72	77	20	8	13
subtype1	6	41	30	36	2	5	1
subtype2	15	42	22	26	9	2	4
subtype3	9	17	6	9	0	1	0
subtype4	36	34	14	6	9	0	8

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.461 (Fisher's exact test), Q value = 0.63

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	384
subtype1	4	117
subtype2	1	119
subtype3	0	42
subtype4	1	106

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'METHLYATION CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0831 (Fisher's exact test), Q value = 0.22

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2
ALL	333	17	12
subtype1	101	5	6
subtype2	108	3	1
subtype3	39	0	1
subtype4	85	9	4

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.181 (Kruskal-Wallis (anova)), Q value = 0.36

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	353	5.6 (8.4)
subtype1	111	6.7 (9.6)
subtype2	109	4.3 (5.9)
subtype3	39	5.9 (9.3)
subtype4	94	5.8 (9.1)

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.562 (Fisher's exact test), Q value = 0.71

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	89	12	1	249
subtype1	26	7	1	70
subtype2	26	3	0	79
subtype3	12	0	0	27
subtype4	25	2	0	73

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.529 (Fisher's exact test), Q value = 0.71

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	290
subtype1	0	83
subtype2	1	86
subtype3	1	35
subtype4	2	86

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S29. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	82	91	91

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.764 (logrank test), Q value = 0.88

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	232	76	0.1 - 105.1 (12.8)
subtype1	70	21	0.1 - 105.1 (11.4)
subtype2	84	29	0.1 - 65.1 (13.2)
subtype3	78	26	0.1 - 79.1 (13.2)

Figure S27. 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 = 0.0595 (Kruskal-Wallis (anova)), Q value = 0.18

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	257	65.8 (10.9)
subtype1	80	66.7 (10.6)
subtype2	91	63.7 (11.7)
subtype3	86	67.3 (10.0)

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.219 (Fisher's exact test), Q value = 0.4

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	6	25	28	25	36	3	42	34	24	23
subtype1	0	0	9	4	10	12	1	14	12	8	6
subtype2	0	2	3	12	10	14	1	15	11	11	9
subtype3	2	4	13	12	5	10	1	13	11	5	8

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00769 (Fisher's exact test), Q value = 0.047

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	8	67	105	74
subtype1	0	20	39	20
subtype2	2	16	37	33
subtype3	6	31	29	21

Figure S30. 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 = 0.874 (Fisher's exact test), Q value = 0.93

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	87	73	42	50
subtype1	27	22	14	15
subtype2	26	28	15	20
subtype3	34	23	13	15

Figure S31. 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 = 0.898 (Fisher's exact test), Q value = 0.93

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	237	16
subtype1	72	4
subtype2	84	6
subtype3	81	6

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.265 (Fisher's exact test), Q value = 0.45

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	99	165
subtype1	34	48
subtype2	37	54
subtype3	28	63

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00756 (Fisher's exact test), Q value = 0.047

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	42	135	37	26	16	4	2
subtype1	9	45	13	11	3	0	1
subtype2	19	43	9	4	12	1	1
subtype3	14	47	15	11	1	3	0

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.124 (Fisher's exact test), Q value = 0.3

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	258
subtype1	2	80
subtype2	0	91
subtype3	4	87

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.551 (Fisher's exact test), Q value = 0.71

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	206	9	8	25
subtype1	65	3	1	11
subtype2	72	4	2	7
subtype3	69	2	5	7

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.43 (Kruskal-Wallis (anova)), Q value = 0.6

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	224	4.7 (6.3)
subtype1	72	4.4 (6.4)
subtype2	80	4.6 (4.8)
subtype3	72	5.2 (7.6)

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.151 (Fisher's exact test), Q value = 0.34

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	60	2	159
subtype1	15	0	57
subtype2	23	0	57
subtype3	22	2	45

Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.763 (Fisher's exact test), Q value = 0.88

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	219
subtype1	0	72
subtype2	1	79
subtype3	1	68

Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S43. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	34	84	55	91

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0255 (logrank test), Q value = 0.13

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	232	76	0.1 - 105.1 (12.8)
subtype1	32	6	0.1 - 105.1 (15.2)
subtype2	72	24	0.1 - 79.1 (13.3)
subtype3	44	11	0.1 - 72.2 (11.8)
subtype4	84	35	0.1 - 65.1 (12.8)

