Correlation between aggregated molecular cancer subtypes and selected clinical features

Stomach Adenocarcinoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

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/C15Q4VD3

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 443 patients, 35 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 5 subtypes that correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death', 'PATHOLOGY_T_STAGE', and 'RADIATION_THERAPY'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY_T_STAGE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', and 'HISTOLOGICAL_TYPE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'RADIATION_THERAPY', 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 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 35 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	0.991 (1.00)	0.267 (0.475)	0.11 (0.298)	0.0465 (0.173)	0.00607 (0.0403)	0.0683 (0.222)	0.105 (0.297)	0.135 (0.326)	0.127 (0.312)	0.245 (0.456)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.367 (0.548)	9.69e-05 (0.0021)	0.319 (0.519)	0.541 (0.676)	0.035 (0.152)	0.000868 (0.00868)	0.00263 (0.023)	0.147 (0.342)	0.0289 (0.139)	0.0115 (0.0709)
PATHOLOGIC STAGE	Fisher's exact test	0.474 (0.616)	0.765 (0.867)	0.0141 (0.0835)	0.287 (0.498)	0.0466 (0.173)	0.298 (0.509)	0.234 (0.455)	0.431 (0.592)	0.0222 (0.122)	0.0541 (0.19)
PATHOLOGY T STAGE	Fisher's exact test	0.176 (0.388)	0.116 (0.302)	0.00015 (0.00279)	0.0226 (0.122)	0.00047 (0.00611)	0.00064 (0.00693)	5e-05 (0.0013)	0.0258 (0.129)	0.00271 (0.023)	0.0336 (0.15)
PATHOLOGY N STAGE	Fisher's exact test	0.241 (0.456)	0.474 (0.616)	0.459 (0.615)	0.419 (0.592)	0.0648 (0.217)	0.194 (0.388)	0.11 (0.298)	0.345 (0.534)	0.182 (0.388)	0.0513 (0.185)
PATHOLOGY M STAGE	Fisher's exact test	0.275 (0.482)	0.0736 (0.233)	0.127 (0.312)	0.327 (0.523)	0.126 (0.312)	0.95 (0.996)	0.247 (0.456)	0.785 (0.88)	0.986 (1.00)	0.373 (0.55)
GENDER	Fisher's exact test	0.0311 (0.145)	0.847 (0.926)	0.33 (0.523)	0.416 (0.592)	0.72 (0.836)	0.218 (0.43)	0.921 (0.976)	0.971 (1.00)	0.923 (0.976)	0.187 (0.388)
RADIATION THERAPY	Fisher's exact test	0.429 (0.592)	0.186 (0.388)	0.0439 (0.173)	0.0246 (0.128)	0.194 (0.388)	0.342 (0.534)	0.309 (0.515)	0.0442 (0.173)	0.0845 (0.256)	0.0033 (0.0238)
HISTOLOGICAL TYPE	Fisher's exact test	0.00301 (0.023)	1e-05 (0.000433)	0.00042 (0.00607)	0.161 (0.368)	0.00053 (0.00626)	0.0029 (0.023)	2e-05 (0.00065)	0.0002 (0.00325)	1e-05 (0.000433)	1e-05 (0.000433)
RESIDUAL TUMOR	Fisher's exact test	0.888 (0.962)	0.0773 (0.239)	0.923 (0.976)	0.578 (0.715)	0.532 (0.676)	0.491 (0.632)	0.0651 (0.217)	0.715 (0.836)	0.636 (0.758)	0.104 (0.297)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.143 (0.337)	0.257 (0.464)	0.358 (0.544)	0.472 (0.616)	0.00619 (0.0403)	0.456 (0.615)	0.0434 (0.173)	0.391 (0.571)	0.115 (0.302)	0.315 (0.518)
RACE	Fisher's exact test	0.583 (0.715)	0.62 (0.754)	0.431 (0.592)	0.432 (0.592)	0.767 (0.867)	0.683 (0.807)	0.091 (0.269)	0.36 (0.544)	0.249 (0.456)	0.177 (0.388)
ETHNICITY	Fisher's exact test	0.833 (0.918)	0.75 (0.863)	1 (1.00)	0.808 (0.897)	0.636 (0.758)	1 (1.00)	0.537 (0.676)	1 (1.00)	0.305 (0.514)	0.192 (0.388)

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	123	187	36	76	19

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

P value = 0.991 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	407	159	0.0 - 122.3 (14.0)
subtype1	116	44	0.6 - 72.2 (13.5)
subtype2	172	70	0.0 - 116.4 (15.5)
subtype3	34	16	0.1 - 79.1 (14.1)
subtype4	69	24	0.1 - 122.3 (12.8)
subtype5	16	5	0.8 - 29.0 (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 = 0.367 (Kruskal-Wallis (anova)), Q value = 0.55

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

	nPatients	Mean (Std.Dev)
ALL	432	65.8 (10.8)
subtype1	120	67.1 (10.0)
subtype2	185	64.7 (11.7)
subtype3	35	67.6 (10.1)
subtype4	74	64.9 (9.9)
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 'PATHOLOGIC_STAGE'

