Correlation between aggregated molecular cancer subtypes and selected clinical features

Stomach Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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 (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1F18WT0

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 11 clinical features across 189 patients, 3 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 do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 3 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	0.476 (1.00)	0.795 (1.00)	0.509 (1.00)	0.869 (1.00)	0.504 (1.00)	0.554 (1.00)	0.515 (1.00)	0.823 (1.00)
AGE	ANOVA	0.191 (1.00)	0.00397 (0.306)	0.646 (1.00)	0.00709 (0.525)	0.0277 (1.00)	0.435 (1.00)	0.0253 (1.00)	0.117 (1.00)
GENDER	Fisher's exact test	0.0107 (0.778)	0.943 (1.00)	0.489 (1.00)	0.154 (1.00)	0.238 (1.00)	0.873 (1.00)	0.697 (1.00)	0.499 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.516 (1.00)	0.0643 (1.00)	0.377 (1.00)	0.141 (1.00)	0.000205 (0.0164)	0.000392 (0.031)	0.000791 (0.0617)	0.0635 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.127 (1.00)	0.0363 (1.00)	0.00408 (0.31)	0.00486 (0.364)	0.514 (1.00)	0.0172 (1.00)	0.135 (1.00)	0.0702 (1.00)
DISTANT METASTASIS	Chi-square test	0.349 (1.00)	0.399 (1.00)	0.406 (1.00)	0.38 (1.00)	0.111 (1.00)	0.994 (1.00)	0.0654 (1.00)	0.69 (1.00)
LYMPH NODE METASTASIS	Chi-square test	0.0801 (1.00)	0.33 (1.00)	0.108 (1.00)	0.215 (1.00)	0.0643 (1.00)	0.834 (1.00)	0.241 (1.00)	0.585 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.415 (1.00)	0.721 (1.00)	0.257 (1.00)	0.604 (1.00)	0.105 (1.00)	0.408 (1.00)	0.0437 (1.00)	0.659 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.0759 (1.00)	0.549 (1.00)	0.836 (1.00)	0.608 (1.00)	0.143 (1.00)	0.806 (1.00)	0.144 (1.00)	0.623 (1.00)
TUMOR STAGECODE	ANOVA
NEOPLASM DISEASESTAGE	Chi-square test	0.782 (1.00)	0.308 (1.00)	0.373 (1.00)	0.308 (1.00)	0.0523 (1.00)	0.794 (1.00)	0.586 (1.00)	0.218 (1.00)

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

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

Cluster Labels	1	2	3	4
Number of samples	82	19	13	73

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

P value = 0.476 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	147	20	0.1 - 72.2 (1.2)
subtype1	63	8	0.1 - 70.1 (0.9)
subtype2	16	3	0.1 - 31.0 (5.5)
subtype3	9	2	0.8 - 21.1 (1.2)
subtype4	59	7	0.2 - 72.2 (2.8)

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 'AGE'

P value = 0.191 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	180	66.6 (11.1)
subtype1	81	64.8 (12.4)
subtype2	17	68.4 (7.7)
subtype3	13	70.7 (9.8)
subtype4	69	67.4 (10.1)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0107 (Fisher's exact test), Q value = 0.78

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

nPatients	FEMALE	MALE
ALL	73	114
subtype1	41	41
subtype2	4	15
subtype3	7	6
subtype4	21	52

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.516 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: '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	29	91	38	11	11	3	2
subtype1	17	42	14	2	5	1	1
subtype2	4	7	7	1	0	0	0
subtype3	1	7	4	0	1	0	0
subtype4	7	35	13	8	5	2	1

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.127 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	7	180
subtype1	1	81
subtype2	0	19
subtype3	0	13
subtype4	6	67

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.349 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	155	18	14
subtype1	66	10	6
subtype2	18	1	0
subtype3	13	0	0
subtype4	58	7	8

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0801 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3	N3A	NX
ALL	58	64	31	14	9	11
subtype1	29	28	13	4	4	4
subtype2	7	2	3	3	3	1
subtype3	3	4	2	0	2	2
subtype4	19	30	13	7	0	4

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

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.415 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	130	9	12	25
subtype1	57	2	6	13
subtype2	14	2	1	1
subtype3	9	0	0	4
subtype4	50	5	5	7

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0759 (ANOVA), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	166	4.6 (6.5)
subtype1	74	3.8 (5.4)
subtype2	16	8.4 (10.5)
subtype3	11	4.7 (5.0)
subtype4	65	4.7 (6.4)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.782 (Chi-square test), Q value = 1

