Correlation between molecular cancer subtypes and selected clinical features

Stomach Adenocarcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Stomach Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1C53HVR

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 6 different clustering approaches and 11 clinical features across 181 patients, 6 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 correlate to 'AGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.
3 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'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 6 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, 6 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
Time to Death	logrank test	0.331 (1.00)	0.798 (1.00)	0.751 (1.00)	0.512 (1.00)	0.75 (1.00)	0.651 (1.00)
AGE	ANOVA	0.108 (1.00)	0.000779 (0.0506)	0.368 (1.00)	0.00246 (0.152)	0.00956 (0.554)	0.367 (1.00)
GENDER	Fisher's exact test	0.00486 (0.292)	1 (1.00)	0.691 (1.00)	0.728 (1.00)	0.316 (1.00)	0.955 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.148 (1.00)	0.0472 (1.00)	0.0122 (0.684)	0.00339 (0.207)	1.14e-06 (7.56e-05)	0.00112 (0.0714)
PATHOLOGY T	Chi-square test	0.486 (1.00)	0.0147 (0.808)	0.00512 (0.302)	0.0157 (0.842)	0.856 (1.00)	0.0557 (1.00)
PATHOLOGY N	Chi-square test	0.405 (1.00)	0.772 (1.00)	0.633 (1.00)	0.884 (1.00)	0.559 (1.00)	0.903 (1.00)
PATHOLOGICSPREAD(M)	Chi-square test	0.475 (1.00)	0.466 (1.00)	0.747 (1.00)	0.826 (1.00)	0.0929 (1.00)	0.726 (1.00)
TUMOR STAGE	Chi-square test	0.445 (1.00)	0.0201 (1.00)	0.322 (1.00)	0.0939 (1.00)	0.443 (1.00)	0.304 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.447 (1.00)	0.192 (1.00)	0.00139 (0.0874)	0.149 (1.00)	0.0192 (1)	0.268 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.404 (1.00)	0.435 (1.00)	0.485 (1.00)	0.815 (1.00)	0.0108 (0.617)	0.386 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.398 (1.00)	0.716 (1.00)	0.068 (1.00)	0.103 (1.00)	0.0156 (0.842)	0.908 (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	80	17	12	71

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

P value = 0.331 (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	143	19	0.1 - 72.2 (1.2)
subtype1	64	8	0.1 - 70.1 (1.0)
subtype2	14	3	0.1 - 53.0 (7.6)
subtype3	8	2	0.8 - 21.1 (1.3)
subtype4	57	6	0.2 - 72.2 (3.0)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.108 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	173	66.8 (11.1)
subtype1	79	64.9 (12.5)
subtype2	15	70.0 (6.6)
subtype3	12	71.7 (9.6)
subtype4	67	67.5 (10.2)

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 = 0.00486 (Fisher's exact test), Q value = 0.29

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

nPatients	FEMALE	MALE
ALL	72	108
subtype1	41	39
subtype2	3	14
subtype3	7	5
subtype4	21	50

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 = 0.148 (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 MUCINOUS TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	10	16	8	3	1	16	73	7	3	10	28
subtype1	9	8	4	2	1	6	34	2	1	1	10
subtype2	0	1	0	0	0	4	5	0	0	1	6
subtype3	1	0	0	0	0	0	7	1	0	0	3
subtype4	0	7	4	1	0	6	27	4	2	8	9

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 'PATHOLOGY.T'

P value = 0.486 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	52	56	18
subtype1	2	17	23	9
subtype2	2	7	5	2
subtype3	1	3	6	0
subtype4	1	25	22	7

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.405 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	44	49	23	15
subtype1	19	20	9	4
subtype2	7	2	2	4
subtype3	3	4	2	0
subtype4	15	23	10	7

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.475 (Chi-square test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	118	16	8
subtype1	43	9	4
subtype2	15	1	0
subtype3	11	0	0
subtype4	49	6	4

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.445 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	24	37	39	26
subtype1	10	13	15	11
subtype2	5	3	4	3
subtype3	0	4	5	0
subtype4	9	17	15	12

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

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

P value = 0.447 (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	5	175
subtype1	1	79
subtype2	0	17
subtype3	0	12
subtype4	4	67

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

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

P value = 0.404 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	125	7	12	25
subtype1	55	2	6	13
subtype2	12	2	1	1
subtype3	8	0	0	4
subtype4	50	3	5	7