Figure S40. 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 = 0.328 (Kruskal-Wallis (anova)), Q value = 0.51

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	257	65.8 (10.9)
subtype1	34	65.9 (11.4)
subtype2	79	66.4 (11.3)
subtype3	53	67.5 (10.0)
subtype4	91	64.4 (10.8)

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0079 (Fisher's exact test), Q value = 0.047

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	6	25	28	25	36	3	42	34	24	23
subtype1	0	1	7	4	3	6	0	3	5	3	0
subtype2	0	3	10	11	4	8	1	16	10	2	13
subtype3	2	2	4	3	7	4	1	11	8	5	3
subtype4	0	0	4	10	11	18	1	12	11	14	7

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00021 (Fisher's exact test), Q value = 0.003

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	8	67	105	74
subtype1	1	16	9	7
subtype2	3	27	27	23
subtype3	4	8	31	10
subtype4	0	16	38	34

Figure S43. 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 = 0.964 (Fisher's exact test), Q value = 0.97

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	87	73	42	50
subtype1	14	6	6	6
subtype2	27	24	13	15
subtype3	19	15	8	10
subtype4	27	28	15	19

Figure S44. 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 = 0.0556 (Fisher's exact test), Q value = 0.18

Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	237	16
subtype1	34	0
subtype2	70	10
subtype3	48	2
subtype4	85	4

Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.801 (Fisher's exact test), Q value = 0.88

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	99	165
subtype1	14	20
subtype2	28	56
subtype3	22	33
subtype4	35	56

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.227 (Fisher's exact test), Q value = 0.41

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	42	135	37	26	16	4	2
subtype1	4	14	6	7	2	1	0
subtype2	15	40	13	8	5	1	2
subtype3	8	32	7	7	0	1	0
subtype4	15	49	11	4	9	1	0

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.00653 (Fisher's exact test), Q value = 0.045

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	258
subtype1	0	34
subtype2	6	78
subtype3	0	55
subtype4	0	91

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0422 (Fisher's exact test), Q value = 0.15

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	206	9	8	25
subtype1	31	0	0	3
subtype2	60	2	7	7
subtype3	41	2	1	9
subtype4	74	5	0	6

Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.716 (Kruskal-Wallis (anova)), Q value = 0.85

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	224	4.7 (6.3)
subtype1	29	4.3 (6.3)
subtype2	66	5.2 (6.8)
subtype3	48	4.9 (8.0)
subtype4	81	4.5 (4.7)

Figure S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.182 (Fisher's exact test), Q value = 0.36

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	60	2	159
subtype1	7	0	20
subtype2	21	0	39
subtype3	14	2	34
subtype4	18	0	66

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.354 (Fisher's exact test), Q value = 0.54

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	219
subtype1	1	26
subtype2	0	60
subtype3	0	50
subtype4	1	83

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S57. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	66	63	46	37	62

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0325 (logrank test), Q value = 0.15

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	242	72	0.0 - 105.1 (12.8)
subtype1	61	10	0.1 - 105.1 (13.2)
subtype2	57	24	0.1 - 59.5 (13.4)
subtype3	38	13	0.0 - 69.0 (11.5)
subtype4	31	7	1.0 - 47.0 (13.6)
subtype5	55	18	0.1 - 72.2 (11.8)

Figure S53. 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 = 0.043 (Kruskal-Wallis (anova)), Q value = 0.15

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	267	65.7 (10.8)
subtype1	65	65.6 (11.1)
subtype2	63	62.3 (11.6)
subtype3	43	65.8 (10.6)
subtype4	36	69.9 (10.6)
subtype5	60	67.0 (9.0)

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0228 (Fisher's exact test), Q value = 0.12

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	10	27	23	29	44	3	42	31	23	25
subtype1	0	2	8	6	12	12	0	7	6	6	3
subtype2	0	0	1	5	7	9	1	10	12	10	5
subtype3	0	2	3	4	2	7	0	7	5	3	10
subtype4	2	3	8	1	3	7	1	5	3	0	3
subtype5	0	3	7	7	5	9	1	13	5	4	4

