Stomach Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Stomach Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 9 clinical features across 162 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'PATHOLOGY.T'.
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 2 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'TUMOR.STAGE'.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that 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 6 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 2 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	0.142 (1.00)	0.734 (1.00)	0.667 (1.00)	0.8 (1.00)	0.223 (1.00)	0.895 (1.00)
AGE	ANOVA	0.129 (1.00)	0.18 (1.00)	0.143 (1.00)	0.447 (1.00)	0.119 (1.00)	0.599 (1.00)
GENDER	Fisher's exact test	0.0167 (0.804)	1 (1.00)	0.547 (1.00)	0.917 (1.00)	0.693 (1.00)	0.62 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.319 (1.00)	0.223 (1.00)	0.0766 (1.00)	0.407 (1.00)	0.109 (1.00)	0.0114 (0.571)
PATHOLOGY T	Chi-square test	0.00471 (0.24)	0.0164 (0.802)	0.242 (1.00)	0.425 (1.00)	0.181 (1.00)	0.265 (1.00)
PATHOLOGY N	Chi-square test	0.765 (1.00)	0.599 (1.00)	0.174 (1.00)	0.0334 (1.00)	0.755 (1.00)	0.112 (1.00)
PATHOLOGICSPREAD(M)	Chi-square test	0.186 (1.00)	0.4 (1.00)	0.327 (1.00)	0.474 (1.00)	0.48 (1.00)	0.691 (1.00)
TUMOR STAGE	Chi-square test	0.527 (1.00)	0.0836 (1.00)	0.0535 (1.00)	0.00304 (0.158)	0.246 (1.00)	0.295 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	1 (1.00)	0.792 (1.00)			0.0701 (1.00)	0.213 (1.00)

Clustering Approach #1: 'CN CNMF'

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

Cluster Labels	1	2	3
Number of samples	21	73	67

'CN CNMF' versus 'Time to Death'

P value = 0.142 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	124	16	0.1 - 72.2 (1.5)
subtype1	10	3	0.9 - 29.0 (8.8)
subtype2	57	7	0.1 - 72.2 (4.3)
subtype3	57	6	0.1 - 70.1 (1.0)

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

'CN CNMF' versus 'AGE'

P value = 0.129 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	154	67.7 (10.6)
subtype1	20	71.5 (9.1)
subtype2	69	68.2 (9.9)
subtype3	65	66.1 (11.5)

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

'CN CNMF' versus 'GENDER'

P value = 0.0167 (Fisher's exact test), Q value = 0.8

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

nPatients	FEMALE	MALE
ALL	65	96
subtype1	12	9
subtype2	21	52
subtype3	32	35

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

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.319 (Chi-square test), Q value = 1

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 ADENOCARCINOMA - SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	19	79	1	9	3	10	35
subtype1	1	14	0	1	0	0	5
subtype2	9	30	0	4	2	9	16
subtype3	9	35	1	4	1	1	14

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

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.00471 (Chi-square test), Q value = 0.24

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

nPatients	T1	T2	T3	T4
ALL	6	53	59	33
subtype1	3	8	5	2
subtype2	1	29	29	10
subtype3	2	16	25	21

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

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.765 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	50	57	24	19
subtype1	7	6	3	0
subtype2	22	25	12	10
subtype3	21	26	9	9

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

'CN CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.186 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	132	18	11
subtype1	21	0	0
subtype2	60	8	5
subtype3	51	10	6

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

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.527 (Chi-square test), Q value = 1

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

nPatients	I	II	III	IV
ALL	25	43	48	29
subtype1	3	5	7	0
subtype2	12	21	19	15
subtype3	10	17	22	14

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

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

nPatients	NO	YES
ALL	5	156
subtype1	0	21
subtype2	3	70
subtype3	2	65

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S11. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	24	11	29	33

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.734 (logrank test), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	11	0.1 - 72.2 (1.6)
subtype1	22	4	0.1 - 65.1 (1.8)
subtype2	11	0	0.1 - 8.7 (1.2)
subtype3	26	3	0.1 - 70.1 (1.1)
subtype4	29	4	0.2 - 72.2 (3.6)

