Correlation between aggregated molecular cancer subtypes and selected clinical features

Stomach and Esophageal carcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QF8S0M

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 14 clinical features across 612 patients, 62 significant findings detected with P value < 0.05 and Q value < 0.25.

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

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 62 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.106 (0.177)	0.276 (0.382)	0.139 (0.226)	0.445 (0.564)	0.0803 (0.143)	0.579 (0.665)	0.00971 (0.0217)	0.0462 (0.0834)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.000939 (0.00284)	8.72e-05 (0.000337)	3.93e-08 (1.47e-06)	2.68e-08 (1.47e-06)	1.52e-06 (3.41e-05)	4.27e-10 (4.79e-08)	3.96e-07 (1.11e-05)	2.08e-06 (3.89e-05)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.0005 (0.0016)	0.00109 (0.00313)	2e-05 (8.62e-05)	1e-05 (5.33e-05)	2e-05 (8.62e-05)	1e-05 (5.33e-05)	0.00151 (0.00403)	1e-05 (5.33e-05)
PATHOLOGY T STAGE	Fisher's exact test	0.00104 (0.00307)	0.00424 (0.0106)	1e-05 (5.33e-05)	1e-05 (5.33e-05)	0.00226 (0.00589)	1e-05 (5.33e-05)	0.00012 (0.000434)	1e-05 (5.33e-05)
PATHOLOGY N STAGE	Fisher's exact test	0.033 (0.0649)	0.0999 (0.17)	1e-05 (5.33e-05)	0.00027 (0.000916)	0.00681 (0.0159)	1e-05 (5.33e-05)	0.02 (0.0422)	5e-05 (2e-04)
PATHOLOGY M STAGE	Fisher's exact test	0.85 (0.895)	0.0411 (0.0756)	0.542 (0.648)	0.513 (0.632)	0.588 (0.665)	0.533 (0.648)	0.285 (0.389)	0.588 (0.665)
GENDER	Fisher's exact test	2e-05 (8.62e-05)	0.896 (0.905)	1e-05 (5.33e-05)	0.00143 (0.00391)	0.00033 (0.00109)	0.0162 (0.0349)	0.00809 (0.0185)	0.00503 (0.0121)
HISTOLOGICAL TYPE	Fisher's exact test	0.00011 (0.000411)	2e-05 (8.62e-05)	0.0209 (0.0433)	0.00084 (0.00261)	1e-05 (5.33e-05)	0.00018 (0.00063)	1e-05 (5.33e-05)	2e-05 (8.62e-05)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	1 (1.00)	0.586 (0.665)	0.0867 (0.149)	0.18 (0.269)	0.301 (0.407)	0.544 (0.648)	0.855 (0.895)	0.448 (0.564)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.172 (0.26)	0.15 (0.24)	0.353 (0.459)	0.256 (0.359)	0.0814 (0.143)	0.0124 (0.0273)	0.0412 (0.0756)	0.246 (0.349)
COMPLETENESS OF RESECTION	Fisher's exact test	0.691 (0.766)	0.156 (0.243)	0.233 (0.334)	0.867 (0.9)	0.57 (0.665)	0.887 (0.905)	0.701 (0.767)	0.341 (0.449)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.0381 (0.0724)	0.331 (0.441)	0.139 (0.226)	0.219 (0.318)	0.594 (0.665)	0.155 (0.243)	0.445 (0.564)	0.0274 (0.055)
RACE	Fisher's exact test	0.00302 (0.00769)	0.00506 (0.0121)	0.0275 (0.055)	3e-05 (0.000124)	1e-05 (5.33e-05)	1e-05 (5.33e-05)	0.00142 (0.00391)	1e-05 (5.33e-05)
ETHNICITY	Fisher's exact test	0.705 (0.767)	0.894 (0.905)	0.716 (0.772)	0.159 (0.245)	0.193 (0.284)	0.459 (0.571)	0.795 (0.848)	0.0342 (0.0661)

