Correlation between aggregated molecular cancer subtypes and selected clinical features

Stomach Adenocarcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C11Z43WF

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, 42 significant findings detected with P value < 0.05 and Q value < 0.25.

4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL_TYPE'.
5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.
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', 'PATHOLOGY_T_STAGE', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'HISTOLOGICAL_TYPE', and 'NUMBER_OF_LYMPH_NODES'.
6 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'RADIATION_THERAPY', 'HISTOLOGICAL_TYPE', and 'RESIDUAL_TUMOR'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY_T_STAGE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', 'RESIDUAL_TUMOR', 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, 42 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	0.583 (0.704)	0.407 (0.608)	0.0709 (0.204)	0.0139 (0.0799)	0.0323 (0.117)	0.00444 (0.0323)	0.0438 (0.143)	0.0722 (0.204)	0.301 (0.515)	0.0852 (0.225)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.254 (0.471)	0.000997 (0.00997)	0.319 (0.539)	0.541 (0.682)	0.00571 (0.0371)	0.00447 (0.0323)	0.00145 (0.0134)	0.147 (0.319)	0.171 (0.347)	0.104 (0.247)
PATHOLOGIC STAGE	Fisher's exact test	0.973 (1.00)	0.721 (0.833)	0.0155 (0.0804)	0.167 (0.344)	0.0889 (0.225)	0.503 (0.663)	0.00035 (0.00414)	0.299 (0.515)	0.21 (0.395)	0.0539 (0.163)
PATHOLOGY T STAGE	Fisher's exact test	0.297 (0.515)	0.348 (0.565)	0.00013 (0.00211)	0.0228 (0.0925)	0.00564 (0.0371)	0.0201 (0.091)	0.0165 (0.0826)	0.0274 (0.105)	1e-05 (0.00026)	0.00924 (0.0572)
PATHOLOGY N STAGE	Fisher's exact test	0.424 (0.618)	0.905 (0.972)	0.45 (0.63)	0.455 (0.63)	0.089 (0.225)	0.00272 (0.0221)	0.0865 (0.225)	0.354 (0.565)	0.479 (0.643)	0.262 (0.48)
PATHOLOGY M STAGE	Fisher's exact test	0.273 (0.493)	0.0349 (0.123)	0.126 (0.282)	0.329 (0.545)	0.284 (0.505)	0.869 (0.949)	0.114 (0.259)	0.783 (0.886)	0.746 (0.85)	0.57 (0.704)
GENDER	Fisher's exact test	0.062 (0.183)	0.981 (1.00)	0.331 (0.545)	0.417 (0.615)	0.724 (0.833)	0.19 (0.363)	0.0787 (0.218)	0.972 (1.00)	0.855 (0.942)	0.593 (0.707)
RADIATION THERAPY	Fisher's exact test	0.106 (0.247)	0.185 (0.363)	0.0202 (0.091)	0.0141 (0.0799)	0.0217 (0.091)	0.0916 (0.225)	0.00255 (0.0221)	0.0206 (0.091)	0.0261 (0.103)	0.103 (0.247)
HISTOLOGICAL TYPE	Fisher's exact test	0.0473 (0.15)	1e-05 (0.00026)	0.00044 (0.00477)	0.164 (0.343)	1e-05 (0.00026)	0.00021 (0.00273)	3e-05 (0.000557)	0.00019 (0.00273)	1e-05 (0.00026)	1e-05 (0.00026)
RESIDUAL TUMOR	Fisher's exact test	0.437 (0.618)	0.0407 (0.139)	0.923 (0.984)	0.577 (0.704)	0.834 (0.926)	0.95 (1.00)	3e-05 (0.000557)	0.713 (0.833)	0.188 (0.363)	0.0212 (0.091)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.183 (0.363)	0.585 (0.704)	0.358 (0.565)	0.472 (0.64)	0.0304 (0.113)	0.0155 (0.0804)	0.155 (0.33)	0.391 (0.598)	0.577 (0.704)	0.708 (0.833)
RACE	Fisher's exact test	0.9 (0.972)	0.977 (1.00)	0.434 (0.618)	0.431 (0.618)	0.0439 (0.143)	0.505 (0.663)	0.09 (0.225)	0.361 (0.565)	0.386 (0.597)	0.0484 (0.15)
ETHNICITY	Fisher's exact test	1 (1.00)	0.53 (0.675)	1 (1.00)	0.808 (0.905)	0.518 (0.673)	0.135 (0.297)	0.399 (0.603)	1 (1.00)	0.526 (0.675)	0.467 (0.639)