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.18 (ANOVA), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	90	67.3 (10.9)
subtype1	22	70.4 (11.4)
subtype2	10	66.2 (8.1)
subtype3	29	64.0 (12.1)
subtype4	29	68.6 (9.6)

Figure S11. 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 S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	32	65
subtype1	8	16
subtype2	3	8
subtype3	10	19
subtype4	11	22

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.223 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH ADENOCARCINOMA - SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	15	44	1	6	3	9	19
subtype1	4	7	0	2	1	2	8
subtype2	3	6	0	0	0	1	1
subtype3	6	16	1	3	0	0	3
subtype4	2	15	0	1	2	6	7

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0164 (Chi-square test), Q value = 0.8

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	4	38	45	10
subtype1	4	11	6	3
subtype2	0	2	7	2
subtype3	0	9	17	3
subtype4	0	16	15	2

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.599 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	38	31	17	11
subtype1	13	8	2	1
subtype2	3	3	2	3
subtype3	11	9	6	3
subtype4	11	11	7	4

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.4 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	81	9	7
subtype1	22	1	1
subtype2	7	2	2
subtype3	24	4	1
subtype4	28	2	3

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.0836 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	18	33	31	15
subtype1	10	7	4	3
subtype2	1	2	6	2
subtype3	3	11	9	6
subtype4	4	13	12	4

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.792 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	92
subtype1	1	23
subtype2	0	11
subtype3	1	28
subtype4	3	30

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S21. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2
Number of samples	22	21

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.667 (logrank test), Q value = 1

Table S22. 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	7	2	1.0 - 22.0 (12.0)
subtype2	11	3	1.0 - 54.0 (18.9)

Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.143 (t-test), Q value = 1

Table S23. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	43	70.4 (10.5)
subtype1	22	68.1 (11.6)
subtype2	21	72.8 (8.8)

Figure S20. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.547 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	20	23
subtype1	9	13
subtype2	11	10

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0766 (Chi-square test), Q value = 1

Table S25. 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	25	1	1	9
subtype1	1	15	1	1	1
subtype2	1	10	0	0	8

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.242 (Chi-square test), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	2	14	9	9
subtype1	0	6	6	6
subtype2	2	8	3	3

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.174 (Chi-square test), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	8	14	5	5
subtype1	2	10	2	2
subtype2	6	4	3	3

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.327 (Chi-square test), Q value = 1

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	34	7	2
subtype1	16	4	2
subtype2	18	3	0

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0535 (Chi-square test), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	7	4	6	11
subtype1	1	4	3	6
subtype2	6	0	3	5

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S30. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	15	7	21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.8 (logrank test), Q value = 1

Table S31. 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	9	3	1.0 - 54.0 (18.9)
subtype2	2	0	1.0 - 30.0 (15.5)
subtype3	7	2	1.0 - 22.0 (12.0)

Figure S27. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.447 (ANOVA), Q value = 1

Table S32. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	43	70.4 (10.5)
subtype1	15	72.8 (8.9)
subtype2	7	66.9 (11.4)
subtype3	21	69.8 (11.2)

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.917 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	20	23
subtype1	8	7
subtype2	3	4
subtype3	9	12

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.407 (Chi-square test), Q value = 1

Table S34. 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	25	1	1	9
subtype1	1	7	0	0	5
subtype2	0	4	0	0	3
subtype3	1	14	1	1	1

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.425 (Chi-square test), Q value = 1

Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	2	14	9	9
subtype1	1	6	3	3
subtype2	1	3	1	0
subtype3	0	5	5	6

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0334 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	8	14	5	5
subtype1	3	2	3	3
subtype2	4	2	0	0
subtype3	1	10	2	2

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.474 (Chi-square test), Q value = 1

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	34	7	2
subtype1	12	3	0
subtype2	6	0	1
subtype3	16	4	1

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.00304 (Chi-square test), Q value = 0.16