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00014 (Fisher's exact test), Q value = 0.0026

Table S61. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	13	70	107	75
subtype1	2	19	24	18
subtype2	0	5	30	25
subtype3	3	13	16	13
subtype4	6	15	8	8
subtype5	2	18	29	11

Figure S56. 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 = 0.0369 (Fisher's exact test), Q value = 0.15

Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	91	76	46	49
subtype1	30	17	8	9
subtype2	14	19	8	19
subtype3	10	15	11	8
subtype4	17	7	9	3
subtype5	20	18	10	10

Figure S57. 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 = 0.125 (Fisher's exact test), Q value = 0.3

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	243	18
subtype1	63	2
subtype2	58	3
subtype3	35	7
subtype4	34	2
subtype5	53	4

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.831 (Fisher's exact test), Q value = 0.91

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	103	171
subtype1	28	38
subtype2	23	40
subtype3	18	28
subtype4	14	23
subtype5	20	42

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00018 (Fisher's exact test), Q value = 0.0029

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	51	131	34	36	14	5	1
subtype1	12	42	3	8	0	1	0
subtype2	19	28	4	3	7	0	1
subtype3	10	19	6	7	3	1	0
subtype4	5	13	7	8	3	1	0
subtype5	5	29	14	10	1	2	0

Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.00829 (Fisher's exact test), Q value = 0.047

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	268
subtype1	0	66
subtype2	0	63
subtype3	4	42
subtype4	0	37
subtype5	2	60

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.55 (Fisher's exact test), Q value = 0.71

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	217	9	10	22
subtype1	55	1	1	5
subtype2	48	2	0	6
subtype3	35	2	5	4
subtype4	30	1	2	3
subtype5	49	3	2	4

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00544 (Kruskal-Wallis (anova)), Q value = 0.043

Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	238	4.8 (7.1)
subtype1	57	4.2 (8.5)
subtype2	54	5.4 (5.4)
subtype3	40	6.3 (7.9)
subtype4	35	4.1 (8.2)
subtype5	52	4.4 (5.2)

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.608 (Fisher's exact test), Q value = 0.75

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	73	4	156
subtype1	20	1	38
subtype2	19	0	43
subtype3	13	0	19
subtype4	6	1	21
subtype5	15	2	35

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.423 (Fisher's exact test), Q value = 0.6

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	231
subtype1	0	59
subtype2	1	61
subtype3	0	32
subtype4	1	27
subtype5	0	52

Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S71. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	38	21	50	62	42	40	21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.083 (logrank test), Q value = 0.22

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	242	72	0.0 - 105.1 (12.8)
subtype1	34	13	0.1 - 105.1 (12.0)
subtype2	20	4	0.3 - 24.7 (13.5)
subtype3	47	20	0.0 - 59.5 (13.5)
subtype4	52	16	0.1 - 69.0 (12.3)
subtype5	38	11	0.1 - 54.1 (12.4)
subtype6	34	8	0.1 - 72.2 (12.2)
subtype7	17	0	0.5 - 32.9 (15.8)

Figure S66. 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 = 0.000276 (Kruskal-Wallis (anova)), Q value = 0.0036

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	267	65.7 (10.8)
subtype1	38	65.2 (10.4)
subtype2	20	61.6 (11.5)
subtype3	50	59.7 (11.1)
subtype4	59	67.9 (9.8)
subtype5	41	70.2 (10.8)
subtype6	38	67.2 (9.3)
subtype7	21	67.6 (8.5)

Figure S67. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00496 (Fisher's exact test), Q value = 0.043

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	10	27	23	29	44	3	42	31	23	25
subtype1	0	0	2	6	2	9	1	2	4	1	7
subtype2	0	1	1	0	2	4	0	4	6	2	1
subtype3	0	0	2	5	7	8	1	8	8	7	3
subtype4	0	4	8	7	3	10	0	9	5	4	9
subtype5	2	3	7	1	9	5	1	5	2	1	5
subtype6	0	2	4	3	4	4	0	13	3	3	0
subtype7	0	0	3	1	2	4	0	1	3	5	0

Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0443 (Fisher's exact test), Q value = 0.15