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

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

Cluster Labels	1	2	3
Number of samples	163	200	246

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

P value = 0.106 (logrank test), Q value = 0.18

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	575	221	0.0 - 122.3 (12.8)
subtype1	154	59	0.1 - 122.3 (12.3)
subtype2	192	81	0.1 - 60.6 (12.6)
subtype3	229	81	0.0 - 116.4 (13.4)

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 = 0.000939 (Kruskal-Wallis (anova)), Q value = 0.0028

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

	nPatients	Mean (Std.Dev)
ALL	602	64.8 (11.3)
subtype1	160	67.1 (9.9)
subtype2	198	62.7 (11.6)
subtype3	244	65.1 (11.5)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 5e-04 (Fisher's exact test), Q value = 0.0016

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	19	48	32	77	84	29	97	72	48	51	4
subtype1	5	6	12	10	9	25	9	29	17	10	15	1
subtype2	2	3	12	9	39	26	18	30	21	14	12	3
subtype3	4	10	24	13	29	33	2	38	34	24	24	0

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00104 (Fisher's exact test), Q value = 0.0031

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

nPatients	T0+T1	T2	T3	T4
ALL	53	126	280	124
subtype1	20	31	76	28
subtype2	15	46	104	25
subtype3	18	49	100	71

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 = 0.033 (Fisher's exact test), Q value = 0.065

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

nPatients	N0	N1	N2	N3
ALL	200	182	96	97
subtype1	42	52	33	23
subtype2	70	69	24	27
subtype3	88	61	39	47

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 = 0.85 (Fisher's exact test), Q value = 0.9

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

nPatients	0	1
ALL	513	39
subtype1	132	9
subtype2	169	12
subtype3	212	18

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 = 2e-05 (Fisher's exact test), Q value = 8.6e-05

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

nPatients	FEMALE	MALE
ALL	182	427
subtype1	45	118
subtype2	37	163
subtype3	100	146

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00011 (Fisher's exact test), Q value = 0.00041

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

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	71	162	81	78	21	8	13
subtype1	6	42	30	32	5	5	3
subtype2	15	34	18	18	3	0	1
subtype3	50	86	33	28	13	3	9

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

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

nPatients	NO	YES
ALL	6	603
subtype1	1	162
subtype2	2	198
subtype3	3	243

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.172 (Kruskal-Wallis (anova)), Q value = 0.26

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

	nPatients	Mean (Std.Dev)
ALL	92	35.2 (21.9)
subtype1	22	39.0 (25.1)
subtype2	57	31.9 (20.0)
subtype3	13	43.0 (22.6)

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.691 (Fisher's exact test), Q value = 0.77

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

nPatients	R0	R1	R2	RX
ALL	347	18	19	25
subtype1	95	7	6	4
subtype2	73	3	5	5
subtype3	179	8	8	16

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0381 (Kruskal-Wallis (anova)), Q value = 0.072

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

	nPatients	Mean (Std.Dev)
ALL	387	5.7 (8.4)
subtype1	106	6.2 (9.9)
subtype2	79	7.3 (9.6)
subtype3	202	4.8 (6.9)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00302 (Fisher's exact test), Q value = 0.0077

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	129	13	1	385
subtype1	22	5	1	107
subtype2	60	3	0	113
subtype3	47	5	0	165

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.705 (Fisher's exact test), Q value = 0.77

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	394
subtype1	2	99
subtype2	1	121
subtype3	4	174

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S16. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	244	149	171

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.276 (logrank test), Q value = 0.38

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	549	206	0.1 - 122.3 (12.8)
subtype1	238	81	0.1 - 122.1 (13.3)
subtype2	145	57	0.1 - 68.0 (12.6)
subtype3	166	68	0.1 - 122.3 (12.5)

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 8.72e-05 (Kruskal-Wallis (anova)), Q value = 0.00034

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

	nPatients	Mean (Std.Dev)
ALL	557	64.4 (11.2)
subtype1	241	66.4 (10.9)
subtype2	147	62.3 (11.0)
subtype3	169	63.2 (11.3)

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

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00109 (Fisher's exact test), Q value = 0.0031

