Correlation between aggregated molecular cancer subtypes and selected clinical features
Esophageal Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1GH9GG2
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GH9GG2
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 7 clinical features across 22 patients, 4 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death'.

  • 2 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 6 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 4 significant findings detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.583
(1.00) 		0.819
(1.00) 		0.25
(1.00) 		0.25
(1.00) 		0.0027
(0.111) 		0.25
(1.00)
AGE t-test 0.00199
(0.0837) 		0.178
(1.00) 		0.0122
(0.464) 		0.00336
(0.134) 		0.0183
(0.675) 		0.00336
(0.134)
NEOPLASM DISEASESTAGE Chi-square test 0.359
(1.00) 		0.307
(1.00) 		0.245
(1.00) 		0.451
(1.00) 		0.0638
(1.00) 		0.451
(1.00)
PATHOLOGY T STAGE Chi-square test 0.422
(1.00) 		0.375
(1.00) 		0.251
(1.00) 		0.375
(1.00) 		0.217
(1.00) 		0.375
(1.00)
PATHOLOGY N STAGE Chi-square test 0.122
(1.00) 		0.872
(1.00) 		0.344
(1.00) 		0.183
(1.00) 		0.578
(1.00) 		0.183
(1.00)
GENDER Fisher's exact test 1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.684
(1.00) 		1
(1.00)
NUMBERPACKYEARSSMOKED t-test 0.588
(1.00) 		0.882
(1.00) 		0.381
(1.00) 		0.187
(1.00) 		0.5
(1.00) 		0.187
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1.  Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 3 12 7
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.583 (logrank test), Q value = 1

Table S2.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 18 6 0.0 - 30.7 (3.2)
subtype1 3 0 0.3 - 0.5 (0.4)
subtype2 11 6 0.8 - 30.7 (3.7)
subtype3 4 0 0.0 - 4.1 (0.1)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.00199 (ANOVA), Q value = 0.084

Table S3.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 22 66.0 (10.8)
subtype1 3 63.0 (7.0)
subtype2 12 72.4 (9.7)
subtype3 7 56.3 (5.1)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.359 (Chi-square test), Q value = 1

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC
ALL 2 1 3 7 5 3 1
subtype1 0 0 0 0 1 2 0
subtype2 1 1 1 5 2 1 1
subtype3 1 0 2 2 2 0 0

Figure S3.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.422 (Chi-square test), Q value = 1

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 4 7 11
subtype1 0 0 3
subtype2 3 4 5
subtype3 1 3 3

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.122 (Chi-square test), Q value = 1

Table S6.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2+N3
ALL 8 10 4
subtype1 0 1 2
subtype2 4 6 2
subtype3 4 3 0

Figure S5.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

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

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 3 19
subtype1 0 3
subtype2 2 10
subtype3 1 6

Figure S6.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.588 (ANOVA), Q value = 1

Table S8.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 14 32.7 (17.6)
subtype1 1 31.0 (NA)
subtype2 9 34.4 (21.2)
subtype3 4 29.4 (10.9)

Figure S7.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #2: 'METHLYATION CNMF'

Table S9.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 8 6 8
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.819 (logrank test), Q value = 1

Table S10.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 18 6 0.0 - 30.7 (3.2)
subtype1 5 0 0.1 - 4.1 (0.4)
subtype2 5 2 0.0 - 30.7 (1.4)
subtype3 8 4 0.3 - 29.0 (5.4)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.178 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 22 66.0 (10.8)
subtype1 8 60.4 (5.4)
subtype2 6 68.2 (14.6)
subtype3 8 70.0 (10.6)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.307 (Chi-square test), Q value = 1

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC
ALL 2 1 3 7 5 3 1
subtype1 1 0 2 1 3 1 0
subtype2 0 1 0 2 2 0 1
subtype3 1 0 1 4 0 2 0

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.375 (Chi-square test), Q value = 1

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 4 7 11
subtype1 1 2 5
subtype2 0 3 3
subtype3 3 2 3

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.872 (Chi-square test), Q value = 1

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2+N3
ALL 8 10 4
subtype1 4 3 1
subtype2 2 3 1
subtype3 2 4 2

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'GENDER'

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

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 3 19
subtype1 1 7
subtype2 1 5
subtype3 1 7

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'GENDER'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.882 (ANOVA), Q value = 1

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 14 32.7 (17.6)
subtype1 4 35.2 (12.8)
subtype2 5 34.0 (26.8)
subtype3 5 29.4 (11.9)

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #3: 'MIRSEQ CNMF'

Table S17.  Description of clustering approach #3: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 7 4 11
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.25 (logrank test), Q value = 1

Table S18.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 18 6 0.0 - 30.7 (3.2)
subtype1 6 1 0.0 - 4.1 (0.3)
subtype2 1 0 0.4 - 0.4 (0.4)
subtype3 11 5 0.3 - 30.7 (3.7)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0122 (ANOVA), Q value = 0.46

Table S19.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 22 66.0 (10.8)
subtype1 7 58.6 (7.5)
subtype2 4 61.5 (7.9)
subtype3 11 72.4 (10.1)

Figure S16.  Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.245 (Chi-square test), Q value = 1

Table S20.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC
ALL 2 1 3 7 5 3 1
subtype1 1 1 2 2 1 0 0
subtype2 0 0 0 0 3 1 0
subtype3 1 0 1 5 1 2 1

Figure S17.  Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.251 (Chi-square test), Q value = 1

Table S21.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 4 7 11
subtype1 1 3 3
subtype2 0 0 4
subtype3 3 4 4

Figure S18.  Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.344 (Chi-square test), Q value = 1