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

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

Cluster Labels	1	2	3	4
Number of samples	234	153	51	3

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

P value = 0.583 (logrank test), Q value = 0.7

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	415	169	0.0 - 122.3 (14.8)
subtype1	222	92	0.3 - 122.3 (14.0)
subtype2	141	60	0.0 - 116.4 (16.8)
subtype3	49	17	0.2 - 79.1 (17.5)
subtype4	3	0	3.2 - 12.6 (12.0)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.254 (Kruskal-Wallis (anova)), Q value = 0.47

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	229	66.1 (9.8)
subtype2	150	64.6 (12.3)
subtype3	50	67.2 (10.1)
subtype4	3	74.0 (2.0)

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 = 0.973 (Fisher's exact test), Q value = 1

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	2	16	41	32	40	56	3	81	63	39	44
subtype1	1	7	19	19	22	32	2	48	31	20	22
subtype2	1	6	16	9	13	20	1	26	21	14	17
subtype3	0	2	6	3	5	4	0	7	10	5	5
subtype4	0	1	0	1	0	0	0	0	1	0	0

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 = 0.297 (Fisher's exact test), Q value = 0.51

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	10	52	108	59
subtype2	9	30	61	48
subtype3	3	10	27	11
subtype4	1	1	0	1

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 = 0.424 (Fisher's exact test), Q value = 0.62

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

nPatients	N0	N1	N2	N3
ALL	130	119	86	88
subtype1	65	63	52	44
subtype2	47	44	27	28
subtype3	17	11	6	16
subtype4	1	1	1	0

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 = 0.273 (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	389	30
subtype1	209	11
subtype2	133	14
subtype3	44	5
subtype4	3	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 = 0.062 (Fisher's exact test), Q value = 0.18

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

nPatients	FEMALE	MALE
ALL	158	283
subtype1	78	156
subtype2	66	87
subtype3	14	37
subtype4	0	3

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 = 0.106 (Fisher's exact test), Q value = 0.25

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

nPatients	NO	YES
ALL	322	76
subtype1	164	49
subtype2	114	23
subtype3	41	4
subtype4	3	0

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 = 0.0473 (Fisher's exact test), Q value = 0.15

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	13	72	163	21	82	8	79
subtype1	7	26	82	9	50	5	52
subtype2	3	33	62	8	26	2	19
subtype3	3	12	18	3	6	1	8
subtype4	0	1	1	1	0	0	0

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.437 (Fisher's exact test), Q value = 0.62

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	187	10	10	9
subtype2	116	6	8	15
subtype3	43	2	1	1
subtype4	3	0	0	0

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 = 0.183 (Kruskal-Wallis (anova)), Q value = 0.36

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	203	6.4 (9.9)
subtype2	139	4.2 (5.5)
subtype3	46	6.8 (8.2)
subtype4	3	1.7 (1.5)

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 = 0.9 (Fisher's exact test), Q value = 0.97

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	49	8	1	144
subtype2	28	3	0	103
subtype3	10	1	0	30
subtype4	1	0	0	1

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

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	3	161
subtype2	2	116
subtype3	0	37
subtype4	0	2

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	5
Number of samples	46	111	51	100	87

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.407 (logrank test), Q value = 0.61

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	384	150	0.1 - 122.3 (15.3)
subtype1	45	20	0.6 - 66.8 (13.6)
subtype2	107	37	0.1 - 77.3 (16.1)
subtype3	49	15	0.3 - 73.4 (19.9)
subtype4	97	41	0.2 - 79.1 (14.0)
subtype5	86	37	0.3 - 122.3 (15.7)

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 = 0.000997 (Kruskal-Wallis (anova)), Q value = 0.01

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	44	63.6 (9.9)
subtype2	108	67.6 (9.2)
subtype3	50	64.9 (13.2)
subtype4	97	66.7 (9.6)
subtype5	87	61.5 (11.3)

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 = 0.721 (Fisher's exact test), Q value = 0.83

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	2	15	35	29	41	55	2	75	62	38	33
subtype1	0	1	5	5	4	2	0	9	8	4	7
subtype2	1	7	11	5	14	17	0	16	19	14	4
subtype3	0	1	3	3	7	10	0	11	8	5	3
subtype4	1	3	9	10	8	17	2	23	12	7	8
subtype5	0	3	7	6	8	9	0	16	15	8	11