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	5	19	28	8	15	4	39	15	7	29
subtype1	1	2	9	12	4	5	3	16	7	4	12
subtype2	1	1	3	2	0	1	0	4	2	1	3
subtype3	0	0	0	4	0	0	0	5	1	1	0
subtype4	0	2	7	10	4	9	1	14	5	1	14

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S12. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	20	33	40	48

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.795 (logrank test), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	131	14	0.1 - 72.2 (1.0)
subtype1	20	0	0.1 - 8.7 (0.9)
subtype2	31	4	0.1 - 65.1 (1.0)
subtype3	37	5	0.1 - 70.1 (0.9)
subtype4	43	5	0.2 - 72.2 (1.3)

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00397 (ANOVA), Q value = 0.31

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	134	65.2 (11.0)
subtype1	19	62.9 (9.6)
subtype2	31	69.9 (10.5)
subtype3	40	61.1 (11.8)
subtype4	44	66.8 (9.8)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.943 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	51	90
subtype1	7	13
subtype2	11	22
subtype3	16	24
subtype4	17	31

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0643 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: '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	24	63	29	10	10	3	2
subtype1	4	11	3	2	0	0	0
subtype2	3	14	8	2	4	1	1
subtype3	14	15	6	0	4	0	1
subtype4	3	23	12	6	2	2	0

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0363 (Fisher's exact test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	134
subtype1	0	20
subtype2	1	32
subtype3	0	40
subtype4	6	42

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.399 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	119	11	11
subtype1	15	3	2
subtype2	30	1	2
subtype3	35	4	1
subtype4	39	3	6

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.33 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	N3A
ALL	52	46	25	10	8
subtype1	8	5	3	1	3
subtype2	17	7	4	2	3
subtype3	13	15	9	2	1
subtype4	14	19	9	5	1

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.721 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	116	8	5
subtype1	17	0	1
subtype2	31	1	1
subtype3	31	4	1
subtype4	37	3	2

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.549 (ANOVA), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	132	4.2 (5.8)
subtype1	18	5.7 (6.4)
subtype2	33	3.3 (6.5)
subtype3	37	4.0 (5.5)
subtype4	44	4.5 (5.2)

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.308 (Chi-square test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	4	13	25	9	14	3	33	14	6	18
subtype1	0	0	1	2	0	2	1	6	3	2	3
subtype2	2	3	5	6	4	2	0	5	3	1	2
subtype3	0	0	4	7	4	3	2	9	4	2	5
subtype4	0	1	3	10	1	7	0	13	4	1	8

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S23. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	24	31	28	30	39

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.509 (logrank test), Q value = 1

Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	115	15	0.1 - 72.2 (1.4)
subtype1	17	1	0.1 - 70.1 (1.0)
subtype2	24	2	0.1 - 59.0 (1.2)
subtype3	18	4	0.2 - 55.0 (2.0)
subtype4	23	4	0.2 - 54.0 (3.6)
subtype5	33	4	0.3 - 72.2 (2.9)

Figure S21. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.646 (ANOVA), Q value = 1

Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	145	67.1 (10.7)
subtype1	23	67.2 (10.5)
subtype2	31	64.7 (11.7)
subtype3	25	67.2 (12.1)
subtype4	29	69.0 (10.6)
subtype5	37	67.4 (8.8)

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.489 (Chi-square test), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	54	98
subtype1	11	13
subtype2	13	18
subtype3	7	21
subtype4	11	19
subtype5	12	27

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.377 (Chi-square test), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: '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
ALL	24	74	30	10	9	3
subtype1	3	17	3	1	0	0
subtype2	7	16	3	0	4	0
subtype3	5	13	6	1	2	1
subtype4	5	11	9	3	1	1
subtype5	4	17	9	5	2	1

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00408 (Chi-square test), Q value = 0.31

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	145
subtype1	0	24
subtype2	0	31
subtype3	5	23
subtype4	0	30
subtype5	2	37

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.406 (Chi-square test), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	125	15	12
subtype1	21	1	2
subtype2	27	2	2
subtype3	20	6	2
subtype4	27	2	1
subtype5	30	4	5

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.108 (Chi-square test), Q value = 1

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	N3A	NX
ALL	48	53	22	13	5	11
subtype1	11	8	0	2	0	3
subtype2	8	11	4	2	4	2
subtype3	7	12	4	4	1	0
subtype4	13	7	6	2	0	2
subtype5	9	15	8	3	0	4