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	18	42	29	78	83	29	91	71	47	40	4
subtype1	9	10	18	11	24	43	12	35	35	21	13	0
subtype2	2	4	10	8	31	21	15	21	14	9	8	2
subtype3	0	4	14	10	23	19	2	35	22	17	19	2

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00424 (Fisher's exact test), Q value = 0.011

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	51	111	271	115
subtype1	32	39	115	48
subtype2	12	38	74	22
subtype3	7	34	82	45

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.0999 (Fisher's exact test), Q value = 0.17

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	194	165	90	92
subtype1	83	68	41	40
subtype2	59	50	20	15
subtype3	52	47	29	37

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0411 (Fisher's exact test), Q value = 0.076

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	478	32
subtype1	209	8
subtype2	129	8
subtype3	140	16

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.896 (Fisher's exact test), Q value = 0.9

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	160	404
subtype1	70	174
subtype2	40	109
subtype3	50	121

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 8.6e-05

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	66	134	72	77	20	8	13
subtype1	23	63	35	44	10	3	4
subtype2	2	24	14	19	0	3	1
subtype3	41	47	23	14	10	2	8

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.586 (Fisher's exact test), Q value = 0.66

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	558
subtype1	2	242
subtype2	1	148
subtype3	3	168

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.15 (Kruskal-Wallis (anova)), Q value = 0.24

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	92	35.2 (21.9)
subtype1	31	41.1 (25.2)
subtype2	49	30.8 (18.6)
subtype3	12	37.7 (23.1)

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.156 (Fisher's exact test), Q value = 0.24

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2
ALL	333	17	12
subtype1	163	4	4
subtype2	51	3	3
subtype3	119	10	5

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.331 (Kruskal-Wallis (anova)), Q value = 0.44

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	353	5.6 (8.4)
subtype1	167	5.4 (8.9)
subtype2	56	4.8 (5.5)
subtype3	130	6.3 (8.9)

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00506 (Fisher's exact test), Q value = 0.012

Table S29. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	130	14	1	360
subtype1	44	6	1	163
subtype2	52	4	0	80
subtype3	34	4	0	117

Figure S27. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.894 (Fisher's exact test), Q value = 0.9

Table S30. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	371
subtype1	3	150
subtype2	1	97
subtype3	3	124

Figure S28. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S31. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	235	121	92

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.139 (logrank test), Q value = 0.23

Table S32. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	414	144	0.0 - 122.1 (12.8)
subtype1	211	64	0.0 - 122.1 (12.9)
subtype2	119	46	0.1 - 105.1 (12.4)
subtype3	84	34	0.1 - 83.2 (13.0)

Figure S29. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 3.93e-08 (Kruskal-Wallis (anova)), Q value = 1.5e-06

Table S33. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	441	64.5 (11.4)
subtype1	228	67.5 (10.7)
subtype2	121	60.3 (11.6)
subtype3	92	62.7 (10.9)

Figure S30. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 2e-05 (Fisher's exact test), Q value = 8.6e-05

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

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	10	15	34	24	68	73	29	54	41	30	30	4
subtype1	8	10	22	14	24	41	13	31	16	14	20	3
subtype2	2	5	8	3	33	19	13	10	9	3	4	1
subtype3	0	0	4	7	11	13	3	13	16	13	6	0

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S35. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	44	106	193	80
subtype1	31	56	88	46
subtype2	13	37	57	6
subtype3	0	13	48	28

Figure S32. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S36. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	162	141	58	57
subtype1	79	79	38	25
subtype2	58	38	11	5
subtype3	25	24	9	27

Figure S33. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.542 (Fisher's exact test), Q value = 0.65

Table S37. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	370	27
subtype1	190	17
subtype2	97	5
subtype3	83	5

Figure S34. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S38. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	127	321
subtype1	76	159
subtype2	15	106
subtype3	36	56

Figure S35. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0209 (Fisher's exact test), Q value = 0.043

Table S39. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	51	131	34	36	14	5	1
subtype1	26	80	25	31	8	5	0
subtype2	2	10	2	0	0	0	0
subtype3	23	41	7	5	6	0	1

Figure S36. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0867 (Fisher's exact test), Q value = 0.15

