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 5 different clustering approaches and 8 clinical features across 124 patients, 4 significant findings detected with P value < 0.05.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and 'AGE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that correlate to 'PATHOLOGY.N'.

  • CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

  • 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 5 different clustering approaches and 8 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 		METHLYATION
CNMF 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRseq
CNMF
subtypes 		MIRseq
cHierClus
subtypes
Time to Death logrank test 0.00603 0.183 0.667 0.569 0.353
AGE ANOVA 0.02 0.962 0.791 0.135 0.709
GENDER Fisher's exact test 0.417 0.56 0.292 0.375 0.333
HISTOLOGICAL TYPE Chi-square test 0.133 0.176 0.107 0.000364 0.0547
PATHOLOGY T Chi-square test 0.44 0.504 0.557 0.307 0.884
PATHOLOGY N Chi-square test 0.262 0.492 0.036 0.559 0.699
PATHOLOGICSPREAD(M) Chi-square test 0.17 0.395 0.901 0.0546 0.458
NEOADJUVANT THERAPY Fisher's exact test 0.0622 0.693
Clustering Approach #1: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3
Number of samples 20 25 19
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00603 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 20 5 1.0 - 54.0 (14.0)
subtype1 6 0 12.0 - 47.0 (19.5)
subtype2 6 3 1.0 - 15.9 (4.0)
subtype3 8 2 1.0 - 54.0 (20.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.02 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 64 68.9 (10.2)
subtype1 20 67.1 (11.6)
subtype2 25 73.2 (8.3)
subtype3 19 65.2 (9.3)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.417 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 29 35
subtype1 8 12
subtype2 14 11
subtype3 7 12

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.133 (Chi-square test)

Table S5.  Clustering Approach #1: '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 - TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL 2 45 2 1 9
subtype1 0 16 1 0 1
subtype2 1 20 0 0 2
subtype3 1 9 1 1 6

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.44 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 2 15 17 9
subtype1 0 7 4 1
subtype2 1 3 8 4
subtype3 1 5 5 4

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.262 (Chi-square test)

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

nPatients N0 N1 N2 N3
ALL 8 26 7 5
subtype1 2 11 2 0
subtype2 2 11 4 3
subtype3 4 4 1 2

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

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.17 (Chi-square test)

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

nPatients M0 M1 MX
ALL 54 7 3
subtype1 20 0 0
subtype2 20 4 1
subtype3 14 3 2

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

Clustering Approach #2: 'RNAseq CNMF subtypes'

Table S9.  Get Full Table Description of clustering approach #2: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 14 11 9 13 10
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.183 (logrank test)

Table S10.  Clustering Approach #2: '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 3 0 1.0 - 15.9 (12.0)
subtype2 1 0 1.0 - 1.0 (1.0)
subtype3 2 1 1.0 - 3.0 (2.0)
subtype4 5 1 18.9 - 54.0 (22.0)
subtype5 7 3 1.0 - 53.0 (9.0)

Figure S8.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.962 (ANOVA)

Table S11.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 57 69.5 (10.1)
subtype1 14 67.9 (11.2)
subtype2 11 70.9 (11.8)
subtype3 9 69.9 (10.4)
subtype4 13 69.2 (10.0)
subtype5 10 70.1 (7.9)

Figure S9.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.56 (Chi-square test)

Table S12.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 30 27
subtype1 8 6
subtype2 4 7
subtype3 4 5
subtype4 9 4
subtype5 5 5

Figure S10.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.176 (Chi-square test)

Table S13.  Clustering Approach #2: '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 12 0 0 0
subtype2 0 9 0 0 2
subtype3 1 8 0 0 0
subtype4 0 6 1 1 4
subtype5 1 4 0 0 3

Figure S11.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.504 (Chi-square test)

Table S14.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

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

Figure S12.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.492 (Chi-square test)

Table S15.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

Figure S13.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.395 (Chi-square test)

Table S16.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 48 7 2
subtype1 13 1 0
subtype2 10 0 1
subtype3 7 2 0
subtype4 11 1 1
subtype5 7 3 0

Figure S14.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #3: 'RNAseq cHierClus subtypes'

Table S17.  Get Full Table Description of clustering approach #3: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2
Number of samples 29 28
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.667 (logrank test)

Table S18.  Clustering Approach #3: '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 11 3 1.0 - 54.0 (18.9)
subtype2 7 2 1.0 - 22.0 (12.0)

Figure S15.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.791 (t-test)

Table S19.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 57 69.5 (10.1)
subtype1 29 69.8 (9.3)
subtype2 28 69.1 (11.0)