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 = 0.348 (Fisher's exact test), Q value = 0.57

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	1	10	24	11
subtype2	11	21	49	30
subtype3	2	5	26	18
subtype4	4	26	47	23
subtype5	3	16	40	28

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 = 0.905 (Fisher's exact test), Q value = 0.97

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

nPatients	N0	N1	N2	N3
ALL	124	101	80	83
subtype1	14	10	12	10
subtype2	40	23	23	24
subtype3	19	13	9	9
subtype4	28	29	20	20
subtype5	23	26	16	20

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 = 0.0349 (Fisher's exact test), Q value = 0.12

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

nPatients	0	1
ALL	353	23
subtype1	36	4
subtype2	106	2
subtype3	46	3
subtype4	89	4
subtype5	76	10

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.981 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	136	259
subtype1	14	32
subtype2	39	72
subtype3	17	34
subtype4	35	65
subtype5	31	56

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.185 (Fisher's exact test), Q value = 0.36

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

nPatients	NO	YES
ALL	302	75
subtype1	31	12
subtype2	81	26
subtype3	41	5
subtype4	76	18
subtype5	73	14

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 = 0.00026

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	13	67	135	20	73	8	78
subtype1	2	7	12	2	8	1	13
subtype2	3	13	45	9	17	2	22
subtype3	1	8	20	0	13	1	8
subtype4	2	5	31	1	28	4	29
subtype5	5	34	27	8	7	0	6

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

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0407 (Fisher's exact test), Q value = 0.14

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

nPatients	R0	R1	R2
ALL	335	17	12
subtype1	35	4	2
subtype2	98	3	1
subtype3	47	0	2
subtype4	87	2	4
subtype5	68	8	3

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 = 0.585 (Kruskal-Wallis (anova)), Q value = 0.7

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	41	5.8 (7.2)
subtype2	101	4.6 (6.0)
subtype3	47	5.1 (8.6)
subtype4	90	6.3 (9.9)
subtype5	78	6.3 (9.7)

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 = 0.977 (Fisher's exact test), Q value = 1

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	11	2	0	28
subtype2	26	3	0	72
subtype3	11	1	0	32
subtype4	21	5	1	59
subtype5	20	2	0	62

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.53 (Fisher's exact test), Q value = 0.68

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	31
subtype2	1	83
subtype3	1	38
subtype4	0	69
subtype5	2	72

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 = 0.0709 (logrank test), Q value = 0.2

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	331	139	0.1 - 122.3 (14.5)
subtype1	28	10	3.0 - 79.1 (16.6)
subtype2	88	33	0.6 - 122.3 (15.8)
subtype3	72	30	0.1 - 72.2 (18.6)
subtype4	89	44	0.3 - 43.4 (12.6)
subtype5	54	22	0.3 - 77.3 (14.6)

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 = 0.54

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 = 0.0155 (Fisher's exact test), Q value = 0.08

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	2	11	32	29	32	44	2	67	51	32	33
subtype1	0	2	4	3	3	2	0	4	3	1	6
subtype2	0	1	13	8	10	12	1	16	16	6	4
subtype3	2	5	9	3	2	9	0	17	12	13	5
subtype4	0	3	2	8	10	13	1	17	11	11	14
subtype5	0	0	4	7	7	8	0	13	9	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 = 0.00013 (Fisher's exact test), Q value = 0.0021

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 = 0.45 (Fisher's exact test), Q value = 0.63

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

nPatients	N0	N1	N2	N3
ALL	109	96	65	71
subtype1	13	8	4	4
subtype2	33	21	17	17
subtype3	19	20	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 = 0.126 (Fisher's exact test), Q value = 0.28

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 = 0.331 (Fisher's exact test), Q value = 0.55

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 = 0.0202 (Fisher's exact test), Q value = 0.091

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

nPatients	NO	YES
ALL	265	57
subtype1	21	4
subtype2	67	18
subtype3	52	19
subtype4	83	7
subtype5	42	9

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 = 0.00044 (Fisher's exact test), Q value = 0.0048

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	10	52	149	19	69	5	50
subtype1	3	2	9	1	12	1	2
subtype2	2	9	34	4	25	1	17
subtype3	0	9	34	3	21	1	13
subtype4	4	20	42	8	7	1	10
subtype5	1	12	30	3	4	1	8

