Cervical Squamous Cell Carcinoma: 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 2 different clustering approaches and 4 clinical features across 17 patients, no significant finding detected with P value < 0.05.

- 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 2 different clustering approaches and 4 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, no significant finding detected.

Clinical Features | Statistical Tests | MIRseq CNMF subtypes | MIRseq cHierClus subtypes
Time to Death | logrank test | 0.484 | 0.531
AGE | ANOVA | 0.277 | 0.831
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 1 | 1
NEOADJUVANT THERAPY | Fisher's exact test | 0.511 | 0.245

Clustering Approach #1: 'MIRseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 9 | 4 | 4

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.484 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 17 | 4 | 0.3 - 101.8 (28.9)
subtype1 | 9 | 2 | 0.3 - 101.8 (28.9)
subtype2 | 4 | 1 | 1.2 - 30.4 (20.3)
subtype3 | 4 | 1 | 8.8 - 95.1 (70.4)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.277 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 17 | 51.1 (13.3)
subtype1 | 9 | 52.8 (14.2)
subtype2 | 4 | 42.0 (9.4)
subtype3 | 4 | 56.5 (12.6)

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

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

P value = 1 (Fisher's exact test)

Table S4. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 11 | 6
subtype1 | 6 | 3
subtype2 | 2 | 2
subtype3 | 3 | 1

Figure S3. Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.511 (Fisher's exact test)

Table S5. Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 11 | 6
subtype1 | 7 | 2
subtype2 | 2 | 2
subtype3 | 2 | 2

Figure S4. Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'MIRseq cHierClus subtypes'

Table S6. Get Full Table Description of clustering approach #2: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 3 | 1 | 3 | 2 | 8

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.531 (logrank test)

Table S7. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 14 | 3 | 0.3 - 95.1 (29.7)
subtype1 | 3 | 0 | 1.2 - 30.4 (12.4)
subtype3 | 3 | 1 | 8.8 - 95.1 (69.9)
subtype5 | 8 | 2 | 0.3 - 70.8 (32.8)

Figure S5. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.831 (ANOVA)

Table S8. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 14 | 53.4 (13.0)
subtype1 | 3 | 51.7 (7.2)
subtype3 | 3 | 57.7 (15.1)
subtype5 | 8 | 52.4 (14.9)

Figure S6. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1 (Fisher's exact test)

Table S9. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 10 | 4
subtype1 | 2 | 1
subtype3 | 2 | 1
subtype5 | 6 | 2

Figure S7. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.245 (Fisher's exact test)

Table S10. Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 10 | 4
subtype1 | 2 | 1
subtype3 | 1 | 2
subtype5 | 7 | 1

Figure S8. Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = CESC.mergedcluster.txt

- Clinical data file = CESC.clin.merged.picked.txt

- Number of patients = 17

- Number of clustering approaches = 2

- Number of selected clinical features = 4

- 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

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)
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