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.257 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	102	7	10	22
subtype1	16	0	1	6
subtype2	19	1	0	6
subtype3	18	3	5	2
subtype4	22	1	2	4
subtype5	27	2	2	4

Figure S28. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.836 (ANOVA), Q value = 1

Table S32. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	132	4.9 (7.0)
subtype1	19	3.6 (8.5)
subtype2	26	5.3 (4.8)
subtype3	26	6.0 (7.4)
subtype4	27	4.8 (8.9)
subtype5	34	4.6 (5.5)

Figure S29. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.373 (Chi-square test), Q value = 1

Table S33. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	5	17	25	5	11	4	31	5	6	26
subtype1	0	1	3	5	1	1	0	6	0	0	2
subtype2	0	0	1	6	1	2	1	7	1	4	5
subtype3	0	1	3	6	1	2	0	5	0	1	8
subtype4	2	1	6	4	1	1	2	6	0	1	4
subtype5	0	2	4	4	1	5	1	7	4	0	7

Figure S30. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S34. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	29	32	42	49

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.869 (logrank test), Q value = 1

Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	115	15	0.1 - 72.2 (1.4)
subtype1	21	2	0.1 - 59.0 (1.1)
subtype2	24	1	0.1 - 55.0 (1.3)
subtype3	35	6	0.3 - 72.2 (4.0)
subtype4	35	6	0.1 - 54.0 (1.0)

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00709 (ANOVA), Q value = 0.52

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	145	67.1 (10.7)
subtype1	29	62.9 (11.7)
subtype2	28	65.9 (9.8)
subtype3	40	66.2 (9.4)
subtype4	48	71.0 (10.5)

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.154 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	54	98
subtype1	12	17
subtype2	7	25
subtype3	13	29
subtype4	22	27

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.141 (Chi-square test), Q value = 1

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: '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
ALL	24	74	30	10	9	3
subtype1	7	14	2	0	5	0
subtype2	6	16	4	3	2	1
subtype3	5	21	9	4	1	1
subtype4	6	23	15	3	1	1

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.00486 (Fisher's exact test), Q value = 0.36

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	145
subtype1	0	29
subtype2	5	27
subtype3	2	40
subtype4	0	49

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.38 (Chi-square test), Q value = 1

Table S40. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	125	15	12
subtype1	27	2	0
subtype2	23	4	5
subtype3	34	4	4
subtype4	41	5	3

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.215 (Chi-square test), Q value = 1

Table S41. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	N3A	NX
ALL	48	53	22	13	5	11
subtype1	9	11	3	2	2	2
subtype2	8	13	4	4	3	0
subtype3	10	18	7	3	0	4
subtype4	21	11	8	4	0	5

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.604 (Chi-square test), Q value = 1

Table S42. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	102	7	10	22
subtype1	17	1	0	6
subtype2	23	2	3	2
subtype3	30	2	3	4
subtype4	32	2	4	10

Figure S38. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.608 (ANOVA), Q value = 1

Table S43. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	132	4.9 (7.0)
subtype1	24	4.6 (5.6)
subtype2	29	6.5 (6.7)
subtype3	37	4.4 (6.8)
subtype4	42	4.5 (8.1)

Figure S39. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.308 (Chi-square test), Q value = 1

Table S44. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	5	17	25	5	11	4	31	5	6	26
subtype1	0	0	2	6	1	2	2	6	1	2	4
subtype2	0	0	5	4	1	4	0	9	0	3	6
subtype3	0	2	3	6	2	4	0	7	4	1	9
subtype4	2	3	7	9	1	1	2	9	0	0	7

Figure S40. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S45. Get Full Table Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	59	46	42	37

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.504 (logrank test), Q value = 1

Table S46. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	144	20	0.1 - 72.2 (1.3)
subtype1	40	9	0.1 - 70.1 (1.6)
subtype2	39	3	0.1 - 65.1 (0.9)
subtype3	32	3	0.1 - 72.2 (1.0)
subtype4	33	5	0.3 - 55.0 (3.6)

Figure S41. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0277 (ANOVA), Q value = 1

Table S47. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	177	66.6 (11.0)
subtype1	58	68.1 (10.4)
subtype2	46	62.5 (12.5)
subtype3	38	68.6 (10.5)
subtype4	35	67.4 (9.0)

Figure S42. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.238 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	72	112
subtype1	29	30
subtype2	18	28
subtype3	13	29
subtype4	12	25

Figure S43. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.000205 (Chi-square test), Q value = 0.016

Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: '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	28	89	38	11	11	3	2
subtype1	9	29	15	3	2	0	0
subtype2	15	19	5	1	5	0	0
subtype3	4	25	6	1	3	1	2
subtype4	0	16	12	6	1	2	0

Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.514 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	177
subtype1	1	58
subtype2	1	45
subtype3	3	39
subtype4	2	35

Figure S45. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.111 (Chi-square test), Q value = 1

Table S51. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	152	18	14
subtype1	48	9	2
subtype2	41	3	2
subtype3	33	2	7
subtype4	30	4	3

Figure S46. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0643 (Chi-square test), Q value = 1

Table S52. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	N3A	NX
ALL	58	63	29	14	9	11
subtype1	16	21	9	7	0	6
subtype2	12	16	8	3	6	1
subtype3	20	14	3	2	2	1
subtype4	10	12	9	2	1	3

Figure S47. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.105 (Chi-square test), Q value = 1

Table S53. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	126	9	12	25
subtype1	33	3	8	12
subtype2	34	2	1	5
subtype3	32	3	0	5
subtype4	27	1	3	3

Figure S48. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.143 (ANOVA), Q value = 1

Table S54. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	162	4.7 (6.6)
subtype1	47	5.9 (9.0)
subtype2	43	5.2 (5.2)
subtype3	41	2.8 (5.0)
subtype4	31	4.8 (5.3)

Figure S49. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0523 (Chi-square test), Q value = 1

Table S55. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	5	19	29	8	14	4	38	13	7	29
subtype1	0	2	5	12	2	2	0	13	2	1	12
subtype2	0	0	4	6	3	3	1	10	5	5	6
subtype3	2	3	7	4	3	3	3	8	3	0	4
subtype4	0	0	3	7	0	6	0	7	3	1	7

Figure S50. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S56. Get Full Table Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	45	60	79

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.554 (logrank test), Q value = 1

Table S57. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	144	20	0.1 - 72.2 (1.3)
subtype1	38	5	0.3 - 55.0 (3.3)
subtype2	46	4	0.1 - 72.2 (0.9)
subtype3	60	11	0.1 - 70.1 (1.6)

Figure S51. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.435 (ANOVA), Q value = 1

Table S58. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	177	66.6 (11.0)
subtype1	43	67.6 (8.9)
subtype2	56	65.1 (13.4)
subtype3	78	67.2 (10.1)

Figure S52. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.873 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	72	112
subtype1	16	29
subtype2	24	36
subtype3	32	47

Figure S53. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.000392 (Chi-square test), Q value = 0.031

Table S60. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: '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	28	89	38	11	11	3	2
subtype1	2	19	14	7	1	2	0
subtype2	14	27	7	1	8	1	0
subtype3	12	43	17	3	2	0	2

Figure S54. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0172 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	177
subtype1	4	41
subtype2	3	57
subtype3	0	79

Figure S55. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.994 (Chi-square test), Q value = 1

Table S62. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	152	18	14
subtype1	37	4	4
subtype2	50	6	4
subtype3	65	8	6

Figure S56. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.834 (Chi-square test), Q value = 1

Table S63. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	N3A	NX
ALL	58	63	29	14	9	11
subtype1	11	16	9	4	2	3
subtype2	20	19	10	4	5	2
subtype3	27	28	10	6	2	6

Figure S57. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.408 (Chi-square test), Q value = 1

Table S64. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	126	9	12	25
subtype1	35	1	3	3
subtype2	43	3	3	7
subtype3	48	5	6	15

Figure S58. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.806 (ANOVA), Q value = 1

Table S65. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	162	4.7 (6.6)
subtype1	39	5.1 (5.6)
subtype2	55	4.3 (5.1)
subtype3	68	4.9 (8.0)

Figure S59. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.794 (Chi-square test), Q value = 1

Table S66. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	5	19	29	8	14	4	38	13	7	29
subtype1	0	0	3	8	2	6	1	8	3	2	9
subtype2	1	3	6	7	3	3	0	13	5	4	11
subtype3	1	2	10	14	3	5	3	17	5	1	9

Figure S60. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S67. Get Full Table Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	51	41	45

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

P value = 0.515 (logrank test), Q value = 1

Table S68. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	127	14	0.1 - 72.2 (1.1)
subtype1	48	8	0.1 - 70.1 (1.3)
subtype2	39	3	0.1 - 59.0 (0.9)
subtype3	40	3	0.1 - 72.2 (1.1)