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 = 0.57

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 = 0.434 (Fisher's exact test), Q value = 0.62

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 = 0.0139 (logrank test), Q value = 0.08

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	331	139	0.1 - 122.3 (14.5)
subtype1	130	59	0.3 - 79.1 (15.5)
subtype2	49	15	0.1 - 55.6 (16.1)
subtype3	75	26	0.6 - 122.3 (16.7)
subtype4	77	39	0.3 - 40.2 (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 = 0.541 (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 = 0.167 (Fisher's exact test), Q value = 0.34

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	2	11	32	29	32	44	2	67	51	32	33
subtype1	1	6	13	13	8	15	0	31	17	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	3
subtype4	0	1	2	6	10	12	1	12	12	9	10

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 = 0.0228 (Fisher's exact test), Q value = 0.093

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 = 0.455 (Fisher's exact test), Q value = 0.63

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

nPatients	N0	N1	N2	N3
ALL	109	96	65	71
subtype1	39	44	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 = 0.329 (Fisher's exact test), Q value = 0.55

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 = 0.417 (Fisher's exact test), Q value = 0.62

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 = 0.0141 (Fisher's exact test), Q value = 0.08

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

nPatients	NO	YES
ALL	265	57
subtype1	96	29
subtype2	38	9
subtype3	59	14
subtype4	72	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 = 0.164 (Fisher's exact test), Q value = 0.34

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	10	52	149	19	69	5	50
subtype1	6	18	59	7	30	2	16
subtype2	1	7	26	2	13	1	8
subtype3	2	11	25	3	20	1	17
subtype4	1	16	39	7	6	1	9

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 = 0.577 (Fisher's exact test), Q value = 0.7

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 = 0.64

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 = 0.431 (Fisher's exact test), Q value = 0.62

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 = 0.91

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	94	95	94	82	50

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0323 (logrank test), Q value = 0.12

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	389	158	0.0 - 122.3 (15.3)
subtype1	87	37	0.6 - 72.2 (12.8)
subtype2	88	47	0.1 - 116.4 (14.0)
subtype3	90	30	0.5 - 105.1 (19.1)
subtype4	78	31	0.0 - 122.3 (17.3)
subtype5	46	13	1.0 - 54.1 (14.6)

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 = 0.00571 (Kruskal-Wallis (anova)), Q value = 0.037

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

	nPatients	Mean (Std.Dev)
ALL	406	65.7 (10.7)
subtype1	91	66.8 (9.8)
subtype2	94	63.1 (10.7)
subtype3	93	65.2 (10.6)
subtype4	79	65.4 (11.3)
subtype5	49	70.1 (10.0)

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 = 0.0889 (Fisher's exact test), Q value = 0.22

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	16	39	29	38	56	3	74	57	37	41
subtype1	1	3	12	7	7	10	1	21	12	7	8
subtype2	0	0	8	7	7	17	0	17	14	12	7
subtype3	0	4	9	8	14	14	1	16	7	9	7
subtype4	0	4	3	5	5	10	0	14	15	9	13
subtype5	1	5	7	2	5	5	1	6	9	0	6

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 = 0.00564 (Fisher's exact test), Q value = 0.037

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

nPatients	T1	T2	T3	T4
ALL	22	88	181	115
subtype1	4	22	46	20
subtype2	0	14	42	36
subtype3	4	21	40	26
subtype4	7	15	35	24
subtype5	7	16	18	9

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 = 0.089 (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	123	112	80	82
subtype1	27	26	18	17
subtype2	29	26	13	23
subtype3	35	26	14	15
subtype4	15	25	20	21
subtype5	17	9	15	6

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 = 0.284 (Fisher's exact test), Q value = 0.5

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

nPatients	0	1
ALL	367	27
subtype1	79	7
subtype2	85	5
subtype3	89	3
subtype4	68	9
subtype5	46	3

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 = 0.724 (Fisher's exact test), Q value = 0.83

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

nPatients	FEMALE	MALE
ALL	147	268
subtype1	32	62
subtype2	39	56
subtype3	32	62
subtype4	29	53
subtype5	15	35

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 = 0.0217 (Fisher's exact test), Q value = 0.091

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

nPatients	NO	YES
ALL	303	72
subtype1	69	14
subtype2	81	11
subtype3	69	20
subtype4	48	22
subtype5	36	5

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 = 1e-05 (Fisher's exact test), Q value = 0.00026