P value = 0.474 (Fisher's exact test), Q value = 0.62

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

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	15	41	32	40	57	4	83	64	39	46
subtype1	1	5	16	8	11	16	0	27	14	7	14
subtype2	1	4	19	12	18	26	2	32	28	17	20
subtype3	0	2	3	2	4	1	0	7	8	3	5
subtype4	0	2	2	8	6	13	2	15	11	10	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: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.176 (Fisher's exact test), Q value = 0.39

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

nPatients	T1	T2	T3	T4
ALL	23	93	196	119
subtype1	6	31	56	28
subtype2	7	36	81	58
subtype3	2	7	20	7
subtype4	5	12	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 = 0.241 (Fisher's exact test), Q value = 0.46

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

nPatients	N0	N1	N2	N3
ALL	130	120	86	88
subtype1	39	36	23	19
subtype2	58	52	33	37
subtype3	11	5	8	11
subtype4	18	24	19	13
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 = 0.275 (Fisher's exact test), Q value = 0.48

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

nPatients	0	1
ALL	389	30
subtype1	106	8
subtype2	162	16
subtype3	32	4
subtype4	71	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 = 0.0311 (Fisher's exact test), Q value = 0.14

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

nPatients	FEMALE	MALE
ALL	158	283
subtype1	37	86
subtype2	79	108
subtype3	7	29
subtype4	26	50
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 'RADIATION_THERAPY'

P value = 0.429 (Fisher's exact test), Q value = 0.59

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

nPatients	NO	YES
ALL	314	74
subtype1	86	23
subtype2	136	29
subtype3	27	3
subtype4	51	17
subtype5	14	2

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 = 0.00301 (Fisher's exact test), Q value = 0.023

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	72	162	83	79	13	21	8
subtype1	7	45	29	30	2	4	4
subtype2	42	74	28	20	9	12	2
subtype3	7	8	9	10	0	1	1
subtype4	13	29	14	14	2	3	0
subtype5	3	6	3	5	0	1	1

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 'RESIDUAL_TUMOR'

P value = 0.888 (Fisher's exact test), Q value = 0.96

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

nPatients	R0	R1	R2	RX
ALL	349	18	19	25
subtype1	95	4	8	5
subtype2	149	8	8	13
subtype3	29	2	2	1
subtype4	58	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: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.143 (Kruskal-Wallis (anova)), Q value = 0.34

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

	nPatients	Mean (Std.Dev)
ALL	391	5.6 (8.4)
subtype1	107	5.8 (9.9)
subtype2	170	4.7 (6.9)
subtype3	31	8.0 (9.6)
subtype4	65	5.8 (8.1)
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 = 0.583 (Fisher's exact test), Q value = 0.71

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	12	1	278
subtype1	26	5	1	71
subtype2	34	3	0	129
subtype3	6	2	0	18
subtype4	18	2	0	51
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 = 0.833 (Fisher's exact test), Q value = 0.92

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	316
subtype1	2	81
subtype2	3	143
subtype3	0	21
subtype4	0	59
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	124	122	42	107

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.267 (logrank test), Q value = 0.48

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	382	146	0.1 - 122.3 (14.0)
subtype1	120	49	0.1 - 79.1 (13.4)
subtype2	117	37	0.1 - 73.4 (14.3)
subtype3	42	14	0.1 - 60.9 (16.1)
subtype4	103	46	0.1 - 122.3 (13.3)

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 = 9.69e-05 (Kruskal-Wallis (anova)), Q value = 0.0021

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

	nPatients	Mean (Std.Dev)
ALL	386	65.2 (10.7)
subtype1	119	66.8 (9.6)
subtype2	119	67.6 (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 'PATHOLOGIC_STAGE'

P value = 0.765 (Fisher's exact test), Q value = 0.87

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

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	35	29	41	56	3	77	63	38	35
subtype1	1	3	11	10	11	19	1	28	19	9	12
subtype2	2	6	14	8	14	20	0	17	21	12	7
subtype3	0	1	2	1	5	8	0	9	8	6	2
subtype4	0	4	8	10	11	9	2	23	15	11	14

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.116 (Fisher's exact test), Q value = 0.3

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

nPatients	T1	T2	T3	T4
ALL	21	78	186	110
subtype1	5	31	60	28
subtype2	11	25	55	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 = 0.474 (Fisher's exact test), Q value = 0.62

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

nPatients	N0	N1	N2	N3
ALL	124	102	80	83
subtype1	31	35	30	25
subtype2	47	24	25	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 = 0.0736 (Fisher's exact test), Q value = 0.23

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

nPatients	0	1
ALL	353	23
subtype1	107	5
subtype2	116	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 = 0.847 (Fisher's exact test), Q value = 0.93

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

nPatients	FEMALE	MALE
ALL	136	259
subtype1	44	80
subtype2	44	78
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 'RADIATION_THERAPY'