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0253 (ANOVA), Q value = 1

Table S69. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	130	65.5 (10.9)
subtype1	49	67.2 (11.3)
subtype2	41	61.7 (12.3)
subtype3	40	67.4 (7.8)

Figure S62. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.697 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	50	87
subtype1	21	30
subtype2	14	27
subtype3	15	30

Figure S63. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000791 (Chi-square test), Q value = 0.062

Table S71. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: '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	23	60	29	10	10	3	2
subtype1	5	21	17	5	1	1	1
subtype2	16	15	3	2	5	0	0
subtype3	2	24	9	3	4	2	1

Figure S64. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.135 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	130
subtype1	1	50
subtype2	1	40
subtype3	5	40

Figure S65. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.0654 (Chi-square test), Q value = 1

Table S73. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	115	11	11
subtype1	42	7	2
subtype2	36	3	2
subtype3	37	1	7

Figure S66. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.241 (Chi-square test), Q value = 1

Table S74. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	N3A
ALL	50	46	23	10	8
subtype1	20	16	9	6	0
subtype2	11	15	8	2	5
subtype3	19	15	6	2	3

Figure S67. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0437 (Chi-square test), Q value = 1

Table S75. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	112	8	5
subtype1	39	2	5
subtype2	35	2	0
subtype3	38	4	0

Figure S68. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.144 (ANOVA), Q value = 1

Table S76. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	128	4.4 (5.9)
subtype1	44	5.2 (7.2)
subtype2	39	5.0 (5.4)
subtype3	45	3.0 (4.5)

Figure S69. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.586 (Chi-square test), Q value = 1

Table S77. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	4	13	26	8	13	3	32	12	6	18
subtype1	1	1	6	13	3	4	0	11	2	1	9
subtype2	0	0	3	5	2	4	1	12	5	4	5
subtype3	1	3	4	8	3	5	2	9	5	1	4

Figure S70. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S78. Get Full Table Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	32	50	55

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

P value = 0.823 (logrank test), Q value = 1

Table S79. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	127	14	0.1 - 72.2 (1.1)
subtype1	30	2	0.3 - 31.0 (1.2)
subtype2	48	4	0.1 - 65.1 (0.9)
subtype3	49	8	0.1 - 72.2 (1.4)

Figure S71. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.117 (ANOVA), Q value = 1

Table S80. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	130	65.5 (10.9)
subtype1	30	66.2 (8.0)
subtype2	50	63.1 (11.6)
subtype3	50	67.5 (11.5)

Figure S72. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.499 (Fisher's exact test), Q value = 1

Table S81. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	50	87
subtype1	12	20
subtype2	21	29
subtype3	17	38

Figure S73. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0635 (Chi-square test), Q value = 1

Table S82. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: '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	23	60	29	10	10	3	2
subtype1	1	15	8	5	1	2	0
subtype2	14	19	9	1	6	0	1
subtype3	8	26	12	4	3	1	1

Figure S74. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0702 (Fisher's exact test), Q value = 1

Table S83. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	7	130
subtype1	3	29
subtype2	0	50
subtype3	4	51

Figure S75. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.69 (Chi-square test), Q value = 1

Table S84. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	115	11	11
subtype1	28	1	3
subtype2	40	6	4
subtype3	47	4	4

Figure S76. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'DISTANT.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.585 (Chi-square test), Q value = 1

Table S85. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N2	N3	N3A
ALL	50	46	23	10	8
subtype1	10	14	6	1	1
subtype2	20	12	9	4	5
subtype3	20	20	8	5	2

Figure S77. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.659 (Chi-square test), Q value = 1

Table S86. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	112	8	5
subtype1	28	1	0
subtype2	40	3	2
subtype3	44	4	3

Figure S78. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.623 (ANOVA), Q value = 1

Table S87. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	128	4.4 (5.9)
subtype1	30	3.5 (3.9)
subtype2	47	4.8 (5.5)
subtype3	51	4.5 (7.1)

Figure S79. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.218 (Chi-square test), Q value = 1

Table S88. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV
ALL	2	4	13	26	8	13	3	32	12	6	18
subtype1	0	0	2	8	2	5	1	7	2	1	4
subtype2	0	0	5	6	5	5	1	10	5	5	8
subtype3	2	4	6	12	1	3	1	15	5	0	6

Figure S80. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = STAD-TP.mergedcluster.txt
Clinical data file = STAD-TP.clin.merged.picked.txt
Number of patients = 189
Number of clustering approaches = 8
Number of selected clinical features = 11
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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' 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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] 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)

[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[7] 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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