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	12	69	155	20	73	7	76
subtype1	1	2	35	2	26	2	25
subtype2	4	30	39	7	6	1	7
subtype3	3	16	42	6	11	2	14
subtype4	3	16	22	3	18	1	18
subtype5	1	5	17	2	12	1	12

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 = 0.834 (Fisher's exact test), Q value = 0.93

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

nPatients	R0	R1	R2	RX
ALL	330	17	17	22
subtype1	74	3	4	4
subtype2	72	4	3	7
subtype3	78	5	1	4
subtype4	65	4	6	4
subtype5	41	1	3	3

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 = 0.0304 (Kruskal-Wallis (anova)), Q value = 0.11

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

	nPatients	Mean (Std.Dev)
ALL	367	5.7 (8.6)
subtype1	79	5.2 (7.9)
subtype2	84	5.5 (7.8)
subtype3	82	4.5 (7.8)
subtype4	76	7.8 (9.5)
subtype5	46	5.8 (10.5)

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 = 0.0439 (Fisher's exact test), Q value = 0.14

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	87	12	1	260
subtype1	20	8	0	54
subtype2	23	0	0	71
subtype3	19	1	0	62
subtype4	16	1	0	46
subtype5	9	2	1	27

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.518 (Fisher's exact test), Q value = 0.67

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	302
subtype1	2	67
subtype2	1	86
subtype3	0	71
subtype4	1	46
subtype5	1	32

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
Number of samples	109	83	135	88

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00444 (logrank test), Q value = 0.032

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	389	158	0.0 - 122.3 (15.3)
subtype1	102	37	0.0 - 122.3 (18.4)
subtype2	79	41	0.1 - 116.4 (13.7)
subtype3	129	57	0.5 - 105.1 (13.0)
subtype4	79	23	1.0 - 66.8 (17.2)

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 = 0.00447 (Kruskal-Wallis (anova)), Q value = 0.032

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

	nPatients	Mean (Std.Dev)
ALL	406	65.7 (10.7)
subtype1	105	65.6 (10.2)
subtype2	82	62.4 (11.2)
subtype3	132	65.7 (10.9)
subtype4	87	68.8 (9.6)

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 = 0.503 (Fisher's exact test), Q value = 0.66

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	16	39	29	38	56	3	74	57	37	41
subtype1	1	4	8	11	6	15	0	17	17	11	11
subtype2	0	0	6	5	9	12	1	14	12	10	7
subtype3	0	6	11	10	11	16	1	30	19	9	16
subtype4	1	6	14	3	12	13	1	13	9	7	7

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 = 0.0201 (Fisher's exact test), Q value = 0.091

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

nPatients	T1	T2	T3	T4
ALL	22	88	181	115
subtype1	9	27	41	29
subtype2	0	12	36	32
subtype3	6	28	69	30
subtype4	7	21	35	24

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 = 0.00272 (Fisher's exact test), Q value = 0.022

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

nPatients	N0	N1	N2	N3
ALL	123	112	80	82
subtype1	24	30	30	21
subtype2	23	23	10	21
subtype3	35	44	22	28
subtype4	41	15	18	12

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 = 0.869 (Fisher's exact test), Q value = 0.95

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

nPatients	0	1
ALL	367	27
subtype1	94	7
subtype2	73	6
subtype3	120	10
subtype4	80	4

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

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

nPatients	FEMALE	MALE
ALL	147	268
subtype1	37	72
subtype2	36	47
subtype3	40	95
subtype4	34	54

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 = 0.0916 (Fisher's exact test), Q value = 0.22

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

nPatients	NO	YES
ALL	303	72
subtype1	69	26
subtype2	70	10
subtype3	101	23
subtype4	63	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 = 0.00021 (Fisher's exact test), Q value = 0.0027

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	12	69	155	20	73	7	76
subtype1	4	17	38	7	21	1	20
subtype2	3	30	28	7	8	0	6
subtype3	2	13	55	3	25	4	32
subtype4	3	9	34	3	19	2	18

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

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

nPatients	R0	R1	R2	RX
ALL	330	17	17	22
subtype1	85	6	6	7
subtype2	61	3	3	6
subtype3	111	5	5	5
subtype4	73	3	3	4

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 = 0.0155 (Kruskal-Wallis (anova)), Q value = 0.08

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

	nPatients	Mean (Std.Dev)
ALL	367	5.7 (8.6)
subtype1	99	6.4 (8.6)
subtype2	71	5.4 (7.7)
subtype3	115	6.3 (9.3)
subtype4	82	4.4 (8.4)

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 = 0.505 (Fisher's exact test), Q value = 0.66

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	87	12	1	260
subtype1	19	4	0	67
subtype2	21	0	0	60
subtype3	29	5	0	80
subtype4	18	3	1	53

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.135 (Fisher's exact test), Q value = 0.3

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	302
subtype1	3	68
subtype2	0	76
subtype3	2	95
subtype4	0	63

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	4	5	6
Number of samples	62	67	80	111	50	66

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0438 (logrank test), Q value = 0.14

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	410	167	0.0 - 122.3 (14.7)
subtype1	57	13	1.0 - 77.3 (15.7)
subtype2	57	29	0.0 - 105.1 (14.5)
subtype3	78	26	0.5 - 122.3 (18.1)
subtype4	107	51	0.1 - 116.4 (14.1)
subtype5	49	21	0.2 - 69.0 (15.9)
subtype6	62	27	0.6 - 63.6 (12.9)

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 = 0.00145 (Kruskal-Wallis (anova)), Q value = 0.013

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	58	69.2 (9.8)
subtype2	66	69.1 (10.0)
subtype3	78	64.2 (11.6)
subtype4	111	62.9 (11.1)
subtype5	50	66.5 (10.2)
subtype6	64	65.4 (9.6)

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 = 0.00035 (Fisher's exact test), Q value = 0.0041

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	2	16	40	32	41	55	3	80	61	39	43
subtype1	1	6	7	3	7	10	1	9	8	4	2
subtype2	0	2	7	12	5	5	0	10	2	1	15
subtype3	0	4	8	2	9	9	0	12	21	8	7
subtype4	0	1	8	8	13	14	1	18	15	17	9
subtype5	0	2	1	2	5	6	0	15	7	5	5
subtype6	1	1	9	5	2	11	1	16	8	4	5

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 = 0.0165 (Fisher's exact test), Q value = 0.083

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	8	13	27	12
subtype2	3	22	26	12
subtype3	4	11	41	24
subtype4	1	18	48	41
subtype5	4	11	22	13
subtype6	3	17	29	16

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 = 0.0865 (Fisher's exact test), Q value = 0.22

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

nPatients	N0	N1	N2	N3
ALL	129	117	85	87
subtype1	22	15	14	8
subtype2	23	19	11	8
subtype3	28	18	15	19
subtype4	32	30	15	29
subtype5	7	14	17	12
subtype6	17	21	13	11

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 = 0.114 (Fisher's exact test), Q value = 0.26

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

nPatients	0	1
ALL	384	30
subtype1	55	3
subtype2	54	11
subtype3	71	4
subtype4	99	7
subtype5	47	2
subtype6	58	3

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0787 (Fisher's exact test), Q value = 0.22

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

nPatients	FEMALE	MALE
ALL	155	281
subtype1	16	46
subtype2	33	34
subtype3	23	57
subtype4	39	72
subtype5	19	31
subtype6	25	41

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.00255 (Fisher's exact test), Q value = 0.022

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

nPatients	NO	YES
ALL	319	74
subtype1	43	16
subtype2	40	3
subtype3	57	19
subtype4	97	11
subtype5	33	14
subtype6	49	11

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00056

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	11	72	164	22	79	8	77
subtype1	1	5	27	4	12	0	13
subtype2	0	9	32	3	18	0	4
subtype3	2	14	26	4	12	4	17
subtype4	4	32	45	7	11	1	10
subtype5	3	9	11	1	10	1	15
subtype6	1	3	23	3	16	2	18

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

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 3e-05 (Fisher's exact test), Q value = 0.00056

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

nPatients	R0	R1	R2	RX
ALL	344	18	18	25
subtype1	50	1	0	3
subtype2	37	4	10	12
subtype3	67	4	0	2
subtype4	89	6	3	6
subtype5	47	1	2	0
subtype6	54	2	3	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 = 0.155 (Kruskal-Wallis (anova)), Q value = 0.33

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	56	3.5 (4.5)
subtype2	53	5.8 (8.8)
subtype3	73	6.4 (10.5)
subtype4	97	5.6 (7.4)
subtype5	49	7.4 (9.8)
subtype6	57	4.6 (5.4)

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 = 0.09 (Fisher's exact test), Q value = 0.22