Table S40. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	442
subtype1	6	229
subtype2	0	121
subtype3	0	92

Figure S37. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.353 (Kruskal-Wallis (anova)), Q value = 0.46

Table S41. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	92	35.2 (21.9)
subtype1	23	41.6 (26.0)
subtype2	66	33.2 (19.9)
subtype3	3	29.5 (28.9)

Figure S38. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.233 (Fisher's exact test), Q value = 0.33

Table S42. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	217	9	10	22
subtype1	140	6	9	13
subtype2	13	0	1	0
subtype3	64	3	0	9

Figure S39. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.139 (Kruskal-Wallis (anova)), Q value = 0.23

Table S43. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	238	4.8 (7.1)
subtype1	153	4.7 (7.5)
subtype2	14	4.5 (8.9)
subtype3	71	5.3 (5.6)

Figure S40. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0275 (Fisher's exact test), Q value = 0.055

Table S44. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	114	6	267
subtype1	49	3	136
subtype2	43	3	63
subtype3	22	0	68

Figure S41. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.716 (Fisher's exact test), Q value = 0.77

Table S45. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	312
subtype1	2	156
subtype2	2	73
subtype3	1	83

Figure S42. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S46. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	76	150	25	80	117

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.445 (logrank test), Q value = 0.56

Table S47. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	414	144	0.0 - 122.1 (12.8)
subtype1	69	29	0.1 - 72.2 (12.2)
subtype2	134	41	0.1 - 122.1 (12.9)
subtype3	25	10	0.4 - 54.0 (10.2)
subtype4	78	27	0.1 - 68.0 (12.5)
subtype5	108	37	0.0 - 105.1 (13.2)

Figure S43. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 2.68e-08 (Kruskal-Wallis (anova)), Q value = 1.5e-06

Table S48. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	441	64.5 (11.4)
subtype1	74	65.9 (10.3)
subtype2	146	68.3 (10.7)
subtype3	25	65.2 (12.9)
subtype4	80	59.0 (10.7)
subtype5	116	62.6 (11.3)

Figure S44. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S49. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	10	15	34	24	68	73	29	54	41	30	30	4
subtype1	0	2	6	3	8	12	7	15	6	5	2	1
subtype2	8	9	18	8	13	25	8	15	8	11	13	2
subtype3	2	0	2	1	2	5	1	1	3	0	0	0
subtype4	0	3	4	1	31	12	10	7	5	2	3	1
subtype5	0	1	4	11	14	19	3	16	19	12	12	0

Figure S45. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

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

nPatients	T0+T1	T2	T3	T4
ALL	44	106	193	80
subtype1	2	19	37	12
subtype2	28	40	44	29
subtype3	6	5	9	0
subtype4	7	24	44	4
subtype5	1	18	59	35

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00027 (Fisher's exact test), Q value = 0.00092

Table S51. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	162	141	58	57
subtype1	22	24	12	10
subtype2	56	43	25	17
subtype3	7	10	3	0
subtype4	45	24	6	3
subtype5	32	40	12	27

Figure S47. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.513 (Fisher's exact test), Q value = 0.63

Table S52. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	370	27
subtype1	63	2
subtype2	120	12
subtype3	14	0
subtype4	69	4
subtype5	104	9

Figure S48. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00143 (Fisher's exact test), Q value = 0.0039

Table S53. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	127	321
subtype1	18	58
subtype2	53	97
subtype3	3	22
subtype4	12	68
subtype5	41	76

Figure S49. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00084 (Fisher's exact test), Q value = 0.0026

Table S54. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	51	131	34	36	14	5	1
subtype1	3	32	8	8	0	2	0
subtype2	15	51	16	21	5	2	0
subtype3	1	2	1	0	0	0	0
subtype4	0	0	0	0	0	0	0
subtype5	32	46	9	7	9	1	1

Figure S50. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.18 (Fisher's exact test), Q value = 0.27

Table S55. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	442
subtype1	2	74
subtype2	4	146
subtype3	0	25
subtype4	0	80
subtype5	0	117

Figure S51. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.256 (Kruskal-Wallis (anova)), Q value = 0.36