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	13	70	107	75
subtype1	0	10	16	10
subtype2	1	2	11	7
subtype3	0	9	23	17
subtype4	6	21	18	15
subtype5	5	13	16	8
subtype6	1	11	18	8
subtype7	0	4	5	10

Figure S69. 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 = 0.00632 (Fisher's exact test), Q value = 0.045

Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	91	76	46	49
subtype1	10	17	4	4
subtype2	5	9	2	5
subtype3	13	12	7	16
subtype4	18	19	16	7
subtype5	22	6	9	4
subtype6	13	10	7	6
subtype7	10	3	1	7

Figure S70. 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 = 0.308 (Fisher's exact test), Q value = 0.49

Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	243	18
subtype1	34	3
subtype2	19	1
subtype3	45	4
subtype4	51	7
subtype5	37	3
subtype6	36	0
subtype7	21	0

Figure S71. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0253 (Fisher's exact test), Q value = 0.13

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	103	171
subtype1	16	22
subtype2	2	19
subtype3	21	29
subtype4	24	38
subtype5	19	23
subtype6	10	30
subtype7	11	10

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00043

Table S79. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	51	131	34	36	14	5	1
subtype1	8	19	6	1	2	0	0
subtype2	5	11	0	4	0	1	0
subtype3	22	17	3	1	6	0	1
subtype4	9	29	7	13	3	1	0
subtype5	4	16	10	10	1	1	0
subtype6	1	24	6	6	1	2	0
subtype7	2	15	2	1	1	0	0

Figure S73. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.148 (Fisher's exact test), Q value = 0.34

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	268
subtype1	0	38
subtype2	0	21
subtype3	0	50
subtype4	4	58
subtype5	0	42
subtype6	2	38
subtype7	0	21

Figure S74. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.759 (Fisher's exact test), Q value = 0.88

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	217	9	10	22
subtype1	28	1	1	5
subtype2	20	0	0	1
subtype3	37	3	1	3
subtype4	48	3	6	4
subtype5	34	1	2	2
subtype6	33	1	0	4
subtype7	17	0	0	3

Figure S75. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.366 (Kruskal-Wallis (anova)), Q value = 0.55

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	238	4.8 (7.1)
subtype1	30	4.9 (7.7)
subtype2	19	6.8 (11.6)
subtype3	44	5.5 (5.9)
subtype4	55	4.7 (6.6)
subtype5	38	4.4 (8.2)
subtype6	32	3.6 (4.0)
subtype7	20	4.7 (5.9)

Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.645 (Fisher's exact test), Q value = 0.78

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	73	4	156
subtype1	8	0	21
subtype2	7	0	14
subtype3	15	0	34
subtype4	19	1	25
subtype5	10	2	20
subtype6	10	1	26
subtype7	4	0	16

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.046 (Fisher's exact test), Q value = 0.15

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	231
subtype1	0	29
subtype2	1	20
subtype3	0	49
subtype4	0	45
subtype5	0	32
subtype6	0	37
subtype7	1	19

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S85. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	186	132	119

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.108 (logrank test), Q value = 0.27

Table S86. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	405	149	0.0 - 122.3 (12.8)
subtype1	167	58	0.0 - 122.3 (14.3)
subtype2	124	52	0.1 - 116.4 (12.4)
subtype3	114	39	0.1 - 72.2 (12.3)

Figure S79. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0056 (Kruskal-Wallis (anova)), Q value = 0.043

Table S87. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	430	65.7 (10.8)
subtype1	182	67.2 (10.5)
subtype2	132	63.2 (11.5)
subtype3	116	66.1 (9.8)

Figure S80. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.584 (Fisher's exact test), Q value = 0.73

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	3	14	41	32	39	55	4	85	62	41	45
subtype1	2	8	20	18	18	23	2	33	22	12	18
subtype2	0	1	9	8	12	16	1	24	22	18	17
subtype3	1	5	12	6	9	16	1	28	18	11	10

Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00065 (Fisher's exact test), Q value = 0.007

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	22	90	196	119
subtype1	13	47	85	35
subtype2	1	21	56	51
subtype3	8	22	55	33

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.183 (Fisher's exact test), Q value = 0.36

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	131	118	84	88
subtype1	60	55	37	26
subtype2	36	33	23	36
subtype3	35	30	24	26