P value = 0.186 (Fisher's exact test), Q value = 0.39

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

nPatients	NO	YES
ALL	300	73
subtype1	92	26
subtype2	91	26
subtype3	36	3
subtype4	81	18

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

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

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	67	134	74	78	13	20	8
subtype1	7	41	30	37	1	2	5
subtype2	15	43	23	26	4	9	2
subtype3	8	17	7	9	0	0	1
subtype4	37	33	14	6	8	9	0

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

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0773 (Fisher's exact test), Q value = 0.24

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

nPatients	R0	R1	R2
ALL	335	17	12
subtype1	102	5	6
subtype2	109	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: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.257 (Kruskal-Wallis (anova)), Q value = 0.46

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

	nPatients	Mean (Std.Dev)
ALL	357	5.6 (8.4)
subtype1	113	6.6 (9.5)
subtype2	111	4.3 (5.8)
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 = 0.62 (Fisher's exact test), Q value = 0.75

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	13	1	253
subtype1	26	7	1	73
subtype2	26	4	0	80
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 = 0.75 (Fisher's exact test), Q value = 0.86

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	293
subtype1	1	84
subtype2	1	88
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	4	5
Number of samples	30	92	82	94	59

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.11 (logrank test), Q value = 0.3

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	323	129	0.1 - 122.3 (14.0)
subtype1	29	11	3.0 - 79.1 (15.9)
subtype2	86	30	0.6 - 122.3 (15.8)
subtype3	72	29	0.1 - 72.2 (16.2)
subtype4	86	39	0.1 - 43.4 (12.2)
subtype5	50	20	0.3 - 37.2 (14.0)

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 = 0.319 (Kruskal-Wallis (anova)), Q value = 0.52

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

	nPatients	Mean (Std.Dev)
ALL	349	65.7 (11.0)
subtype1	28	66.8 (8.8)
subtype2	92	67.2 (10.3)
subtype3	78	65.9 (11.9)
subtype4	94	63.8 (11.5)
subtype5	57	65.2 (10.9)

Figure S28. 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 = 0.0141 (Fisher's exact test), Q value = 0.083

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

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	10	32	29	32	44	3	69	52	32	35
subtype1	0	2	4	3	3	2	0	4	3	1	6
subtype2	0	1	13	8	10	12	1	16	16	6	5
subtype3	3	4	9	3	2	9	1	17	12	13	5
subtype4	0	3	2	8	10	13	1	17	11	11	15
subtype5	0	0	4	7	7	8	0	15	10	1	4

Figure S29. 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 = 0.00015 (Fisher's exact test), Q value = 0.0028

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

nPatients	T1	T2	T3	T4
ALL	14	76	156	101
subtype1	2	9	11	6
subtype2	1	27	42	20
subtype3	8	18	27	28
subtype4	3	8	44	36
subtype5	0	14	32	11

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 = 0.459 (Fisher's exact test), Q value = 0.61

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

nPatients	N0	N1	N2	N3
ALL	109	97	65	71
subtype1	13	8	4	4
subtype2	33	21	17	17
subtype3	19	21	19	18
subtype4	25	28	13	24
subtype5	19	19	12	8

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 = 0.127 (Fisher's exact test), Q value = 0.31

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

nPatients	0	1
ALL	322	21
subtype1	24	4
subtype2	88	2
subtype3	75	4
subtype4	84	8
subtype5	51	3

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 = 0.33 (Fisher's exact test), Q value = 0.52

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

nPatients	FEMALE	MALE
ALL	121	236
subtype1	6	24
subtype2	35	57
subtype3	24	58
subtype4	35	59
subtype5	21	38

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0439 (Fisher's exact test), Q value = 0.17

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

nPatients	NO	YES
ALL	257	55
subtype1	21	4
subtype2	65	18
subtype3	53	18
subtype4	78	7
subtype5	40	8

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00042 (Fisher's exact test), Q value = 0.0061

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	52	149	69	50	10	19	5
subtype1	2	9	12	2	3	1	1
subtype2	9	34	25	17	2	4	1
subtype3	9	34	21	13	0	3	1
subtype4	20	42	7	10	4	8	1
subtype5	12	30	4	8	1	3	1

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

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.923 (Fisher's exact test), Q value = 0.98

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

nPatients	R0	R1	R2	RX
ALL	282	15	14	25
subtype1	23	1	3	2
subtype2	77	3	2	7
subtype3	63	3	4	4
subtype4	73	5	4	7
subtype5	46	3	1	5

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

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.358 (Kruskal-Wallis (anova)), Q value = 0.54

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

	nPatients	Mean (Std.Dev)
ALL	313	5.5 (7.9)
subtype1	28	4.1 (5.7)
subtype2	80	5.9 (9.8)
subtype3	70	5.7 (6.2)
subtype4	84	6.2 (9.3)
subtype5	51	4.2 (4.8)

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 = 0.431 (Fisher's exact test), Q value = 0.59

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	66	5	1	232
subtype1	3	0	0	18
subtype2	14	2	1	61
subtype3	16	3	0	46
subtype4	18	0	0	68
subtype5	15	0	0	39