Table S56. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	92	35.2 (21.9)
subtype1	10	27.4 (18.6)
subtype2	14	41.1 (28.5)
subtype3	14	43.8 (21.9)
subtype4	48	31.9 (19.1)
subtype5	6	39.8 (27.2)

Figure S52. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.867 (Fisher's exact test), Q value = 0.9

Table S57. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	217	9	10	22
subtype1	44	2	1	4
subtype2	87	4	7	8
subtype3	4	0	0	0
subtype4	0	0	0	0
subtype5	82	3	2	10

Figure S53. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.219 (Kruskal-Wallis (anova)), Q value = 0.32

Table S58. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	238	4.8 (7.1)
subtype1	46	4.6 (6.4)
subtype2	99	4.3 (6.5)
subtype3	4	1.8 (1.3)
subtype5	89	5.7 (8.0)

Figure S54. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00012

Table S59. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	114	6	267
subtype1	14	1	49
subtype2	31	3	82
subtype3	0	0	20
subtype4	38	2	37
subtype5	31	0	79

Figure S55. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.159 (Fisher's exact test), Q value = 0.24

Table S60. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	312
subtype1	2	53
subtype2	1	93
subtype3	1	11
subtype4	0	55
subtype5	1	100

Figure S56. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S61. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	154	326	130

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0803 (logrank test), Q value = 0.14

Table S62. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	576	221	0.0 - 122.3 (12.8)
subtype1	151	65	0.1 - 68.0 (12.4)
subtype2	299	111	0.0 - 122.3 (13.9)
subtype3	126	45	0.1 - 116.4 (11.2)

Figure S57. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 1.52e-06 (Kruskal-Wallis (anova)), Q value = 3.4e-05

Table S63. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	603	64.8 (11.2)
subtype1	154	61.2 (11.1)
subtype2	319	66.8 (10.6)
subtype3	130	64.2 (11.9)

Figure S58. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 2e-05 (Fisher's exact test), Q value = 8.6e-05

Table S64. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	19	48	33	77	84	30	97	72	48	50	4
subtype1	0	5	12	3	36	19	16	19	14	6	11	2
subtype2	11	12	27	21	28	45	13	51	39	29	26	2
subtype3	0	2	9	9	13	20	1	27	19	13	13	0

Figure S59. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.00226 (Fisher's exact test), Q value = 0.0059

Table S65. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	53	126	281	124
subtype1	13	38	78	17
subtype2	35	66	139	71
subtype3	5	22	64	36

Figure S60. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00681 (Fisher's exact test), Q value = 0.016

Table S66. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	201	183	96	96
subtype1	62	52	16	14
subtype2	102	92	60	52
subtype3	37	39	20	30

Figure S61. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.588 (Fisher's exact test), Q value = 0.66

Table S67. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	514	39
subtype1	125	11
subtype2	278	18
subtype3	111	10

Figure S62. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00033 (Fisher's exact test), Q value = 0.0011

Table S68. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	182	428
subtype1	27	127
subtype2	107	219
subtype3	48	82

Figure S63. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S69. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	71	163	80	78	22	8	13
subtype1	5	18	9	15	1	3	1
subtype2	31	103	58	55	11	4	10
subtype3	35	42	13	8	10	1	2

Figure S64. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.301 (Fisher's exact test), Q value = 0.41

Table S70. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	604
subtype1	0	154
subtype2	5	321
subtype3	1	129

Figure S65. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0814 (Kruskal-Wallis (anova)), Q value = 0.14

Table S71. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	91	35.4 (21.9)
subtype1	60	31.3 (18.8)
subtype2	22	43.7 (25.7)
subtype3	9	42.3 (26.2)

Figure S66. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.57 (Fisher's exact test), Q value = 0.66

Table S72. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	348	18	18	25
subtype1	39	3	4	1
subtype2	217	11	10	19
subtype3	92	4	4	5

Figure S67. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.594 (Kruskal-Wallis (anova)), Q value = 0.67

Table S73. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	387	5.5 (8.1)
subtype1	46	6.0 (9.5)
subtype2	240	5.4 (8.1)
subtype3	101	5.6 (7.3)

