Correlation between aggregated molecular cancer subtypes and selected clinical features
Sarcoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1CC0XR9
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 3 clinical features across 26 patients, one significant finding 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 do not correlate to any clinical features.

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

  • 2 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 do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes 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 3 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.

Clinical
Features
Time
to
Death
AGE GENDER
Statistical Tests logrank test t-test Fisher's exact test
Copy Number Ratio CNMF subtypes 0.191
(1.00)
0.722
(1.00)
0.0835
(1.00)
METHLYATION CNMF 0.0116
(0.209)
0.32
(1.00)
0.866
(1.00)
MIRSEQ CNMF 0.824
(1.00)
0.416
(1.00)
0.524
(1.00)
MIRSEQ CHIERARCHICAL 0.824
(1.00)
0.478
(1.00)
0.524
(1.00)
MIRseq Mature CNMF subtypes 0.824
(1.00)
0.416
(1.00)
0.524
(1.00)
MIRseq Mature cHierClus subtypes 0.824
(1.00)
0.478
(1.00)
0.524
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 8 5 9
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.191 (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 22 6 0.1 - 53.3 (11.3)
subtype1 8 2 0.1 - 46.7 (9.4)
subtype2 5 3 2.0 - 24.5 (9.7)
subtype3 9 1 0.1 - 53.3 (13.6)

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 = 0.722 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 22 64.6 (7.7)
subtype1 8 66.0 (7.7)
subtype2 5 65.4 (6.5)
subtype3 9 63.0 (8.8)

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 'GENDER'

P value = 0.0835 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 10 12
subtype1 5 3
subtype2 0 5
subtype3 5 4

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S5.  Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 10 13 3
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0116 (logrank test), Q value = 0.21

Table S6.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 26 8 0.1 - 76.4 (13.2)
subtype1 10 2 5.2 - 76.4 (30.7)
subtype2 13 6 0.1 - 74.7 (8.6)
subtype3 3 0 0.1 - 4.1 (0.1)

Figure S4.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.32 (ANOVA), Q value = 1

Table S7.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 26 63.8 (9.4)
subtype1 10 64.3 (12.6)
subtype2 13 65.2 (6.8)
subtype3 3 56.0 (2.0)

Figure S5.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.866 (Fisher's exact test), Q value = 1

Table S8.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 12 14
subtype1 4 6
subtype2 6 7
subtype3 2 1

Figure S6.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

Clustering Approach #3: 'MIRSEQ CNMF'

Table S9.  Get Full Table Description of clustering approach #3: 'MIRSEQ CNMF'

Cluster Labels 1 2
Number of samples 6 4
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.824 (logrank test), Q value = 1

Table S10.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 10 3 0.1 - 53.3 (9.1)
subtype1 6 2 0.5 - 53.3 (9.1)
subtype2 4 1 0.1 - 26.9 (6.8)

Figure S7.  Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.416 (t-test), Q value = 1

Table S11.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 10 64.0 (8.8)
subtype1 6 61.8 (7.6)
subtype2 4 67.2 (10.6)

Figure S8.  Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.524 (Fisher's exact test), Q value = 1

Table S12.  Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 5 5
subtype1 2 4
subtype2 3 1

Figure S9.  Get High-res Image Clustering Approach #3: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

Clustering Approach #4: 'MIRSEQ CHIERARCHICAL'

Table S13.  Get Full Table Description of clustering approach #4: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 1 3 6
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.824 (logrank test), Q value = 1

Table S14.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 9 3 0.1 - 53.3 (9.7)
subtype2 3 1 0.1 - 26.9 (13.6)
subtype3 6 2 0.5 - 53.3 (9.1)

Figure S10.  Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.478 (t-test), Q value = 1

Table S15.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 9 64.0 (9.3)
subtype2 3 68.3 (12.7)
subtype3 6 61.8 (7.6)

Figure S11.  Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.524 (Fisher's exact test), Q value = 1

Table S16.  Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 4 5
subtype2 2 1
subtype3 2 4

Figure S12.  Get High-res Image Clustering Approach #4: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

Clustering Approach #5: 'MIRseq Mature CNMF subtypes'

Table S17.  Get Full Table Description of clustering approach #5: 'MIRseq Mature CNMF subtypes'

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

P value = 0.824 (logrank test), Q value = 1

Table S18.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 10 3 0.1 - 53.3 (9.1)
subtype1 6 2 0.5 - 53.3 (9.1)
subtype2 4 1 0.1 - 26.9 (6.8)

Figure S13.  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 = 0.416 (t-test), Q value = 1

Table S19.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 10 64.0 (8.8)
subtype1 6 61.8 (7.6)
subtype2 4 67.2 (10.6)

Figure S14.  Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.524 (Fisher's exact test), Q value = 1

Table S20.  Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 5 5
subtype1 2 4
subtype2 3 1

Figure S15.  Get High-res Image Clustering Approach #5: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #6: 'MIRseq Mature cHierClus subtypes'

Table S21.  Get Full Table Description of clustering approach #6: 'MIRseq Mature cHierClus subtypes'

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

P value = 0.824 (logrank test), Q value = 1

Table S22.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 9 3 0.1 - 53.3 (9.7)
subtype2 3 1 0.1 - 26.9 (13.6)
subtype3 6 2 0.5 - 53.3 (9.1)

Figure S16.  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 = 0.478 (t-test), Q value = 1

Table S23.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 9 64.0 (9.3)
subtype2 3 68.3 (12.7)
subtype3 6 61.8 (7.6)

Figure S17.  Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.524 (Fisher's exact test), Q value = 1

Table S24.  Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 4 5
subtype2 2 1
subtype3 2 4

Figure S18.  Get High-res Image Clustering Approach #6: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Methods & Data
Input
  • Cluster data file = SARC-TP.mergedcluster.txt

  • Clinical data file = SARC-TP.clin.merged.picked.txt

  • Number of patients = 26

  • Number of clustering approaches = 6

  • Number of selected clinical features = 3

  • 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

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

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] 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] 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)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] 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)