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

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

P value = 0.398 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	136	4.0 (5.6)
subtype1	67	3.3 (4.5)
subtype2	8	6.2 (7.4)
subtype3	8	3.2 (4.0)
subtype4	53	4.6 (6.7)

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S13. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	18	30	39	46

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.798 (logrank test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	124	14	0.1 - 72.2 (1.0)
subtype1	18	0	0.1 - 8.7 (0.9)
subtype2	28	4	0.1 - 65.1 (1.0)
subtype3	37	5	0.1 - 70.1 (1.0)
subtype4	41	5	0.1 - 72.2 (1.5)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000779 (ANOVA), Q value = 0.051

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

	nPatients	Mean (Std.Dev)
ALL	126	65.6 (11.2)
subtype1	17	62.8 (10.0)
subtype2	28	71.1 (10.0)
subtype3	39	60.9 (11.9)
subtype4	42	67.4 (9.7)

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

'METHLYATION CNMF' versus 'GENDER'

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

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	50	83
subtype1	7	11
subtype2	11	19
subtype3	15	24
subtype4	17	29

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0472 (Chi-square test), Q value = 1

Table S17. 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 MUCINOUS TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	10	16	8	3	1	14	44	6	3	9	19
subtype1	1	5	1	0	0	3	6	0	0	1	1
subtype2	1	6	1	1	0	2	7	2	1	2	7
subtype3	8	1	2	2	1	6	13	2	0	0	4
subtype4	0	4	4	0	0	3	18	2	2	6	7

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0147 (Chi-square test), Q value = 0.81

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	4	37	45	9
subtype1	0	2	7	2
subtype2	4	10	6	1
subtype3	0	8	14	3
subtype4	0	17	18	3

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.772 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	37	31	17	10
subtype1	4	3	2	2
subtype2	12	6	2	1
subtype3	8	9	6	2
subtype4	13	13	7	5

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.466 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	81	9	5
subtype1	8	2	1
subtype2	19	1	1
subtype3	21	4	0
subtype4	33	2	3

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.0201 (Chi-square test), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	17	33	30	15
subtype1	1	2	6	2
subtype2	10	6	3	2
subtype3	2	10	8	5
subtype4	4	15	13	6

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.192 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	128
subtype1	0	18
subtype2	1	29
subtype3	0	39
subtype4	4	42

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.435 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	110	6	5
subtype1	15	0	1
subtype2	29	0	1
subtype3	30	4	1
subtype4	36	2	2

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.716 (ANOVA), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	122	4.2 (5.9)
subtype1	16	5.6 (6.6)
subtype2	29	3.5 (6.9)
subtype3	36	4.1 (5.5)
subtype4	41	4.1 (5.2)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S25. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	28	29	22	29	39

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.751 (logrank test), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	15	0.1 - 72.2 (1.4)
subtype1	20	2	0.1 - 70.1 (1.0)
subtype2	23	2	0.1 - 59.0 (1.2)
subtype3	15	2	0.2 - 55.0 (3.0)
subtype4	23	5	0.2 - 53.0 (3.6)
subtype5	32	4	0.3 - 72.2 (3.2)

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.368 (ANOVA), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	140	67.2 (10.7)
subtype1	27	68.4 (11.0)
subtype2	29	64.4 (11.7)
subtype3	19	66.2 (12.2)
subtype4	28	70.0 (10.3)
subtype5	37	67.1 (9.0)

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.691 (Chi-square test), Q value = 1

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	53	94
subtype1	13	15
subtype2	11	18
subtype3	6	16
subtype4	10	19
subtype5	13	26

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0122 (Chi-square test), Q value = 0.68

Table S29. 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 MUCINOUS TYPE	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	5	6	4	2	16	67	6	3	10	23
subtype1	0	4	0	0	4	16	0	0	1	3
subtype2	5	0	1	1	3	15	2	0	0	1
subtype3	0	0	0	0	3	9	2	1	1	5
subtype4	0	1	0	0	3	10	1	1	3	8
subtype5	0	1	3	1	3	17	1	1	5	6

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00512 (Chi-square test), Q value = 0.3

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	48	50	17
subtype1	0	9	9	2
subtype2	0	2	14	4
subtype3	0	12	5	4
subtype4	5	13	7	3
subtype5	1	12	15	4

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.633 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	41	44	20	14
subtype1	8	8	2	2
subtype2	6	9	4	2
subtype3	4	10	3	5
subtype4	13	6	5	2
subtype5	10	11	6	3

Figure S28. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.747 (Chi-square test), Q value = 1

Table S32. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	109	14	7
subtype1	21	2	1
subtype2	19	2	1
subtype3	16	5	1
subtype4	25	2	1
subtype5	28	3	3

Figure S29. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.322 (Chi-square test), Q value = 1

Table S33. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	23	33	35	24
subtype1	4	5	6	3
subtype2	0	7	8	5
subtype3	3	5	6	7
subtype4	10	6	7	3
subtype5	6	10	8	6

Figure S30. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.00139 (Chi-square test), Q value = 0.087

Table S34. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	142
subtype1	0	28
subtype2	0	29
subtype3	4	18
subtype4	0	29
subtype5	1	38

Figure S31. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.485 (Chi-square test), Q value = 1

Table S35. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	98	6	10	22
subtype1	18	0	2	7
subtype2	18	1	0	5
subtype3	14	2	4	2
subtype4	21	1	2	4
subtype5	27	2	2	4

Figure S32. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.068 (ANOVA), Q value = 1

Table S36. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	105	4.3 (6.1)
subtype1	18	4.1 (8.7)
subtype2	24	5.5 (4.9)
subtype3	17	6.4 (7.1)
subtype4	19	1.0 (1.8)
subtype5	27	4.2 (5.5)

Figure S33. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S37. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	46	59	42

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.512 (logrank test), Q value = 1

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	15	0.1 - 72.2 (1.4)
subtype1	36	2	0.1 - 59.0 (1.0)
subtype2	42	8	0.1 - 55.0 (2.5)
subtype3	35	5	0.3 - 72.2 (4.3)

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00246 (ANOVA), Q value = 0.15

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	140	67.2 (10.7)
subtype1	45	64.1 (10.6)
subtype2	55	71.0 (10.6)
subtype3	40	65.6 (9.7)

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.728 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	53	94
subtype1	18	28
subtype2	22	37
subtype3	13	29

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00339 (Chi-square test), Q value = 0.21

Table S41. 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 MUCINOUS TYPE	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	5	6	4	2	16	67	6	3	10	23
subtype1	5	1	1	2	7	22	2	0	1	3
subtype2	0	3	0	0	2	26	3	2	5	16
subtype3	0	2	3	0	7	19	1	1	4	4

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.0157 (Chi-square test), Q value = 0.84

Table S42. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	48	50	17
subtype1	0	9	20	5
subtype2	5	27	12	7
subtype3	1	12	18	5

Figure S38. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.884 (Chi-square test), Q value = 1

Table S43. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	41	44	20	14
subtype1	10	14	6	4
subtype2	21	16	8	7
subtype3	10	14	6	3

Figure S39. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.826 (Chi-square test), Q value = 1

Table S44. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	109	14	7
subtype1	31	5	1
subtype2	47	6	3
subtype3	31	3	3

Figure S40. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0939 (Chi-square test), Q value = 1

Table S45. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	23	33	35	24
subtype1	2	10	13	8
subtype2	16	13	10	10
subtype3	5	10	12	6

Figure S41. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.149 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	142
subtype1	0	46
subtype2	4	55
subtype3	1	41

Figure S42. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.815 (Chi-square test), Q value = 1

Table S47. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	98	6	10	22
subtype1	28	2	2	8
subtype2	40	2	6	10
subtype3	30	2	2	4

Figure S43. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.103 (ANOVA), Q value = 1

Table S48. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	105	4.3 (6.1)
subtype1	36	5.5 (5.6)
subtype2	37	2.6 (4.9)
subtype3	32	4.8 (7.4)

Figure S44. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S49. Get Full Table Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	81	39	47

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.75 (logrank test), Q value = 1

Table S50. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	130	18	0.1 - 72.2 (1.5)
subtype1	60	11	0.1 - 70.1 (2.5)
subtype2	32	3	0.1 - 59.0 (1.3)
subtype3	38	4	0.1 - 72.2 (1.3)

Figure S45. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00956 (ANOVA), Q value = 0.55

Table S51. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	160	67.5 (10.7)
subtype1	80	68.7 (10.5)
subtype2	39	63.0 (11.2)
subtype3	41	69.3 (9.5)

Figure S46. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.316 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	66	101
subtype1	37	44
subtype2	13	26
subtype3	16	31

Figure S47. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.14e-06 (Chi-square test), Q value = 7.6e-05

Table S53. 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 MUCINOUS TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	6	7	8	2	1	16	73	8	3	10	28
subtype1	1	0	2	0	0	8	39	2	0	5	20
subtype2	5	0	2	2	0	7	17	2	0	1	2
subtype3	0	7	4	0	1	1	17	4	3	4	6

Figure S48. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.856 (Chi-square test), Q value = 1

Table S54. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	52	57	18
subtype1	4	30	29	10
subtype2	0	9	14	4
subtype3	2	13	14	4

Figure S49. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.559 (Chi-square test), Q value = 1

Table S55. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	45	49	23	15
subtype1	24	26	12	9
subtype2	7	11	6	5
subtype3	14	12	5	1

Figure S50. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0929 (Chi-square test), Q value = 1

Table S56. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	119	16	8
subtype1	62	13	3
subtype2	27	2	1
subtype3	30	1	4

Figure S51. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

P value = 0.443 (Chi-square test), Q value = 1

Table S57. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	24	38	39	26
subtype1	13	19	21	15
subtype2	2	8	11	6
subtype3	9	11	7	5

Figure S52. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.0192 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	162
subtype1	0	81
subtype2	1	38
subtype3	4	43

Figure S53. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0108 (Chi-square test), Q value = 0.62

Table S59. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	111	7	12	25
subtype1	45	3	11	17
subtype2	28	2	0	5
subtype3	38	2	1	3

Figure S54. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0156 (ANOVA), Q value = 0.84

Table S60. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	122	4.2 (5.8)
subtype1	49	4.5 (7.0)
subtype2	34	6.0 (5.5)
subtype3	39	2.2 (3.4)

Figure S55. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S61. Get Full Table Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	38	42	87

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.651 (logrank test), Q value = 1

Table S62. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	130	18	0.1 - 72.2 (1.5)
subtype1	32	3	0.3 - 55.0 (3.8)
subtype2	31	3	0.1 - 65.1 (1.1)
subtype3	67	12	0.1 - 72.2 (1.5)

Figure S56. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.367 (ANOVA), Q value = 1

Table S63. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	160	67.5 (10.7)
subtype1	36	68.1 (8.7)
subtype2	40	65.4 (13.2)
subtype3	84	68.2 (10.1)

Figure S57. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.955 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	66	101
subtype1	14	24
subtype2	17	25
subtype3	35	52

Figure S58. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.00112 (Chi-square test), Q value = 0.071

Table S65. 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 MUCINOUS TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	6	7	8	2	1	16	73	8	3	10	28
subtype1	0	2	5	0	0	2	13	1	2	6	7
subtype2	5	1	0	2	0	4	22	3	0	0	3
subtype3	1	4	3	0	1	10	38	4	1	4	18

Figure S59. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.0557 (Chi-square test), Q value = 1

Table S66. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	52	57	18
subtype1	0	11	14	4
subtype2	0	7	16	7
subtype3	6	34	27	7

Figure S60. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.903 (Chi-square test), Q value = 1

Table S67. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	45	49	23	15
subtype1	8	9	7	4
subtype2	11	12	5	4
subtype3	26	28	11	7

Figure S61. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.726 (Chi-square test), Q value = 1

Table S68. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	119	16	8
subtype1	28	2	1
subtype2	27	4	3
subtype3	64	10	4

Figure S62. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.304 (Chi-square test), Q value = 1

Table S69. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	24	38	39	26
subtype1	3	12	7	6
subtype2	4	8	9	9
subtype3	17	18	23	11

Figure S63. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.268 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	162
subtype1	2	36
subtype2	2	40
subtype3	1	86

Figure S64. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.386 (Chi-square test), Q value = 1

Table S71. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	111	7	12	25
subtype1	29	1	2	3
subtype2	27	3	1	7
subtype3	55	3	9	15

Figure S65. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.908 (ANOVA), Q value = 1

Table S72. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	122	4.2 (5.8)
subtype1	26	4.1 (4.6)
subtype2	36	4.5 (5.1)
subtype3	60	4.0 (6.7)

Figure S66. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Cluster data file = STAD-TP.mergedcluster.txt
Clinical data file = STAD-TP.clin.merged.picked.txt
Number of patients = 181
Number of clustering approaches = 6
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)

Made with Nozzle