Figure S68. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S74. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	129	14	385
subtype1	56	4	77
subtype2	46	10	213
subtype3	27	0	95

Figure S69. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.193 (Fisher's exact test), Q value = 0.28

Table S75. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	394
subtype1	4	95
subtype2	2	191
subtype3	1	108

Figure S70. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S76. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5
Number of samples	125	106	227	85	67

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.579 (logrank test), Q value = 0.66

Table S77. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	576	221	0.0 - 122.3 (12.8)
subtype1	118	50	0.1 - 79.1 (12.0)
subtype2	98	39	0.0 - 122.1 (12.3)
subtype3	214	77	0.1 - 122.3 (13.3)
subtype4	83	28	0.1 - 68.0 (12.6)
subtype5	63	27	0.1 - 116.4 (13.4)

Figure S71. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 4.27e-10 (Kruskal-Wallis (anova)), Q value = 4.8e-08

Table S78. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	603	64.8 (11.2)
subtype1	123	66.5 (10.0)
subtype2	105	68.6 (9.8)
subtype3	223	65.5 (11.5)
subtype4	85	58.6 (10.8)
subtype5	67	61.5 (11.4)

Figure S72. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S79. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	11	19	48	33	77	84	30	97	72	48	50	4
subtype1	3	1	10	10	6	19	5	24	11	12	13	3
subtype2	5	6	15	8	11	13	8	10	7	8	6	0
subtype3	3	9	14	10	23	32	6	40	34	17	22	0
subtype4	0	3	4	1	31	13	11	9	6	2	3	1
subtype5	0	0	5	4	6	7	0	14	14	9	6	0

Figure S73. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S80. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	53	126	281	124
subtype1	7	28	57	28
subtype2	17	28	40	15
subtype3	22	36	106	51
subtype4	7	24	48	5
subtype5	0	10	30	25

Figure S74. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S81. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	201	183	96	96
subtype1	30	41	25	21
subtype2	42	28	12	15
subtype3	65	68	45	38
subtype4	47	27	6	3
subtype5	17	19	8	19

Figure S75. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.533 (Fisher's exact test), Q value = 0.65

Table S82. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	514	39
subtype1	100	12
subtype2	91	5
subtype3	191	15
subtype4	74	4
subtype5	58	3

Figure S76. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0162 (Fisher's exact test), Q value = 0.035

Table S83. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	182	428
subtype1	36	89
subtype2	36	70
subtype3	73	154
subtype4	13	72
subtype5	24	43

Figure S77. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.00018 (Fisher's exact test), Q value = 0.00063

Table S84. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	71	163	80	78	22	8	13
subtype1	5	34	26	27	4	4	3
subtype2	12	33	19	14	4	1	2
subtype3	33	70	32	31	8	3	6
subtype4	0	2	0	0	0	0	0
subtype5	21	24	3	6	6	0	2

Figure S78. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.544 (Fisher's exact test), Q value = 0.65

Table S85. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	6	604
subtype1	2	123
subtype2	0	106
subtype3	4	223
subtype4	0	85
subtype5	0	67

Figure S79. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0124 (Kruskal-Wallis (anova)), Q value = 0.027

Table S86. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	91	35.4 (21.9)
subtype1	8	46.5 (18.5)
subtype2	6	18.3 (17.2)
subtype3	26	41.7 (24.7)
subtype4	50	31.5 (18.9)
subtype5	1	80.0 (NA)

Figure S80. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.887 (Fisher's exact test), Q value = 0.9

Table S87. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	348	18	18	25
subtype1	84	3	5	4
subtype2	62	3	3	8
subtype3	152	9	8	9
subtype4	2	0	0	0
subtype5	48	3	2	4

Figure S81. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.155 (Kruskal-Wallis (anova)), Q value = 0.24

Table S88. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	387	5.5 (8.1)
subtype1	86	5.4 (6.2)
subtype2	71	5.3 (9.8)
subtype3	173	5.7 (8.2)
subtype4	2	0.5 (0.7)
subtype5	55	5.7 (8.1)