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

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

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	255
subtype1	0	13
subtype2	1	62
subtype3	0	52
subtype4	1	75
subtype5	0	53

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	140	58	79	80

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0465 (logrank test), Q value = 0.17

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	323	129	0.1 - 122.3 (14.0)
subtype1	125	55	0.1 - 79.1 (14.3)
subtype2	48	14	0.1 - 55.6 (14.5)
subtype3	75	26	0.6 - 122.3 (16.4)
subtype4	75	34	0.1 - 40.2 (12.6)

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 = 0.541 (Kruskal-Wallis (anova)), Q value = 0.68

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

	nPatients	Mean (Std.Dev)
ALL	349	65.7 (11.0)
subtype1	132	66.0 (11.3)
subtype2	58	67.0 (10.3)
subtype3	79	65.1 (11.0)
subtype4	80	64.7 (11.0)

Figure S41. 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 = 0.287 (Fisher's exact test), Q value = 0.5

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

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	10	32	29	32	44	3	69	52	32	35
subtype1	2	5	13	13	8	15	1	32	18	11	16
subtype2	1	1	3	3	7	5	1	13	8	7	4
subtype3	0	3	14	7	7	12	0	11	14	5	4
subtype4	0	1	2	6	10	12	1	13	12	9	11

Figure S42. 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 = 0.0226 (Fisher's exact test), Q value = 0.12

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

nPatients	T1	T2	T3	T4
ALL	14	76	156	101
subtype1	7	33	58	38
subtype2	3	6	29	18
subtype3	3	27	31	17
subtype4	1	10	38	28

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 = 0.419 (Fisher's exact test), Q value = 0.59

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

nPatients	N0	N1	N2	N3
ALL	109	97	65	71
subtype1	39	45	23	27
subtype2	19	15	12	9
subtype3	29	14	18	15
subtype4	22	23	12	20

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 = 0.327 (Fisher's exact test), Q value = 0.52

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

nPatients	0	1
ALL	322	21
subtype1	123	11
subtype2	51	2
subtype3	75	2
subtype4	73	6

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 = 0.416 (Fisher's exact test), Q value = 0.59

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

nPatients	FEMALE	MALE
ALL	121	236
subtype1	42	98
subtype2	23	35
subtype3	25	54
subtype4	31	49

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

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0246 (Fisher's exact test), Q value = 0.13

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

nPatients	NO	YES
ALL	257	55
subtype1	93	27
subtype2	36	9
subtype3	59	14
subtype4	69	5

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.161 (Fisher's exact test), Q value = 0.37

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	52	149	69	50	10	19	5
subtype1	18	59	30	16	6	7	2
subtype2	7	26	13	8	1	2	1
subtype3	11	25	20	17	2	3	1
subtype4	16	39	6	9	1	7	1

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

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.578 (Fisher's exact test), Q value = 0.71

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

nPatients	R0	R1	R2	RX
ALL	282	15	14	25
subtype1	106	6	8	8
subtype2	45	2	1	8
subtype3	68	2	2	4
subtype4	63	5	3	5

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

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.472 (Kruskal-Wallis (anova)), Q value = 0.62

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

	nPatients	Mean (Std.Dev)
ALL	313	5.5 (7.9)
subtype1	119	5.8 (7.9)
subtype2	52	4.8 (7.6)
subtype3	70	5.2 (8.3)
subtype4	72	5.8 (8.0)

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 = 0.432 (Fisher's exact test), Q value = 0.59

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	66	5	1	232
subtype1	30	2	0	79
subtype2	11	1	0	40
subtype3	12	2	1	52
subtype4	13	0	0	61

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.808 (Fisher's exact test), Q value = 0.9

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	255
subtype1	1	93
subtype2	0	41
subtype3	1	54
subtype4	0	67

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	63	63	44	40	64

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00607 (logrank test), Q value = 0.04

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	242	81	0.0 - 105.1 (13.8)
subtype1	58	10	0.3 - 105.1 (16.5)
subtype2	58	29	0.1 - 59.5 (14.1)
subtype3	37	15	0.0 - 69.0 (12.8)
subtype4	33	8	1.0 - 47.0 (14.1)
subtype5	56	19	0.1 - 72.2 (12.5)

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 = 0.035 (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	62	65.3 (11.2)
subtype2	63	62.5 (11.5)
subtype3	41	65.4 (10.2)
subtype4	39	69.9 (10.3)
subtype5	62	67.1 (9.5)

Figure S54. 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 = 0.0466 (Fisher's exact test), Q value = 0.17

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

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	30	23	25
subtype1	0	2	8	6	9	12	0	7	6	6	3
subtype2	0	0	1	5	7	9	1	11	11	10	5
subtype3	0	2	3	4	3	6	0	6	4	3	10
subtype4	2	3	8	1	5	8	1	4	3	0	3
subtype5	0	3	7	7	5	9	1	14	6	4	4

Figure S55. 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 = 0.00047 (Fisher's exact test), Q value = 0.0061