Figure S16.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.292 (Fisher's exact test)

Table S20.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 30 27
subtype1 13 16
subtype2 17 11

Figure S17.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.107 (Chi-square test)

Table S21.  Clustering Approach #3: '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 1 18 0 0 8
subtype2 1 21 1 1 1

Figure S18.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.557 (Chi-square test)

Table S22.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 2 14 14 9
subtype1 2 8 8 4
subtype2 0 6 6 5

Figure S19.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.036 (Chi-square test)

Table S23.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 8 20 6 5
subtype1 7 6 4 3
subtype2 1 14 2 2

Figure S20.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.901 (Chi-square test)

Table S24.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 48 7 2
subtype1 25 3 1
subtype2 23 4 1

Figure S21.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #4: 'MIRseq CNMF subtypes'

Table S25.  Get Full Table Description of clustering approach #4: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 39 47 37
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.569 (logrank test)

Table S26.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 76 14 0.1 - 70.1 (4.4)
subtype1 37 6 0.3 - 70.1 (4.7)
subtype2 17 5 0.1 - 47.0 (7.1)
subtype3 22 3 0.1 - 65.1 (1.2)

Figure S22.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.135 (ANOVA)

Table S27.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 121 68.0 (10.8)
subtype1 37 66.9 (10.4)
subtype2 47 70.4 (9.7)
subtype3 37 66.1 (12.2)

Figure S23.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.375 (Fisher's exact test)

Table S28.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 48 75
subtype1 14 25
subtype2 22 25
subtype3 12 25

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

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000364 (Chi-square test)

Table S29.  Clustering Approach #4: '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 12 68 4 2 9 23
subtype1 4 13 0 2 7 13
subtype2 2 34 1 0 1 6
subtype3 6 21 3 0 1 4

Figure S25.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.307 (Chi-square test)

Table S30.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 4 43 40 15
subtype1 1 18 16 3
subtype2 3 12 14 5
subtype3 0 13 10 7

Figure S26.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.559 (Chi-square test)

Table S31.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 32 43 18 12
subtype1 15 13 7 3
subtype2 7 18 5 4
subtype3 10 12 6 5

Figure S27.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.0546 (Chi-square test)

Table S32.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 106 13 4
subtype1 37 2 0
subtype2 36 7 4
subtype3 33 4 0

Figure S28.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0622 (Fisher's exact test)

Table S33.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 116 7
subtype1 35 4
subtype2 47 0
subtype3 34 3

Figure S29.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'MIRseq cHierClus subtypes'

Table S34.  Get Full Table Description of clustering approach #5: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 29 48 46
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.353 (logrank test)

Table S35.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 76 14 0.1 - 70.1 (4.4)
subtype1 19 4 0.3 - 54.0 (4.3)
subtype2 31 5 0.1 - 70.1 (3.6)
subtype3 26 5 0.1 - 27.0 (5.8)

Figure S30.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.709 (ANOVA)

Table S36.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 121 68.0 (10.8)
subtype1 28 69.0 (9.1)
subtype2 48 67.0 (11.6)
subtype3 45 68.5 (11.1)

Figure S31.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.333 (Fisher's exact test)

Table S37.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 48 75
subtype1 10 19
subtype2 16 32
subtype3 22 24

Figure S32.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0547 (Chi-square test)

Table S38.  Clustering Approach #5: '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 12 68 4 2 9 23
subtype1 0 14 0 2 4 9
subtype2 6 26 3 0 3 7
subtype3 6 28 1 0 2 7

Figure S33.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.884 (Chi-square test)

Table S39.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 4 43 40 15
subtype1 1 11 9 2
subtype2 1 16 14 8
subtype3 2 16 17 5

Figure S34.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.699 (Chi-square test)

Table S40.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 32 43 18 12
subtype1 9 8 3 1
subtype2 13 16 9 5
subtype3 10 19 6 6

Figure S35.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.458 (Chi-square test)

Table S41.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 106 13 4
subtype1 24 4 1
subtype2 43 5 0
subtype3 39 4 3

Figure S36.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.693 (Fisher's exact test)

Table S42.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 116 7
subtype1 28 1
subtype2 44 4
subtype3 44 2

Figure S37.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = STAD.mergedcluster.txt

  • Clinical data file = STAD.clin.merged.picked.txt

  • Number of patients = 124

  • Number of clustering approaches = 5

  • Number of selected clinical features = 8

  • 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

Download Results

This is an experimental feature. Location of data archives could not be determined.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[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)
Meta

  • Maintainer = TCGA GDAC Team

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