Figure S82. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S89. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	129	14	385
subtype1	23	4	73
subtype2	19	4	66
subtype3	29	3	159
subtype4	40	2	40
subtype5	18	1	47

Figure S83. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.459 (Fisher's exact test), Q value = 0.57

Table S90. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	394
subtype1	3	76
subtype2	0	64
subtype3	3	136
subtype4	0	58
subtype5	1	60

Figure S84. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S91. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	133	208	170

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

P value = 0.00971 (logrank test), Q value = 0.022

Table S92. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	497	193	0.1 - 122.3 (12.7)
subtype1	131	63	0.1 - 79.1 (12.3)
subtype2	201	67	0.1 - 122.3 (12.8)
subtype3	165	63	0.1 - 83.2 (13.2)

Figure S85. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 3.96e-07 (Kruskal-Wallis (anova)), Q value = 1.1e-05

Table S93. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	505	64.4 (11.3)
subtype1	132	60.5 (11.1)
subtype2	203	67.2 (10.9)
subtype3	170	64.0 (11.1)

Figure S86. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.00151 (Fisher's exact test), Q value = 0.004

Table S94. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	10	12	37	30	71	75	28	84	62	41	37	4
subtype1	1	2	7	3	28	19	13	17	13	6	10	3
subtype2	9	8	18	13	22	30	8	31	31	17	11	1
subtype3	0	2	12	14	21	26	7	36	18	18	16	0

Figure S87. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00012 (Fisher's exact test), Q value = 0.00043

Table S95. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	44	105	248	99
subtype1	9	30	71	13
subtype2	29	41	97	36
subtype3	6	34	80	50

Figure S88. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.02 (Fisher's exact test), Q value = 0.042

Table S96. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	172	153	84	80
subtype1	45	48	18	11
subtype2	72	53	43	33
subtype3	55	52	23	36

Figure S89. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.285 (Fisher's exact test), Q value = 0.39

Table S97. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	432	31
subtype1	105	11
subtype2	179	9
subtype3	148	11

Figure S90. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.00809 (Fisher's exact test), Q value = 0.018

Table S98. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	142	369
subtype1	24	109
subtype2	61	147
subtype3	57	113

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

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

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	57	115	68	69	19	6	9
subtype1	4	13	11	12	1	1	0
subtype2	17	50	39	44	5	4	4
subtype3	36	52	18	13	13	1	5

Figure S92. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.855 (Fisher's exact test), Q value = 0.9

Table S100. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	5	506
subtype1	1	132
subtype2	3	205
subtype3	1	169

Figure S93. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0412 (Kruskal-Wallis (anova)), Q value = 0.076

Table S101. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	88	35.8 (22.0)
subtype1	53	30.9 (19.5)
subtype2	19	40.0 (19.4)
subtype3	16	47.2 (28.1)

Figure S94. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.701 (Fisher's exact test), Q value = 0.77

Table S102. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2
ALL	294	14	10
subtype1	34	2	2
subtype2	144	5	4
subtype3	116	7	4

Figure S95. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.445 (Kruskal-Wallis (anova)), Q value = 0.56

Table S103. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	309	5.5 (7.9)
subtype1	38	5.2 (6.7)
subtype2	149	5.5 (8.8)
subtype3	122	5.6 (7.2)

Figure S96. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.00142 (Fisher's exact test), Q value = 0.0039

Table S104. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	107	12	335
subtype1	42	3	69
subtype2	31	7	138
subtype3	34	2	128

Figure S97. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.795 (Fisher's exact test), Q value = 0.85

Table S105. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	324
subtype1	2	74
subtype2	3	119
subtype3	2	131

Figure S98. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'ETHNICITY'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S106. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	47	120	162	100	82

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

P value = 0.0462 (logrank test), Q value = 0.083

Table S107. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	497	193	0.1 - 122.3 (12.7)
subtype1	45	22	0.1 - 60.6 (9.3)
subtype2	117	53	0.4 - 122.1 (14.3)
subtype3	158	48	0.1 - 122.3 (13.3)
subtype4	97	42	0.1 - 83.2 (12.6)
subtype5	80	28	0.1 - 68.0 (12.5)

Figure S99. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 2.08e-06 (Kruskal-Wallis (anova)), Q value = 3.9e-05

Table S108. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	505	64.4 (11.3)
subtype1	46	65.2 (10.8)
subtype2	118	65.9 (10.7)
subtype3	159	66.5 (11.1)
subtype4	100	63.5 (11.6)
subtype5	82	58.7 (10.9)

Figure S100. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S109. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	10	12	37	30	71	75	28	84	62	41	37	4
subtype1	0	0	6	2	2	5	5	12	5	3	2	0
subtype2	9	5	13	4	10	18	8	11	14	9	5	3
subtype3	1	4	8	15	16	27	4	33	22	14	15	0
subtype4	0	0	7	8	11	13	2	19	15	13	12	0
subtype5	0	3	3	1	32	12	9	9	6	2	3	1

Figure S101. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S110. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	44	105	248	99
subtype1	3	10	22	9
subtype2	24	20	46	20
subtype3	9	33	85	34
subtype4	1	18	50	31
subtype5	7	24	45	5

Figure S102. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 2e-04

Table S111. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	172	153	84	80
subtype1	10	18	8	8
subtype2	43	35	19	12
subtype3	47	46	33	32
subtype4	26	29	18	25
subtype5	46	25	6	3

Figure S103. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.588 (Fisher's exact test), Q value = 0.66

Table S112. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	432	31
subtype1	40	1
subtype2	90	8
subtype3	144	9
subtype4	86	9
subtype5	72	4

Figure S104. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.00503 (Fisher's exact test), Q value = 0.012

Table S113. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	142	369
subtype1	12	35
subtype2	34	86
subtype3	44	118
subtype4	40	60
subtype5	12	70

Figure S105. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 8.6e-05

Table S114. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients	STOMACH ADENOCARCINOMA DIFFUSE TYPE	STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	57	115	68	69	19	6	9
subtype1	0	16	7	10	1	0	0
subtype2	7	14	19	27	1	3	2
subtype3	23	53	27	23	6	2	5
subtype4	27	30	15	9	11	1	2
subtype5	0	2	0	0	0	0	0

Figure S106. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.448 (Fisher's exact test), Q value = 0.56

Table S115. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	5	506
subtype1	1	46
subtype2	1	119
subtype3	3	159
subtype4	0	100
subtype5	0	82

Figure S107. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.246 (Kruskal-Wallis (anova)), Q value = 0.35

Table S116. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	88	35.8 (22.0)
subtype1	4	40.0 (22.7)
subtype2	21	36.1 (20.3)
subtype3	15	47.5 (29.0)
subtype4	1	40.0 (NA)
subtype5	47	31.5 (19.5)

Figure S108. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS_OF_RESECTION'

P value = 0.341 (Fisher's exact test), Q value = 0.45

Table S117. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2
ALL	294	14	10
subtype1	29	2	0
subtype2	66	0	2
subtype3	118	6	5
subtype4	79	6	3
subtype5	2	0	0

Figure S109. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS_OF_RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0274 (Kruskal-Wallis (anova)), Q value = 0.055

Table S118. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	309	5.5 (7.9)
subtype1	30	5.2 (5.8)
subtype2	70	4.4 (8.8)
subtype3	124	6.2 (8.3)
subtype4	83	5.6 (7.4)
subtype5	2	0.5 (0.7)

Figure S110. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 5.3e-05

Table S119. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	107	12	335
subtype1	7	2	28
subtype2	14	5	81
subtype3	21	4	115
subtype4	26	0	72
subtype5	39	1	39

Figure S111. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0342 (Fisher's exact test), Q value = 0.066

Table S120. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	324
subtype1	3	22
subtype2	1	54
subtype3	2	104
subtype4	1	88
subtype5	0	56

Figure S112. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/STES-TP/15125163/STES-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/STES-TP/15090071/STES-TP.merged_data.txt
Number of patients = 612
Number of clustering approaches = 8
Number of selected clinical features = 14
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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