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	21	18
subtype2	0	5	30	25
subtype3	3	13	15	12
subtype4	6	15	11	8
subtype5	2	18	30	12

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 = 0.0648 (Fisher's exact test), Q value = 0.22

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	27	17	8	9
subtype2	14	19	8	19
subtype3	11	12	11	8
subtype4	19	8	9	3
subtype5	20	20	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 = 0.126 (Fisher's exact test), Q value = 0.31

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

nPatients	0	1
ALL	243	18
subtype1	60	2
subtype2	58	3
subtype3	34	7
subtype4	37	2
subtype5	54	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 = 0.72 (Fisher's exact test), Q value = 0.84

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

nPatients	FEMALE	MALE
ALL	103	171
subtype1	27	36
subtype2	23	40
subtype3	18	26
subtype4	15	25
subtype5	20	44

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.194 (Fisher's exact test), Q value = 0.39

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

nPatients	NO	YES
ALL	203	26
subtype1	50	5
subtype2	55	3
subtype3	26	7
subtype4	28	4
subtype5	44	7

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00053 (Fisher's exact test), Q value = 0.0063

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	51	130	34	36	1	14	5
subtype1	11	40	3	8	0	0	1
subtype2	19	28	4	3	1	7	0
subtype3	9	18	6	7	0	3	1
subtype4	7	14	7	8	0	3	1
subtype5	5	30	14	10	0	1	2

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

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.532 (Fisher's exact test), Q value = 0.68

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

nPatients	R0	R1	R2	RX
ALL	217	9	10	22
subtype1	53	1	1	5
subtype2	48	2	0	6
subtype3	33	2	5	4
subtype4	32	1	2	3
subtype5	51	3	2	4

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

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.00619 (Kruskal-Wallis (anova)), Q value = 0.04

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	55	4.3 (8.6)
subtype2	54	5.4 (5.4)
subtype3	38	6.5 (8.0)
subtype4	37	3.9 (8.0)
subtype5	54	4.2 (5.1)

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 = 0.767 (Fisher's exact test), Q value = 0.87

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	19	1	36
subtype2	18	0	44
subtype3	12	0	18
subtype4	8	1	22
subtype5	16	2	36

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.636 (Fisher's exact test), Q value = 0.76

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	56
subtype2	1	61
subtype3	0	30
subtype4	1	30
subtype5	0	54

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
Number of samples	110	58	106

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0683 (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	81	0.0 - 105.1 (13.8)
subtype1	98	32	0.1 - 105.1 (13.1)
subtype2	53	25	0.0 - 59.5 (14.0)
subtype3	91	24	0.8 - 69.0 (14.3)

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 = 0.000868 (Kruskal-Wallis (anova)), Q value = 0.0087

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	107	65.6 (10.4)
subtype2	58	61.3 (11.0)
subtype3	102	68.4 (10.3)

Figure S67. 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 = 0.298 (Fisher's exact test), Q value = 0.51

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

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	30	23	25
subtype1	0	3	9	9	9	19	2	21	15	6	10
subtype2	0	0	3	5	9	10	0	9	8	8	4
subtype3	2	7	15	9	11	15	1	12	7	9	11

Figure S68. 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 = 0.00064 (Fisher's exact test), Q value = 0.0069

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	2	25	53	27
subtype2	0	10	25	21
subtype3	11	35	29	27

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 = 0.194 (Fisher's exact test), Q value = 0.39

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	32	36	17	18
subtype2	18	15	7	16
subtype3	41	25	22	15

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 = 0.95 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	243	18
subtype1	95	7
subtype2	54	3
subtype3	94	8

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 = 0.218 (Fisher's exact test), Q value = 0.43

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

nPatients	FEMALE	MALE
ALL	103	171
subtype1	35	75
subtype2	22	36
subtype3	46	60

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

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.342 (Fisher's exact test), Q value = 0.53

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

nPatients	NO	YES
ALL	203	26
subtype1	81	9
subtype2	50	4
subtype3	72	13

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0029 (Fisher's exact test), Q value = 0.023

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	51	130	34	36	1	14	5
subtype1	16	57	16	13	0	4	3
subtype2	20	24	4	2	1	6	0
subtype3	15	49	14	21	0	4	2

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

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.491 (Fisher's exact test), Q value = 0.63

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

nPatients	R0	R1	R2	RX
ALL	217	9	10	22
subtype1	91	2	2	9
subtype2	44	3	1	4
subtype3	82	4	7	9

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

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.456 (Kruskal-Wallis (anova)), Q value = 0.61

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	92	5.3 (8.2)
subtype2	51	4.8 (5.5)
subtype3	95	4.4 (6.6)

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 = 0.683 (Fisher's exact test), Q value = 0.81

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	28	2	66
subtype2	16	0	40
subtype3	29	2	50

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

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

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	1	95
subtype2	0	56
subtype3	1	80

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	174	130	132

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.105 (logrank test), Q value = 0.3