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.403 (Fisher's exact test), Q value = 0.59

Table S91. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	388	30
subtype1	165	14
subtype2	116	11
subtype3	107	5

Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.858 (Fisher's exact test), Q value = 0.92

Table S92. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	158	279
subtype1	70	116
subtype2	46	86
subtype3	42	77

Figure S85. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00065

Table S93. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	71	163	80	78	22	8	13
subtype1	25	69	41	33	6	4	7
subtype2	39	52	15	11	10	1	3
subtype3	7	42	24	34	6	3	3

Figure S86. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.77 (Fisher's exact test), Q value = 0.88

Table S94. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	431
subtype1	3	183
subtype2	1	131
subtype3	2	117

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

'MIRSEQ CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.154 (Fisher's exact test), Q value = 0.34

Table S95. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	348	18	18	25
subtype1	142	6	9	17
subtype2	107	7	5	6
subtype3	99	5	4	2

Figure S88. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0761 (Kruskal-Wallis (anova)), Q value = 0.21

Table S96. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	387	5.5 (8.1)
subtype1	165	5.0 (8.0)
subtype2	118	5.7 (7.1)
subtype3	104	6.2 (9.1)

Figure S89. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.028 (Fisher's exact test), Q value = 0.13

Table S97. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	89	12	274
subtype1	29	3	115
subtype2	28	2	95
subtype3	32	7	64

Figure S90. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.839 (Fisher's exact test), Q value = 0.91

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	314
subtype1	2	111
subtype2	1	117
subtype3	1	86

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S99. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	79	174	79	105

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.18 (logrank test), Q value = 0.36

Table S100. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	405	149	0.0 - 122.3 (12.8)
subtype1	71	23	0.0 - 64.2 (12.0)
subtype2	160	54	0.1 - 122.3 (13.5)
subtype3	74	31	0.1 - 116.4 (12.8)
subtype4	100	41	0.1 - 79.1 (12.2)

Figure S92. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.0364 (Kruskal-Wallis (anova)), Q value = 0.15

Table S101. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	430	65.7 (10.8)
subtype1	78	68.0 (9.6)
subtype2	170	65.0 (11.3)
subtype3	79	63.6 (11.5)
subtype4	103	66.7 (9.7)

Figure S93. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0579 (Fisher's exact test), Q value = 0.18

Table S102. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	3	14	41	32	39	55	4	85	62	41	45
subtype1	2	3	12	6	11	10	2	9	8	4	6
subtype2	0	10	10	12	13	22	0	37	29	18	19
subtype3	0	0	8	4	9	7	0	16	15	9	8
subtype4	1	1	11	10	6	16	2	23	10	10	12

Figure S94. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.0403 (Fisher's exact test), Q value = 0.15

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	22	90	196	119
subtype1	8	22	31	15
subtype2	11	35	78	48
subtype3	0	12	37	27
subtype4	3	21	50	29

Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.17 (Fisher's exact test), Q value = 0.36

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	131	118	84	88
subtype1	32	21	12	9
subtype2	48	45	39	39
subtype3	23	22	10	21
subtype4	28	30	23	19

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.79 (Fisher's exact test), Q value = 0.88

Table S105. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	388	30
subtype1	70	6
subtype2	158	10
subtype3	69	5
subtype4	91	9

Figure S97. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.557 (Fisher's exact test), Q value = 0.71

Table S106. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	158	279
subtype1	33	46
subtype2	57	117
subtype3	30	49
subtype4	38	67

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 4e-04 (Fisher's exact test), Q value = 0.0047

Table S107. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	71	163	80	78	22	8	13
subtype1	10	34	16	13	3	1	2
subtype2	31	63	33	29	7	3	7
subtype3	24	32	6	7	7	0	2
subtype4	6	34	25	29	5	4	2

Figure S99. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.353 (Fisher's exact test), Q value = 0.54

Table S108. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	431
subtype1	0	79
subtype2	4	170
subtype3	0	79
subtype4	2	103

Figure S100. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.786 (Fisher's exact test), Q value = 0.88

Table S109. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	348	18	18	25
subtype1	60	2	3	8
subtype2	145	8	7	9
subtype3	59	4	3	5
subtype4	84	4	5	3

