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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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