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	402	157	0.0 - 122.3 (14.0)
subtype1	153	57	0.0 - 122.3 (16.1)
subtype2	122	57	0.1 - 116.4 (12.9)
subtype3	127	43	0.1 - 72.2 (12.8)

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 = 0.00263 (Kruskal-Wallis (anova)), Q value = 0.023

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

	nPatients	Mean (Std.Dev)
ALL	427	65.7 (10.8)
subtype1	168	67.5 (10.2)
subtype2	130	63.1 (11.6)
subtype3	129	66.1 (10.2)

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

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.234 (Fisher's exact test), Q value = 0.45

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

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	15	40	32	41	56	4	82	62	39	45
subtype1	2	7	20	16	17	22	2	28	23	9	17
subtype2	0	0	9	8	11	16	1	25	21	17	18
subtype3	1	8	11	8	13	18	1	29	18	13	10

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

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

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

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

nPatients	T1	T2	T3	T4
ALL	23	92	193	118
subtype1	12	46	79	31
subtype2	0	21	55	51
subtype3	11	25	59	36

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 = 0.11 (Fisher's exact test), Q value = 0.3

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

nPatients	N0	N1	N2	N3
ALL	129	118	85	87
subtype1	56	52	34	24
subtype2	34	33	22	37
subtype3	39	33	29	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 = 0.247 (Fisher's exact test), Q value = 0.46

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

nPatients	0	1
ALL	384	30
subtype1	151	14
subtype2	114	11
subtype3	119	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 = 0.921 (Fisher's exact test), Q value = 0.98

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

nPatients	FEMALE	MALE
ALL	155	281
subtype1	64	110
subtype2	45	85
subtype3	46	86

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.309 (Fisher's exact test), Q value = 0.52

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

nPatients	NO	YES
ALL	311	72
subtype1	113	29
subtype2	102	17
subtype3	96	26

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

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

Table S94. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	72	163	80	77	11	22	8
subtype1	24	69	37	29	4	6	4
subtype2	39	47	16	12	4	10	1
subtype3	9	47	27	36	3	6	3

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

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0651 (Fisher's exact test), Q value = 0.22

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

nPatients	R0	R1	R2	RX
ALL	344	18	18	25
subtype1	128	6	9	17
subtype2	106	6	5	6
subtype3	110	6	4	2

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

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0434 (Kruskal-Wallis (anova)), Q value = 0.17

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

	nPatients	Mean (Std.Dev)
ALL	385	5.6 (8.1)
subtype1	154	4.7 (7.1)
subtype2	116	6.3 (8.4)
subtype3	115	6.0 (8.9)

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 = 0.091 (Fisher's exact test), Q value = 0.27

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	88	13	273
subtype1	26	4	106
subtype2	28	2	93
subtype3	34	7	74

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.537 (Fisher's exact test), Q value = 0.68

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	317
subtype1	3	109
subtype2	1	114
subtype3	1	94

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
Number of samples	245	82	109

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.135 (logrank test), Q value = 0.33

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	402	157	0.0 - 122.3 (14.0)
subtype1	220	78	0.0 - 122.3 (14.7)
subtype2	78	35	0.1 - 116.4 (13.9)
subtype3	104	44	0.1 - 79.1 (12.8)

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

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

	nPatients	Mean (Std.Dev)
ALL	427	65.7 (10.8)
subtype1	238	66.1 (10.7)
subtype2	82	63.6 (11.9)
subtype3	107	66.5 (9.8)

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.431 (Fisher's exact test), Q value = 0.59

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

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	15	40	32	41	56	4	82	62	39	45
subtype1	2	14	23	15	25	32	2	43	36	19	23
subtype2	0	0	7	6	9	7	0	17	15	9	9
subtype3	1	1	10	11	7	17	2	22	11	11	13

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

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.0258 (Fisher's exact test), Q value = 0.13

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

nPatients	T1	T2	T3	T4
ALL	23	92	193	118
subtype1	20	56	104	60
subtype2	0	13	38	28
subtype3	3	23	51	30

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 = 0.345 (Fisher's exact test), Q value = 0.53

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

nPatients	N0	N1	N2	N3
ALL	129	118	85	87
subtype1	80	63	51	43
subtype2	22	24	11	22
subtype3	27	31	23	22

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 = 0.785 (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	384	30
subtype1	219	16
subtype2	72	5
subtype3	93	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 = 0.971 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	155	281
subtype1	86	159
subtype2	30	52
subtype3	39	70

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.0442 (Fisher's exact test), Q value = 0.17

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

nPatients	NO	YES
ALL	311	72
subtype1	165	47
subtype2	68	7
subtype3	78	18

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 2e-04 (Fisher's exact test), Q value = 0.0032

Table S108. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	72	163	80	77	11	22	8
subtype1	39	97	47	40	7	10	3
subtype2	25	31	7	8	2	7	1
subtype3	8	35	26	29	2	5	4

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

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.715 (Fisher's exact test), Q value = 0.84