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	88	13	273
subtype1	14	4	38
subtype2	5	0	32
subtype3	21	3	50
subtype4	25	0	82
subtype5	8	3	33
subtype6	15	3	38

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.399 (Fisher's exact test), Q value = 0.6

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	317
subtype1	1	43
subtype2	0	37
subtype3	3	62
subtype4	1	101
subtype5	0	28
subtype6	0	46

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 = 0.0722 (logrank test), Q value = 0.2

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	410	167	0.0 - 122.3 (14.7)
subtype1	225	82	0.0 - 122.3 (15.9)
subtype2	81	39	0.1 - 116.4 (13.7)
subtype3	104	46	0.2 - 79.1 (13.0)

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 = 0.32

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 = 0.299 (Fisher's exact test), Q value = 0.51

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	2	16	40	32	41	55	3	80	61	39	43
subtype1	1	15	23	15	25	31	1	42	35	19	22
subtype2	0	0	7	6	9	7	0	16	15	9	8
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 = 0.0274 (Fisher's exact test), Q value = 0.1

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 = 0.354 (Fisher's exact test), Q value = 0.57

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

nPatients	N0	N1	N2	N3
ALL	129	117	85	87
subtype1	80	63	51	43
subtype2	22	23	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 = 0.783 (Fisher's exact test), Q value = 0.89

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 = 0.972 (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 = 0.0206 (Fisher's exact test), Q value = 0.091

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

nPatients	NO	YES
ALL	319	74
subtype1	167	49
subtype2	73	7
subtype3	79	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 = 0.00019 (Fisher's exact test), Q value = 0.0027

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	11	72	164	22	79	8	77
subtype1	7	39	98	10	46	3	40
subtype2	2	25	31	7	7	1	8
subtype3	2	8	35	5	26	4	29

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

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.713 (Fisher's exact test), Q value = 0.83

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 = 0.6

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 = 0.361 (Fisher's exact test), Q value = 0.57

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
Number of samples	148	121	74

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

P value = 0.301 (logrank test), Q value = 0.51

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	333	136	0.1 - 122.3 (14.8)
subtype1	145	54	0.6 - 122.3 (16.7)
subtype2	117	52	0.1 - 115.7 (13.7)
subtype3	71	30	0.3 - 105.1 (13.9)

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 = 0.171 (Kruskal-Wallis (anova)), Q value = 0.35

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	143	65.8 (10.3)
subtype2	120	63.7 (11.1)
subtype3	72	66.7 (11.1)

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 = 0.21 (Fisher's exact test), Q value = 0.4

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	2	9	30	29	35	47	2	67	52	32	30
subtype1	1	6	14	9	14	18	1	30	27	14	11
subtype2	0	2	7	8	12	20	1	23	18	16	10
subtype3	1	1	9	12	9	9	0	14	7	2	9

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 = 1e-05 (Fisher's exact test), Q value = 0.00026

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	11	23	76	38
subtype2	2	17	58	44
subtype3	2	32	29	11

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 = 0.479 (Fisher's exact test), Q value = 0.64

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

nPatients	N0	N1	N2	N3
ALL	103	88	74	71
subtype1	45	34	38	28
subtype2	32	34	21	30
subtype3	26	20	15	13

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 = 0.746 (Fisher's exact test), Q value = 0.85

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

nPatients	0	1
ALL	306	22
subtype1	133	8
subtype2	106	8
subtype3	67	6

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 = 0.855 (Fisher's exact test), Q value = 0.94

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

nPatients	FEMALE	MALE
ALL	118	225
subtype1	49	99
subtype2	44	77
subtype3	25	49

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 = 0.0261 (Fisher's exact test), Q value = 0.1

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

nPatients	NO	YES
ALL	264	64
subtype1	106	37
subtype2	101	20
subtype3	57	7

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 = 0.00026

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	7	58	117	19	67	6	69
subtype1	3	16	42	5	32	3	47
subtype2	4	34	48	10	16	1	8
subtype3	0	8	27	4	19	2	14

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 = 0.188 (Fisher's exact test), Q value = 0.36

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

nPatients	R0	R1	R2
ALL	291	14	10
subtype1	129	4	2
subtype2	101	6	3
subtype3	61	4	5

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 = 0.577 (Kruskal-Wallis (anova)), Q value = 0.7

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	135	5.5 (8.9)
subtype2	107	5.7 (7.5)
subtype3	66	5.4 (6.6)

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 = 0.386 (Fisher's exact test), Q value = 0.6

