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 9 clinical features across 136 patients, 4 significant findings detected with P value < 0.05.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 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 4 subtypes that correlate to 'GENDER' and '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 5 different clustering approaches and 9 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 0.523 0.56 0.676 0.116 0.26
AGE ANOVA 0.123 0.358 0.591 0.961 0.923
GENDER Fisher's exact test 0.626 0.59 0.362 0.0239 0.372
HISTOLOGICAL TYPE Chi-square test 0.00941 0.363 0.175 0.00398 0.0101
PATHOLOGY T Chi-square test 0.246 0.304 0.578 0.969 0.337
PATHOLOGY N Chi-square test 0.418 0.509 0.148 0.561 0.781
PATHOLOGICSPREAD(M) Chi-square test 0.531 0.725 0.496 0.698 0.557
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 1 0.16 0.184
NEOADJUVANT THERAPY Fisher's exact test 0.781 0.0861 0.517
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 19 24 25
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.523 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 65 9 0.1 - 70.1 (2.2)
subtype1 19 4 0.1 - 65.1 (4.3)
subtype2 23 3 0.4 - 55.0 (3.6)
subtype3 23 2 0.1 - 70.1 (1.2)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.123 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 66 66.8 (11.3)
subtype1 19 70.0 (11.5)
subtype2 22 68.0 (10.3)
subtype3 25 63.3 (11.6)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.626 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 24 44
subtype1 8 11
subtype2 9 15
subtype3 7 18

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00941 (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 - PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL 11 28 3 2 9 15
subtype1 4 7 0 0 2 6
subtype2 1 8 0 2 7 6
subtype3 6 13 3 0 0 3

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

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.246 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 2 31 29 6
subtype1 2 9 6 2
subtype2 0 13 9 2
subtype3 0 9 14 2

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

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.418 (Chi-square test)

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

nPatients N0 N1 N2 N3
ALL 27 21 13 7
subtype1 11 5 2 1
subtype2 6 9 5 4
subtype3 10 7 6 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 = 0.531 (Chi-square test)

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

nPatients M0 M1 MX
ALL 61 6 1
subtype1 17 1 1
subtype2 22 2 0
subtype3 22 3 0

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

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

P value = 1 (Fisher's exact test)

Table S9.  Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 66 2
subtype1 19 0
subtype2 23 1
subtype3 24 1

Figure S8.  Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.781 (Fisher's exact test)

Table S10.  Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 61 7
subtype1 18 1
subtype2 21 3
subtype3 22 3

Figure S9.  Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'RNAseq CNMF subtypes'

Table S11.  Get Full Table Description of clustering approach #2: '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 S12.  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 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 S10.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.358 (ANOVA)

Table S13.  Clustering Approach #2: '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 S11.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.59 (Chi-square test)

Table S14.  Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.363 (Chi-square test)

Table S15.  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 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 S13.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.304 (Chi-square test)

Table S16.  Clustering Approach #2: '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 S14.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.509 (Chi-square test)

Table S17.  Clustering Approach #2: '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 S15.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.725 (Chi-square test)

Table S18.  Clustering Approach #2: '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 S16.  Get High-res Image Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #3: 'RNAseq cHierClus subtypes'

Table S19.  Get Full Table Description of clustering approach #3: '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 S20.  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 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 S17.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.591 (ANOVA)

Table S21.  Clustering Approach #3: '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 S18.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.362 (Fisher's exact test)

Table S22.  Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

Figure S19.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.175 (Chi-square test)

Table S23.  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 0 9 0 0 2
subtype2 1 15 0 0 0
subtype3 1 9 0 0 5
subtype4 0 6 1 1 2

Figure S20.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.578 (Chi-square test)

Table S24.  Clustering Approach #3: '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 S21.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.148 (Chi-square test)

Table S25.  Clustering Approach #3: '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 S22.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.496 (Chi-square test)

Table S26.  Clustering Approach #3: '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 S23.  Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

Clustering Approach #4: 'MIRseq CNMF subtypes'

Table S27.  Get Full Table Description of clustering approach #4: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 30 29 30 38
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.116 (logrank test)