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

nPatients	R0	R1	R2	RX
ALL	344	18	18	25
subtype1	196	9	10	17
subtype2	61	5	3	5
subtype3	87	4	5	3

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.391 (Kruskal-Wallis (anova)), Q value = 0.57

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

	nPatients	Mean (Std.Dev)
ALL	385	5.6 (8.1)
subtype1	222	5.4 (8.0)
subtype2	73	5.5 (7.9)
subtype3	90	6.0 (8.4)

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 = 0.36 (Fisher's exact test), Q value = 0.54

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	88	13	273
subtype1	43	7	157
subtype2	21	1	56
subtype3	24	5	60

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	5	317
subtype1	3	167
subtype2	1	74
subtype3	1	76

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	74	57	60	60	33	59

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

P value = 0.127 (logrank test), Q value = 0.31

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	331	131	0.1 - 122.3 (14.0)
subtype1	72	28	0.6 - 66.8 (12.9)
subtype2	56	17	0.3 - 122.3 (14.0)
subtype3	58	24	0.1 - 105.1 (13.6)
subtype4	55	18	0.1 - 72.2 (18.2)
subtype5	32	14	0.1 - 57.4 (17.0)
subtype6	58	30	0.1 - 59.5 (12.6)

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 = 0.0289 (Kruskal-Wallis (anova)), Q value = 0.14

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

	nPatients	Mean (Std.Dev)
ALL	335	65.2 (10.8)
subtype1	72	64.2 (10.8)
subtype2	56	66.9 (9.9)
subtype3	58	66.1 (11.4)
subtype4	57	68.3 (10.8)
subtype5	33	64.8 (9.5)
subtype6	59	61.3 (10.8)

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 'PATHOLOGIC_STAGE'

P value = 0.0222 (Fisher's exact test), Q value = 0.12

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

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	8	30	29	35	48	3	69	53	32	32
subtype1	1	1	10	2	7	7	1	19	13	7	6
subtype2	0	4	4	0	4	11	0	10	13	5	6
subtype3	1	1	6	13	7	6	0	14	4	2	6
subtype4	1	2	3	8	8	11	1	9	6	7	3
subtype5	0	0	3	4	2	6	1	6	5	1	5
subtype6	0	0	4	2	7	7	0	11	12	10	6

Figure S107. 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 = 0.00271 (Fisher's exact test), Q value = 0.023

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

nPatients	T1	T2	T3	T4
ALL	15	72	163	93
subtype1	3	14	38	19
subtype2	7	10	26	14
subtype3	2	25	26	7
subtype4	3	9	27	21
subtype5	0	6	16	11
subtype6	0	8	30	21

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 = 0.182 (Fisher's exact test), Q value = 0.39

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

nPatients	N0	N1	N2	N3
ALL	103	89	74	71
subtype1	21	19	16	16
subtype2	11	13	20	12
subtype3	22	17	11	10
subtype4	26	17	8	9
subtype5	9	11	6	6
subtype6	14	12	13	18

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 = 0.986 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	306	22
subtype1	67	4
subtype2	51	4
subtype3	55	4
subtype4	52	3
subtype5	29	2
subtype6	52	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 = 0.923 (Fisher's exact test), Q value = 0.98

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

nPatients	FEMALE	MALE
ALL	118	225
subtype1	26	48
subtype2	16	41
subtype3	21	39
subtype4	22	38
subtype5	11	22
subtype6	22	37

Figure S111. 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 = 0.0845 (Fisher's exact test), Q value = 0.26

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

nPatients	NO	YES
ALL	261	63
subtype1	52	18
subtype2	43	12
subtype3	44	6
subtype4	46	11
subtype5	23	10
subtype6	53	6

Figure S112. 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 = 1e-05 (Fisher's exact test), Q value = 0.00043

Table S122. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	58	116	68	69	7	19	6
subtype1	2	24	19	26	0	1	2
subtype2	14	13	9	14	3	3	1
subtype3	7	23	17	8	0	3	2
subtype4	7	27	9	11	2	4	0
subtype5	6	11	8	4	0	3	1
subtype6	22	18	6	6	2	5	0

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

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.636 (Fisher's exact test), Q value = 0.76

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

nPatients	R0	R1	R2
ALL	291	14	10
subtype1	63	2	2
subtype2	45	3	2
subtype3	50	3	3
subtype4	56	1	0
subtype5	29	1	0
subtype6	48	4	3

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

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.115 (Kruskal-Wallis (anova)), Q value = 0.3

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

	nPatients	Mean (Std.Dev)
ALL	308	5.6 (7.9)
subtype1	67	5.7 (9.1)
subtype2	52	6.7 (9.5)
subtype3	53	5.4 (6.9)
subtype4	54	3.8 (6.0)
subtype5	29	4.8 (6.6)
subtype6	53	6.5 (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 = 0.249 (Fisher's exact test), Q value = 0.46

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	67	12	227
subtype1	18	6	41
subtype2	13	2	38
subtype3	6	1	36
subtype4	11	3	41
subtype5	7	0	24
subtype6	12	0	47

