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.

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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