Table S28.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 78 14 0.1 - 70.1 (4.2)
subtype1 27 6 0.1 - 70.1 (1.7)
subtype2 17 2 0.1 - 65.1 (4.0)
subtype3 22 3 0.3 - 54.0 (4.2)
subtype4 12 3 1.0 - 53.0 (8.0)

Figure S24.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.961 (ANOVA)

Table S29.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 123 67.9 (10.8)
subtype1 29 67.1 (12.6)
subtype2 28 67.8 (12.4)
subtype3 28 68.0 (8.8)
subtype4 38 68.6 (9.6)

Figure S25.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0239 (Fisher's exact test)

Table S30.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 50 77
subtype1 8 22
subtype2 7 22
subtype3 14 16
subtype4 21 17

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

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00398 (Chi-square test)

Table S31.  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 70 6 2 9 23
subtype1 5 11 1 0 3 10
subtype2 5 16 3 0 1 2
subtype3 1 14 1 2 4 8
subtype4 1 29 1 0 1 3

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.969 (Chi-square test)

Table S32.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients T1 T2 T3 T4
ALL 4 45 40 17
subtype1 2 14 10 4
subtype2 0 10 8 5
subtype3 1 10 10 4
subtype4 1 11 12 4

Figure S28.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.561 (Chi-square test)

Table S33.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 32 46 18 13
subtype1 12 10 5 3
subtype2 7 11 3 5
subtype3 8 9 6 2
subtype4 5 16 4 3

Figure S29.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.698 (Chi-square test)

Table S34.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 110 13 4
subtype1 25 4 1
subtype2 28 1 0
subtype3 26 3 1
subtype4 31 5 2

Figure S30.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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

P value = 0.16 (Fisher's exact test)

Table S35.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 123 4
subtype1 30 0
subtype2 27 2
subtype3 28 2
subtype4 38 0

Figure S31.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0861 (Fisher's exact test)

Table S36.  Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 118 9
subtype1 28 2
subtype2 25 4
subtype3 27 3
subtype4 38 0

Figure S32.  Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'MIRseq cHierClus subtypes'

Table S37.  Get Full Table Description of clustering approach #5: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 40 36 51
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.26 (logrank test)

Table S38.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 78 14 0.1 - 70.1 (4.2)
subtype1 24 5 0.1 - 70.1 (1.2)
subtype2 26 4 0.3 - 55.0 (4.2)
subtype3 28 5 0.1 - 47.0 (6.0)

Figure S33.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.923 (ANOVA)

Table S39.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 123 67.9 (10.8)
subtype1 38 67.4 (12.2)
subtype2 35 68.0 (9.2)
subtype3 50 68.3 (10.8)

Figure S34.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.372 (Fisher's exact test)

Table S40.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 50 77
subtype1 14 26
subtype2 12 24
subtype3 24 27

Figure S35.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0101 (Chi-square test)

Table S41.  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 70 6 2 9 23
subtype1 4 24 4 0 0 6
subtype2 2 16 0 2 7 9
subtype3 6 30 2 0 2 8

Figure S36.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.337 (Chi-square test)

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

nPatients T1 T2 T3 T4
ALL 4 45 40 17
subtype1 1 14 8 8
subtype2 0 14 13 4
subtype3 3 17 19 5

Figure S37.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.781 (Chi-square test)

Table S43.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 32 46 18 13
subtype1 11 16 5 3
subtype2 10 9 6 5
subtype3 11 21 7 5

Figure S38.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

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

P value = 0.557 (Chi-square test)

Table S44.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients M0 M1 MX
ALL 110 13 4
subtype1 35 5 0
subtype2 31 4 1
subtype3 44 4 3

Figure S39.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

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

P value = 0.184 (Fisher's exact test)

Table S45.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 123 4
subtype1 38 2
subtype2 34 2
subtype3 51 0

Figure S40.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.517 (Fisher's exact test)

Table S46.  Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 118 9
subtype1 36 4
subtype2 33 3
subtype3 49 2

Figure S41.  Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = STAD.mergedcluster.txt

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

  • Number of patients = 136

  • Number of clustering approaches = 5

  • 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

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)
[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)
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