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 = 0.305 (Fisher's exact test), Q value = 0.51

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	250
subtype1	1	44
subtype2	2	40
subtype3	0	43
subtype4	2	42
subtype5	0	25
subtype6	0	56

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	4	5	6
Number of samples	85	57	36	76	28	61

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

P value = 0.245 (logrank test), Q value = 0.46

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	331	131	0.1 - 122.3 (14.0)
subtype1	82	29	0.9 - 79.1 (15.3)
subtype2	56	27	0.1 - 115.7 (15.0)
subtype3	35	12	0.1 - 105.1 (13.9)
subtype4	72	26	0.8 - 122.3 (15.7)
subtype5	26	11	0.3 - 37.2 (13.9)
subtype6	60	26	0.1 - 63.6 (12.3)

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 = 0.0115 (Kruskal-Wallis (anova)), Q value = 0.071

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

	nPatients	Mean (Std.Dev)
ALL	335	65.2 (10.8)
subtype1	83	67.1 (10.9)
subtype2	56	60.4 (10.6)
subtype3	35	67.0 (12.7)
subtype4	73	65.5 (9.8)
subtype5	28	64.5 (10.8)
subtype6	60	66.1 (9.8)

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 'PATHOLOGIC_STAGE'

P value = 0.0541 (Fisher's exact test), Q value = 0.19

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

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	8	30	29	35	48	3	69	53	32	32
subtype1	2	3	8	9	12	10	1	17	11	8	4
subtype2	0	0	4	2	7	5	0	11	13	8	7
subtype3	0	1	3	8	6	3	0	9	1	0	5
subtype4	1	4	6	4	3	15	1	12	13	7	9
subtype5	0	0	2	1	1	5	0	4	8	6	1
subtype6	0	0	7	5	6	10	1	16	7	3	6

Figure S120. 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 = 0.0336 (Fisher's exact test), Q value = 0.15

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

nPatients	T1	T2	T3	T4
ALL	15	72	163	93
subtype1	6	20	39	20
subtype2	0	9	27	21
subtype3	1	12	18	5
subtype4	7	16	35	18
subtype5	0	2	12	14
subtype6	1	13	32	15

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 = 0.0513 (Fisher's exact test), Q value = 0.19

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

nPatients	N0	N1	N2	N3
ALL	103	89	74	71
subtype1	33	17	20	14
subtype2	12	13	12	18
subtype3	15	11	7	3
subtype4	19	20	19	17
subtype5	8	6	3	11
subtype6	16	22	13	8

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 = 0.373 (Fisher's exact test), Q value = 0.55

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

nPatients	0	1
ALL	306	22
subtype1	81	2
subtype2	50	4
subtype3	32	4
subtype4	65	6
subtype5	26	1
subtype6	52	5

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 = 0.187 (Fisher's exact test), Q value = 0.39

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

nPatients	FEMALE	MALE
ALL	118	225
subtype1	32	53
subtype2	24	33
subtype3	12	24
subtype4	17	59
subtype5	11	17
subtype6	22	39

Figure S124. 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 = 0.0033 (Fisher's exact test), Q value = 0.024

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

nPatients	NO	YES
ALL	261	63
subtype1	62	19
subtype2	47	10
subtype3	27	0
subtype4	54	20
subtype5	26	1
subtype6	45	13

Figure S125. 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 = 0.00043

Table S136. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: '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 ADENOCARCINOMA SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE
ALL	58	116	68	69	7	19	6
subtype1	9	19	20	30	1	2	4
subtype2	24	17	5	3	2	5	1
subtype3	3	17	11	2	0	3	0
subtype4	15	26	13	12	4	5	1
subtype5	4	13	4	7	0	0	0
subtype6	3	24	15	15	0	4	0

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

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.104 (Fisher's exact test), Q value = 0.3

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

nPatients	R0	R1	R2
ALL	291	14	10
subtype1	78	0	2
subtype2	47	4	2
subtype3	27	2	3
subtype4	65	5	1
subtype5	26	0	1
subtype6	48	3	1

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

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.315 (Kruskal-Wallis (anova)), Q value = 0.52

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

	nPatients	Mean (Std.Dev)
ALL	308	5.6 (7.9)
subtype1	79	5.0 (8.6)
subtype2	52	6.5 (8.6)
subtype3	29	4.4 (5.6)
subtype4	72	6.3 (9.3)
subtype5	26	6.9 (7.4)
subtype6	50	4.4 (5.1)

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 = 0.177 (Fisher's exact test), Q value = 0.39

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	67	12	227
subtype1	20	5	49
subtype2	15	0	42
subtype3	5	0	19
subtype4	10	5	54
subtype5	3	0	24
subtype6	14	2	39

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 = 0.192 (Fisher's exact test), Q value = 0.39

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	250
subtype1	3	50
subtype2	0	53
subtype3	0	24
subtype4	0	53
subtype5	1	24
subtype6	1	46

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/20140953/STAD-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/STAD-TP/19775542/STAD-TP.merged_data.txt
Number of patients = 443
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