Figure S101. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0284 (Kruskal-Wallis (anova)), Q value = 0.13

Table S110. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	387	5.5 (8.1)
subtype1	68	3.8 (6.9)
subtype2	162	6.3 (8.6)
subtype3	70	5.3 (7.5)
subtype4	87	5.7 (8.3)

Figure S102. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.245 (Fisher's exact test), Q value = 0.42

Table S111. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	89	12	274
subtype1	18	3	47
subtype2	27	3	115
subtype3	20	1	55
subtype4	24	5	57

Figure S103. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

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

Table S112. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	314
subtype1	0	55
subtype2	2	113
subtype3	1	73
subtype4	1	73

Figure S104. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S113. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	75	55	61	58	36	58

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

P value = 0.104 (logrank test), Q value = 0.27

Table S114. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	331	121	0.1 - 122.3 (12.8)
subtype1	73	27	0.1 - 60.6 (11.5)
subtype2	55	17	0.3 - 122.3 (11.9)
subtype3	58	21	0.1 - 105.1 (13.8)
subtype4	54	16	0.1 - 72.2 (13.0)
subtype5	35	13	0.1 - 57.4 (14.8)
subtype6	56	27	0.1 - 59.5 (11.6)

Figure S105. 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 = 0.0393 (Kruskal-Wallis (anova)), Q value = 0.15

Table S115. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	337	65.2 (10.8)
subtype1	74	64.2 (11.0)
subtype2	55	66.4 (9.2)
subtype3	59	66.8 (11.4)
subtype4	55	68.3 (10.8)
subtype5	36	64.6 (9.6)
subtype6	58	61.3 (11.0)

Figure S106. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0771 (Fisher's exact test), Q value = 0.21

Table S116. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	3	7	31	29	33	47	3	72	52	34	32
subtype1	1	1	9	2	7	6	1	22	13	7	6
subtype2	0	3	4	2	2	10	0	11	12	6	5
subtype3	1	1	7	12	8	6	0	13	4	2	7
subtype4	1	2	4	7	7	10	1	9	6	8	3
subtype5	0	0	3	4	2	7	1	8	5	1	5
subtype6	0	0	4	2	7	8	0	9	12	10	6

Figure S107. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00418 (Fisher's exact test), Q value = 0.039

Table S117. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	14	70	165	94
subtype1	3	12	41	19
subtype2	6	10	24	15
subtype3	2	25	26	8
subtype4	3	9	28	18
subtype5	0	7	17	12
subtype6	0	7	29	22

Figure S108. 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 = 0.179 (Fisher's exact test), Q value = 0.36

Table S118. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	105	89	72	72
subtype1	21	19	18	16
subtype2	11	13	18	12
subtype3	23	17	12	9
subtype4	26	15	7	10
subtype5	9	13	5	8
subtype6	15	12	12	17

Figure S109. 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 = 0.958 (Fisher's exact test), Q value = 0.97

Table S119. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	309	22
subtype1	70	4
subtype2	51	3
subtype3	55	5
subtype4	50	3
subtype5	32	2
subtype6	51	5

Figure S110. 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 = 0.949 (Fisher's exact test), Q value = 0.97

Table S120. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	120	223
subtype1	27	48
subtype2	16	39
subtype3	22	39
subtype4	20	38
subtype5	13	23
subtype6	22	36

Figure S111. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 5e-05 (Fisher's exact test), Q value = 0.0013

Table S121. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	57	115	68	69	19	6	9
subtype1	2	24	20	24	2	2	1
subtype2	15	11	9	13	2	1	4
subtype3	6	23	17	10	3	2	0
subtype4	7	26	8	11	4	0	2
subtype5	7	13	7	5	3	1	0
subtype6	20	18	7	6	5	0	2

Figure S112. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.588 (Fisher's exact test), Q value = 0.73

Table S122. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	5	338
subtype1	1	74
subtype2	0	55
subtype3	1	60
subtype4	2	56
subtype5	1	35
subtype6	0	58

Figure S113. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.471 (Fisher's exact test), Q value = 0.64

Table S123. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2
ALL	294	14	10
subtype1	66	2	2
subtype2	45	3	1
subtype3	50	3	4
subtype4	54	1	0
subtype5	32	1	0
subtype6	47	4	3