Table S22.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2+N3
ALL 8 10 4
subtype1 4 3 0
subtype2 2 1 1
subtype3 2 6 3

Figure S19.  Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

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

Table S23.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 3 19
subtype1 1 6
subtype2 0 4
subtype3 2 9

Figure S20.  Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #6: 'GENDER'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.381 (ANOVA), Q value = 1

Table S24.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 14 32.7 (17.6)
subtype1 5 37.2 (22.7)
subtype2 2 43.8 (8.8)
subtype3 7 26.3 (14.4)

Figure S21.  Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #4: 'MIRSEQ CHIERARCHICAL'

Table S25.  Description of clustering approach #4: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 2 3
Number of samples 11 11
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.25 (logrank test), Q value = 1

Table S26.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 18 6 0.0 - 30.7 (3.2)
subtype2 11 5 0.3 - 30.7 (3.7)
subtype3 7 1 0.0 - 4.1 (0.4)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00336 (t-test), Q value = 0.13

Table S27.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 22 66.0 (10.8)
subtype2 11 72.4 (10.1)
subtype3 11 59.6 (7.4)

Figure S23.  Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.451 (Chi-square test), Q value = 1

Table S28.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC
ALL 2 1 3 7 5 3 1
subtype2 1 0 1 5 1 2 1
subtype3 1 1 2 2 4 1 0

Figure S24.  Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.375 (Chi-square test), Q value = 1

Table S29.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 4 7 11
subtype2 3 4 4
subtype3 1 3 7

Figure S25.  Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.183 (Chi-square test), Q value = 1

Table S30.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2+N3
ALL 8 10 4
subtype2 2 6 3
subtype3 6 4 1

Figure S26.  Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S31.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 3 19
subtype2 2 9
subtype3 1 10

Figure S27.  Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.187 (t-test), Q value = 1

Table S32.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 14 32.7 (17.6)
subtype2 7 26.3 (14.4)
subtype3 7 39.1 (19.2)

Figure S28.  Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #5: 'MIRseq Mature CNMF subtypes'

Table S33.  Description of clustering approach #5: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 4 2 6 10
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0027 (logrank test), Q value = 0.11

Table S34.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 16 6 0.0 - 30.7 (2.3)
subtype1 1 0 0.5 - 0.5 (0.5)
subtype3 5 1 0.0 - 0.8 (0.1)
subtype4 10 5 0.3 - 30.7 (5.3)

Figure S29.  Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0183 (ANOVA), Q value = 0.68

Table S35.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 20 66.8 (11.0)
subtype1 4 61.0 (8.0)
subtype3 6 59.7 (7.9)
subtype4 10 73.3 (10.2)

Figure S30.  Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0638 (Chi-square test), Q value = 1

Table S36.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC
ALL 1 1 3 6 5 3 1
subtype1 0 0 0 0 4 0 0
subtype3 0 1 2 2 0 1 0
subtype4 1 0 1 4 1 2 1

Figure S31.  Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.217 (Chi-square test), Q value = 1

Table S37.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 2 7 11
subtype1 0 0 4
subtype3 0 3 3
subtype4 2 4 4

Figure S32.  Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.578 (Chi-square test), Q value = 1

Table S38.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2+N3
ALL 7 9 4
subtype1 2 2 0
subtype3 3 2 1
subtype4 2 5 3

Figure S33.  Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.684 (Fisher's exact test), Q value = 1

Table S39.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 2 18
subtype1 0 4
subtype3 0 6
subtype4 2 8

Figure S34.  Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.5 (ANOVA), Q value = 1

Table S40.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 13 32.1 (18.2)
subtype1 3 39.5 (9.7)
subtype3 3 38.3 (31.8)
subtype4 7 26.3 (14.4)

Figure S35.  Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #6: 'MIRseq Mature cHierClus subtypes'

Table S41.  Description of clustering approach #6: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 2 3
Number of samples 11 11
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.25 (logrank test), Q value = 1

Table S42.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 18 6 0.0 - 30.7 (3.2)
subtype2 11 5 0.3 - 30.7 (3.7)
subtype3 7 1 0.0 - 4.1 (0.4)

Figure S36.  Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.00336 (t-test), Q value = 0.13

Table S43.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 22 66.0 (10.8)
subtype2 11 72.4 (10.1)
subtype3 11 59.6 (7.4)

Figure S37.  Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.451 (Chi-square test), Q value = 1

Table S44.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE IA STAGE IB STAGE IIA STAGE IIB STAGE IIIA STAGE IIIB STAGE IIIC
ALL 2 1 3 7 5 3 1
subtype2 1 0 1 5 1 2 1
subtype3 1 1 2 2 4 1 0

Figure S38.  Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.375 (Chi-square test), Q value = 1

Table S45.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 4 7 11
subtype2 3 4 4
subtype3 1 3 7

Figure S39.  Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.183 (Chi-square test), Q value = 1

Table S46.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2+N3
ALL 8 10 4
subtype2 2 6 3
subtype3 6 4 1

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

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

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

Table S47.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

nPatients FEMALE MALE
ALL 3 19
subtype2 2 9
subtype3 1 10

Figure S41.  Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.187 (t-test), Q value = 1

Table S48.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 14 32.7 (17.6)
subtype2 7 26.3 (14.4)
subtype3 7 39.1 (19.2)

Figure S42.  Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NUMBERPACKYEARSSMOKED'

Methods & Data
Input

  • Cluster data file = ESCA-TP.mergedcluster.txt

  • Clinical data file = ESCA-TP.merged_data.txt

  • Number of patients = 22

  • Number of clustering approaches = 6

  • Number of selected clinical features = 7

  • Exclude small clusters that include fewer than K patients, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[3] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[4] 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)
[5] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[6] 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)
Copyright © 2014 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle