Stomach Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

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 10 clinical features across 159 patients, 4 significant findings detected with P value < 0.05.

5 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death' and 'GENDER'.
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 6 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.
CNMF clustering analysis on sequencing-based miR expression data identified 5 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that 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 10 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 4 significant findings detected.


Clinical Features	Statistical Tests	CN CNMF	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRseq CNMF subtypes	MIRseq cHierClus subtypes
Time to Death	logrank test	0.0105	0.165	0.56	0.676	0.158	0.612
AGE	ANOVA	0.178	0.183	0.358	0.591	0.38	0.833
GENDER	Fisher's exact test	0.0388	0.911	0.59	0.362	0.275	0.514
HISTOLOGICAL TYPE	Chi-square test	0.104	0.463	0.363	0.175	0.031	0.00316
PATHOLOGY T	Chi-square test	0.279	0.0825	0.304	0.578	0.768	0.545
PATHOLOGY N	Chi-square test	0.117	0.753	0.509	0.148	0.564	0.433
PATHOLOGICSPREAD(M)	Chi-square test	0.909	0.819	0.725	0.496	0.445	0.885
TUMOR STAGE	Chi-square test	0.924	0.21	0.746	0.0898	0.0727	0.194
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.316	0.81			0.0665	0.132
NEOADJUVANT THERAPY	Fisher's exact test	0.0539	0.873			0.23	0.797

Clustering Approach #1: 'CN CNMF'

Table S1. Get Full Table Description of clustering approach #1: 'CN CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	68	16	12	26	36

'CN CNMF' versus 'Time to Death'

P value = 0.0105 (logrank test)

Table S2. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	105	15	0.1 - 72.2 (2.9)
subtype1	46	7	0.1 - 70.1 (1.0)
subtype2	14	2	0.1 - 53.0 (3.8)
subtype3	4	2	0.2 - 22.0 (7.7)
subtype4	17	0	0.2 - 55.0 (6.9)
subtype5	24	4	0.3 - 72.2 (3.8)

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

'CN CNMF' versus 'AGE'

P value = 0.178 (ANOVA)

Table S3. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	151	67.9 (10.7)
subtype1	66	67.5 (12.0)
subtype2	16	71.1 (7.4)
subtype3	11	70.8 (8.8)
subtype4	25	64.0 (10.0)
subtype5	33	69.3 (9.7)

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

'CN CNMF' versus 'GENDER'

P value = 0.0388 (Chi-square test)

Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	60	98
subtype1	31	37
subtype2	5	11
subtype3	8	4
subtype4	7	19
subtype5	9	27

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

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.104 (Chi-square test)

Table S5. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	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	16	89	7	3	10	28
subtype1	7	44	2	1	1	11
subtype2	2	6	0	0	1	7
subtype3	0	8	1	0	0	3
subtype4	4	12	1	0	3	5
subtype5	3	19	3	2	5	2

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

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.279 (Chi-square test)

Table S6. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	52	61	18
subtype1	3	20	29	8
subtype2	0	5	8	2
subtype3	2	2	4	1
subtype4	1	12	5	3
subtype5	0	13	15	4

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

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.117 (Chi-square test)

Table S7. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	44	57	24	15
subtype1	21	26	11	5
subtype2	9	1	2	3
subtype3	1	7	1	0
subtype4	5	11	5	2
subtype5	8	12	5	5

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

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.909 (Chi-square test)

Table S8. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	134	16	8
subtype1	56	8	4
subtype2	15	1	0
subtype3	11	1	0
subtype4	23	2	1
subtype5	29	4	3

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

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.924 (Chi-square test)

Table S9. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	24	37	43	26
subtype1	11	16	20	11
subtype2	4	6	3	2
subtype3	1	1	4	1
subtype4	3	6	8	4
subtype5	5	8	8	8

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

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.316 (Chi-square test)

Table S10. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	153
subtype1	1	67
subtype2	0	16
subtype3	0	12
subtype4	1	25
subtype5	3	33

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

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.0539 (Chi-square test)

Table S11. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	11	147
subtype1	2	66
subtype2	0	16
subtype3	0	12
subtype4	4	22
subtype5	5	31

Figure S10. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

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	26	21	20	28

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.165 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	86	10	0.1 - 72.2 (1.4)
subtype1	24	4	0.1 - 65.1 (1.8)
subtype2	20	0	0.1 - 55.0 (1.2)
subtype3	17	4	0.4 - 72.2 (3.6)
subtype4	25	2	0.1 - 70.1 (1.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.183 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	88	67.1 (10.9)
subtype1	24	70.5 (11.1)
subtype2	20	66.0 (7.1)
subtype3	16	68.5 (11.1)
subtype4	28	64.2 (12.5)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.911 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	31	64
subtype1	8	18
subtype2	8	13
subtype3	7	13
subtype4	8	20

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.463 (Chi-square test)

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 - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	14	44	6	3	9	19
subtype1	4	8	2	1	3	8
subtype2	2	10	0	1	4	4
subtype3	2	10	1	1	2	4
subtype4	6	16	3	0	0	3

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0825 (Chi-square test)

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	4	37	45	9
subtype1	4	11	8	3
subtype2	0	7	12	2
subtype3	0	10	8	2
subtype4	0	9	17	2

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.753 (Chi-square test)

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	37	31	17	10
subtype1	13	9	3	1
subtype2	5	8	4	4
subtype3	8	6	4	2
subtype4	11	8	6	3

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.819 (Chi-square test)

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	81	9	5
subtype1	24	1	1
subtype2	18	2	1
subtype3	16	2	2
subtype4	23	4	1

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.21 (Chi-square test)

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	17	33	30	15
subtype1	10	8	5	3
subtype2	1	7	9	4
subtype3	3	7	7	3
subtype4	3	11	9	5

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

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

P value = 0.81 (Fisher's exact test)

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	90
subtype1	1	25
subtype2	1	20
subtype3	2	18
subtype4	1	27

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.873 (Fisher's exact test)

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	11	84
subtype1	2	24
subtype2	3	18
subtype3	3	17
subtype4	3	25

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

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	6
Number of samples	11	6	7	11	12	10

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.56 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	18	5	1.0 - 54.0 (14.0)
subtype1	2	0	1.0 - 12.0 (6.5)
subtype2	2	0	1.0 - 18.9 (10.0)
subtype3	1	0	3.0 - 3.0 (3.0)
subtype4	2	1	1.0 - 15.9 (8.5)
subtype5	4	2	4.0 - 30.0 (20.5)
subtype6	7	2	1.0 - 54.0 (27.0)

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 = 0.358 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	57	69.5 (10.1)
subtype1	11	67.0 (12.3)
subtype2	6	67.3 (12.0)
subtype3	7	66.7 (8.1)
subtype4	11	75.6 (9.5)
subtype5	12	69.2 (9.0)
subtype6	10	68.9 (9.1)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.59 (Chi-square test)

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

nPatients	FEMALE	MALE
ALL	27	30
subtype1	5	6
subtype2	4	2
subtype3	5	2
subtype4	5	6
subtype5	4	8
subtype6	4	6

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 = 0.363 (Chi-square test)

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 - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	2	39	1	1	9
subtype1	0	9	0	0	0
subtype2	0	4	0	0	2
subtype3	1	6	0	0	0
subtype4	0	10	0	0	1
subtype5	0	6	1	1	3
subtype6	1	4	0	0	3

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.304 (Chi-square test)

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	2	14	14	9
subtype1	0	1	2	3
subtype2	0	2	3	0
subtype3	1	1	0	2
subtype4	0	3	3	0
subtype5	1	2	4	1
subtype6	0	5	2	3

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.509 (Chi-square test)

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

nPatients	N0	N1	N2	N3
ALL	8	20	6	5
subtype1	1	5	1	1
subtype2	2	2	1	0
subtype3	1	1	0	1
subtype4	1	7	1	0
subtype5	1	4	0	1
subtype6	2	1	3	2

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

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

P value = 0.725 (Chi-square test)

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	48	7	2
subtype1	10	1	0
subtype2	5	0	1
subtype3	6	1	0
subtype4	10	1	0
subtype5	9	2	1
subtype6	8	2	0

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.746 (Chi-square test)

Table S31. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	7	4	10	11
subtype1	0	1	2	3
subtype2	2	0	2	0
subtype3	1	0	1	1
subtype4	1	1	2	1
subtype5	1	2	1	2
subtype6	2	0	2	4

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S32. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	11	18	17	11

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.676 (logrank test)

Table S33. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	18	5	1.0 - 54.0 (14.0)
subtype1	2	0	1.0 - 27.0 (14.0)
subtype2	5	1	1.0 - 15.9 (3.0)
subtype3	8	3	1.0 - 54.0 (14.0)
subtype4	3	1	19.1 - 30.0 (22.0)

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.591 (ANOVA)

Table S34. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	57	69.5 (10.1)
subtype1	11	72.1 (10.9)
subtype2	18	70.1 (11.5)
subtype3	17	69.3 (8.7)
subtype4	11	66.2 (9.4)

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.362 (Fisher's exact test)

Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	30
subtype1	7	4
subtype2	8	10
subtype3	9	8
subtype4	3	8

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.175 (Chi-square test)

Table S36. 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 - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	2	39	1	1	9
subtype1	0	9	0	0	2
subtype2	1	15	0	0	0
subtype3	1	9	0	0	5
subtype4	0	6	1	1	2

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.578 (Chi-square test)

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	2	14	14	9
subtype1	0	3	4	0
subtype2	0	2	3	4
subtype3	1	5	4	4
subtype4	1	4	3	1

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.148 (Chi-square test)

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	8	20	6	5
subtype1	3	5	1	0
subtype2	0	8	2	2
subtype3	3	2	3	3
subtype4	2	5	0	0

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.496 (Chi-square test)

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	48	7	2
subtype1	10	0	1
subtype2	15	3	0
subtype3	14	3	0
subtype4	9	1	1

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.0898 (Chi-square test)

Table S40. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	7	4	10	11
subtype1	3	0	3	0
subtype2	0	2	2	5
subtype3	2	0	4	5
subtype4	2	2	1	1

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #5: 'MIRseq CNMF subtypes'

Table S41. Get Full Table Description of clustering approach #5: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	29	43	24	24	30

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.158 (logrank test)

Table S42. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	97	15	0.1 - 72.2 (3.0)
subtype1	27	5	0.3 - 70.1 (1.4)
subtype2	15	4	0.1 - 47.0 (7.1)
subtype3	18	2	0.3 - 54.0 (4.4)
subtype4	16	2	0.1 - 72.2 (1.0)
subtype5	21	2	0.1 - 65.1 (1.7)

Figure S37. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.38 (ANOVA)

Table S43. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	143	67.8 (10.7)
subtype1	28	65.8 (11.6)
subtype2	43	69.7 (9.4)
subtype3	22	67.8 (8.9)
subtype4	20	69.8 (11.2)
subtype5	30	65.6 (12.4)

Figure S38. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.275 (Chi-square test)

Table S44. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	59	91
subtype1	12	17
subtype2	22	21
subtype3	6	18
subtype4	9	15
subtype5	10	20

Figure S39. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.031 (Chi-square test)

Table S45. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	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	14	85	7	3	10	26
subtype1	4	13	1	0	2	9
subtype2	2	30	1	0	2	5
subtype3	1	11	0	2	4	6
subtype4	1	13	3	1	2	3
subtype5	6	18	2	0	0	3

Figure S40. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.768 (Chi-square test)

Table S46. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	51	55	17
subtype1	1	10	13	4
subtype2	2	13	14	5
subtype3	0	9	9	2
subtype4	3	10	7	2
subtype5	0	9	12	4

Figure S41. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.564 (Chi-square test)

Table S47. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	43	54	22	13
subtype1	10	11	5	2
subtype2	7	16	7	3
subtype3	9	7	4	0
subtype4	9	10	1	3
subtype5	8	10	5	5

Figure S42. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.445 (Chi-square test)

Table S48. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	130	14	6
subtype1	26	3	0
subtype2	36	5	2
subtype3	21	2	1
subtype4	20	1	3
subtype5	27	3	0

Figure S43. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0727 (Chi-square test)

Table S49. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	24	35	38	25
subtype1	2	12	10	4
subtype2	6	3	12	7
subtype3	4	8	4	3
subtype4	9	4	5	4
subtype5	3	8	7	7

Figure S44. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.0665 (Chi-square test)

Table S50. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	145
subtype1	0	29
subtype2	0	43
subtype3	1	23
subtype4	3	21
subtype5	1	29

Figure S45. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.23 (Chi-square test)

Table S51. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	11	139
subtype1	2	27
subtype2	0	43
subtype3	3	21
subtype4	3	21
subtype5	3	27

Figure S46. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq cHierClus subtypes'

Table S52. Get Full Table Description of clustering approach #6: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	52	55	43

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.612 (logrank test)

Table S53. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	97	15	0.1 - 72.2 (3.0)
subtype1	35	5	0.1 - 72.2 (1.1)
subtype2	34	6	0.1 - 53.0 (3.8)
subtype3	28	4	0.3 - 55.0 (4.1)

Figure S47. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.833 (ANOVA)

Table S54. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	143	67.8 (10.7)
subtype1	50	67.1 (12.3)
subtype2	53	68.4 (10.6)
subtype3	40	67.9 (8.8)

Figure S48. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.514 (Fisher's exact test)

Table S55. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	59	91
subtype1	18	34
subtype2	25	30
subtype3	16	27

Figure S49. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00316 (Chi-square test)

Table S56. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	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	14	85	7	3	10	26
subtype1	6	33	4	1	0	6
subtype2	6	32	2	0	1	11
subtype3	2	20	1	2	9	9

Figure S50. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.545 (Chi-square test)

Table S57. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	51	55	17
subtype1	2	15	18	8
subtype2	4	20	20	5
subtype3	0	16	17	4

Figure S51. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.433 (Chi-square test)

Table S58. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	43	54	22	13
subtype1	16	22	7	2
subtype2	17	21	7	5
subtype3	10	11	8	6

Figure S52. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.885 (Chi-square test)

Table S59. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	130	14	6
subtype1	44	6	2
subtype2	48	4	3
subtype3	38	4	1

Figure S53. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.194 (Chi-square test)

Table S60. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	24	35	38	25
subtype1	9	13	11	10
subtype2	11	9	19	6
subtype3	4	13	8	9

Figure S54. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.132 (Fisher's exact test)

Table S61. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	145
subtype1	2	50
subtype2	0	55
subtype3	3	40

Figure S55. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.797 (Fisher's exact test)

Table S62. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	11	139
subtype1	4	48
subtype2	3	52
subtype3	4	39

Figure S56. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'

Methods & Data

Input

Cluster data file = STAD.mergedcluster.txt
Clinical data file = STAD.clin.merged.picked.txt
Number of patients = 159
Number of clustering approaches = 6
Number of selected clinical features = 10
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

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

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

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

[6] 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)

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