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	30	8	94
subtype2	28	2	91
subtype3	9	2	42

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 = 0.526 (Fisher's exact test), Q value = 0.68

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	3	90
subtype2	1	109
subtype3	1	51

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
Number of samples	103	72	45	62	61

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

P value = 0.0852 (logrank test), Q value = 0.22

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	333	136	0.1 - 122.3 (14.8)
subtype1	100	37	0.3 - 122.3 (15.7)
subtype2	70	31	0.1 - 115.7 (17.0)
subtype3	43	19	1.0 - 105.1 (13.3)
subtype4	60	29	0.6 - 63.6 (12.3)
subtype5	60	20	1.1 - 74.5 (18.0)

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 = 0.104 (Kruskal-Wallis (anova)), Q value = 0.25

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	99	66.3 (10.1)
subtype2	71	62.1 (11.6)
subtype3	43	66.2 (11.3)
subtype4	61	66.3 (9.7)
subtype5	61	65.5 (11.3)

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 = 0.0539 (Fisher's exact test), Q value = 0.16

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	2	9	30	29	35	47	2	67	52	32	30
subtype1	0	5	7	7	7	15	0	16	22	14	7
subtype2	0	0	6	4	10	7	0	13	13	8	8
subtype3	0	2	3	9	5	7	0	11	1	0	6
subtype4	0	0	8	5	5	10	1	15	7	4	6
subtype5	2	2	6	4	8	8	1	12	9	6	3

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 = 0.00924 (Fisher's exact test), Q value = 0.057

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	7	14	51	31
subtype2	0	13	36	23
subtype3	2	19	19	5
subtype4	1	14	30	17
subtype5	5	12	27	17

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 = 0.262 (Fisher's exact test), Q value = 0.48

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

nPatients	N0	N1	N2	N3
ALL	103	88	74	71
subtype1	30	21	21	29
subtype2	19	20	13	18
subtype3	17	12	11	5
subtype4	16	22	12	10
subtype5	21	13	17	9

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 = 0.57 (Fisher's exact test), Q value = 0.7

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

nPatients	0	1
ALL	306	22
subtype1	93	5
subtype2	62	6
subtype3	41	4
subtype4	52	5
subtype5	58	2

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 = 0.593 (Fisher's exact test), Q value = 0.71

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

nPatients	FEMALE	MALE
ALL	118	225
subtype1	29	74
subtype2	27	45
subtype3	16	29
subtype4	24	38
subtype5	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 = 0.103 (Fisher's exact test), Q value = 0.25

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

nPatients	NO	YES
ALL	264	64
subtype1	77	24
subtype2	61	9
subtype3	34	3
subtype4	46	13
subtype5	46	15

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 = 0.00026

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

nPatients	STOMACH ADENOCARCINOMA, SIGNET RING TYPE	STOMACH, ADENOCARCINOMA, DIFFUSE TYPE	STOMACH, ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, MUCINOUS TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, NOT OTHERWISE SPECIFIED (NOS)	STOMACH, INTESTINAL ADENOCARCINOMA, PAPILLARY TYPE	STOMACH, INTESTINAL ADENOCARCINOMA, TUBULAR TYPE
ALL	7	58	117	19	67	6	69
subtype1	4	14	38	6	17	1	23
subtype2	2	28	25	7	5	1	4
subtype3	0	5	20	1	12	1	6
subtype4	0	3	24	4	18	0	13
subtype5	1	8	10	1	15	3	23

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 = 0.0212 (Fisher's exact test), Q value = 0.091

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

nPatients	R0	R1	R2
ALL	291	14	10
subtype1	91	4	0
subtype2	57	6	3
subtype3	37	1	4
subtype4	50	3	1
subtype5	56	0	2

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 = 0.708 (Kruskal-Wallis (anova)), Q value = 0.83

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	95	6.3 (8.5)
subtype2	66	5.7 (8.0)
subtype3	38	5.6 (7.7)
subtype4	51	4.5 (5.0)
subtype5	58	5.1 (9.2)

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 = 0.0484 (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	67	12	227
subtype1	12	4	78
subtype2	17	1	52
subtype3	6	0	23
subtype4	14	2	41
subtype5	18	5	33

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 = 0.467 (Fisher's exact test), Q value = 0.64

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	71
subtype2	0	66
subtype3	0	29
subtype4	1	47
subtype5	1	37

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/22552605/STAD-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/STAD-TP/22507291/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)

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