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	7	4	6	11
subtype1	3	0	3	5
subtype2	4	0	0	0
subtype3	0	4	3	6

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

Clustering Approach #5: 'MIRseq CNMF subtypes'

Table S39. Get Full Table Description of clustering approach #5: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	51	34	49

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.223 (logrank test), Q value = 1

Table S40. 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	44	10	0.1 - 54.0 (4.0)
subtype2	24	1	0.1 - 70.1 (1.2)
subtype3	29	4	0.1 - 72.2 (4.1)

Figure S35. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.119 (ANOVA), Q value = 1

Table S41. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	127	68.0 (10.9)
subtype1	49	66.8 (10.7)
subtype2	34	66.3 (12.9)
subtype3	44	70.8 (9.1)

Figure S36. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.693 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	52	82
subtype1	22	29
subtype2	13	21
subtype3	17	32

Figure S37. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.109 (Chi-square test), Q value = 1

Table S43. 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	69	7	3	10	26
subtype1	6	20	2	1	6	16
subtype2	5	22	2	0	0	4
subtype3	3	27	3	2	4	6

Figure S38. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.181 (Chi-square test), Q value = 1

Table S44. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	51	50	17
subtype1	2	20	23	6
subtype2	0	11	16	3
subtype3	4	20	11	8

Figure S39. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.755 (Chi-square test), Q value = 1

Table S45. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	43	46	21	13
subtype1	19	16	9	4
subtype2	11	13	6	2
subtype3	13	17	6	7

Figure S40. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.48 (Chi-square test), Q value = 1

Table S46. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	114	14	6
subtype1	43	6	2
subtype2	30	4	0
subtype3	41	4	4

Figure S41. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.246 (Chi-square test), Q value = 1

Table S47. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	24	35	34	25
subtype1	8	17	14	9
subtype2	4	12	8	6
subtype3	12	6	12	10

Figure S42. 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 = 0.0701 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	129
subtype1	0	51
subtype2	1	33
subtype3	4	45

Figure S43. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'MIRseq cHierClus subtypes'

Table S49. Get Full Table Description of clustering approach #6: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	43	34	57

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.895 (logrank test), Q value = 1

Table S50. 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	32	4	0.1 - 70.1 (1.2)
subtype2	26	4	0.3 - 55.0 (4.1)
subtype3	39	7	0.1 - 72.2 (4.0)

Figure S44. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.599 (ANOVA), Q value = 1

Table S51. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	127	68.0 (10.9)
subtype1	42	66.7 (12.9)
subtype2	31	68.3 (9.4)
subtype3	54	68.9 (10.2)

Figure S45. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.62 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	52	82
subtype1	15	28
subtype2	12	22
subtype3	25	32

Figure S46. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0114 (Chi-square test), Q value = 0.57

Table S53. 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	69	7	3	10	26
subtype1	6	26	3	0	0	6
subtype2	2	14	1	2	8	7
subtype3	6	29	3	1	2	13

Figure S47. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.265 (Chi-square test), Q value = 1

Table S54. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	51	50	17
subtype1	1	13	17	8
subtype2	0	14	14	4
subtype3	5	24	19	5

Figure S48. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.112 (Chi-square test), Q value = 1

Table S55. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	43	46	21	13
subtype1	13	19	7	2
subtype2	8	8	9	5
subtype3	22	19	5	6

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

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

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

Table S56. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	114	14	6
subtype1	36	6	1
subtype2	30	3	1
subtype3	48	5	4

Figure S50. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.295 (Chi-square test), Q value = 1

Table S57. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	24	35	34	25
subtype1	6	13	10	10
subtype2	4	11	7	8
subtype3	14	11	17	7

Figure S51. 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 = 0.213 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	129
subtype1	1	42
subtype2	3	31
subtype3	1	56

Figure S52. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data

Input

Cluster data file = STAD-TP.mergedcluster.txt
Clinical data file = STAD-TP.clin.merged.picked.txt
Number of patients = 162
Number of clustering approaches = 6
Number of selected clinical features = 9
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[8] 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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