Figure S114. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.18 (Kruskal-Wallis (anova)), Q value = 0.36

Table S124. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	309	5.5 (7.9)
subtype1	69	5.8 (8.9)
subtype2	51	6.9 (9.6)
subtype3	53	5.2 (6.9)
subtype4	52	3.9 (6.0)
subtype5	32	4.7 (6.4)
subtype6	52	6.2 (8.2)

Figure S115. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.532 (Fisher's exact test), Q value = 0.71

Table S125. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	68	11	227
subtype1	18	6	42
subtype2	12	1	38
subtype3	7	1	36
subtype4	11	2	40
subtype5	8	0	26
subtype6	12	1	45

Figure S116. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.0714 (Fisher's exact test), Q value = 0.21

Table S126. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	247
subtype1	0	46
subtype2	2	35
subtype3	0	44
subtype4	2	41
subtype5	0	27
subtype6	0	54

Figure S117. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S127. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	199	72	72

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

P value = 0.414 (logrank test), Q value = 0.6

Table S128. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	331	121	0.1 - 122.3 (12.8)
subtype1	189	71	0.1 - 122.3 (12.7)
subtype2	71	28	0.1 - 115.7 (12.8)
subtype3	71	22	0.1 - 79.1 (13.3)

Figure S118. 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 = 0.087 (Kruskal-Wallis (anova)), Q value = 0.23

Table S129. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	337	65.2 (10.8)
subtype1	193	65.9 (10.3)
subtype2	72	62.7 (11.6)
subtype3	72	66.0 (10.9)

Figure S119. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.694 (Fisher's exact test), Q value = 0.84

Table S130. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	3	7	31	29	33	47	3	72	52	34	32
subtype1	1	5	19	19	14	30	2	45	26	19	19
subtype2	0	0	5	5	9	7	0	14	14	9	9
subtype3	2	2	7	5	10	10	1	13	12	6	4

Figure S120. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.302 (Fisher's exact test), Q value = 0.49

Table S131. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	14	70	165	94
subtype1	9	43	97	50
subtype2	0	12	36	24
subtype3	5	15	32	20

Figure S121. 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 = 0.424 (Fisher's exact test), Q value = 0.6

Table S132. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	105	89	72	72
subtype1	61	53	42	41
subtype2	18	18	13	21
subtype3	26	18	17	10

Figure S122. 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 = 0.538 (Fisher's exact test), Q value = 0.71

Table S133. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	309	22
subtype1	178	13
subtype2	63	6
subtype3	68	3

Figure S123. 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 = 0.267 (Fisher's exact test), Q value = 0.45

Table S134. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	120	223
subtype1	63	136
subtype2	30	42
subtype3	27	45

Figure S124. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00043

Table S135. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	57	115	68	69	19	6	9
subtype1	25	81	40	38	8	2	5
subtype2	25	22	7	6	9	1	2
subtype3	7	12	21	25	2	3	2

Figure S125. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.229 (Fisher's exact test), Q value = 0.41

Table S136. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	5	338
subtype1	5	194
subtype2	0	72
subtype3	0	72

Figure S126. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.0594 (Fisher's exact test), Q value = 0.18

Table S137. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2
ALL	294	14	10
subtype1	170	8	4
subtype2	58	6	3
subtype3	66	0	3

Figure S127. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.147 (Kruskal-Wallis (anova)), Q value = 0.34

Table S138. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	309	5.5 (7.9)
subtype1	175	5.6 (7.6)
subtype2	65	6.1 (8.0)
subtype3	69	4.8 (8.7)

Figure S128. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0452 (Fisher's exact test), Q value = 0.15

Table S139. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	68	11	227
subtype1	32	6	134
subtype2	18	0	52
subtype3	18	5	41

Figure S129. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.19 (Fisher's exact test), Q value = 0.36

Table S140. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	247
subtype1	2	142
subtype2	0	64
subtype3	2	41

Figure S130. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/STAD-TP/15114286/STAD-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/STAD-TP/15090008/STAD-TP.merged_data.txt
Number of patients = 438
Number of clustering approaches = 10
Number of selected clinical features = 13
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)

Made with Nozzle