Cervical Squamous Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 6 different clustering approaches and 6 clinical features across 32 patients, one significant finding detected with P value < 0.05.

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

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

- CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

- 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 6 different clustering approaches and 6 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, one significant finding detected.

Clinical Features | Time to Death | AGE | RADIATIONS RADIATION REGIMENINDICATION | NUMBERPACKYEARSSMOKED | STOPPEDSMOKINGYEAR | TOBACCOSMOKINGHISTORYINDICATOR
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | ANOVA | ANOVA
CN CNMF | 100 | 0.647 | 0.801 |  |  | 0.574
METHLYATION CNMF | 0.0604 | 0.817 | 0.789 |  |  | 0.834
RNAseq CNMF subtypes | 100 | 0.837 | 0.0228 |  |  | 0.966
RNAseq cHierClus subtypes | 100 | 0.617 | 1 |  |  | 0.637
MIRseq CNMF subtypes | 0.956 | 0.42 | 1 |  |  | 0.862
MIRseq cHierClus subtypes | 0.183 | 0.259 | 0.856 |  |  | 0.975

Clustering Approach #1: 'CN CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 10 | 4 | 12

'CN CNMF' versus 'Time to Death'

P value = 100 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 25 | 1 | 0.1 - 95.1 (2.7)
subtype1 | 9 | 0 | 0.3 - 70.8 (2.7)
subtype2 | 4 | 0 | 1.2 - 69.9 (3.6)
subtype3 | 12 | 1 | 0.1 - 95.1 (3.8)

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

'CN CNMF' versus 'AGE'

P value = 0.647 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 26 | 48.5 (11.8)
subtype1 | 10 | 46.8 (10.4)
subtype2 | 4 | 53.5 (6.9)
subtype3 | 12 | 48.2 (14.3)

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

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.801 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 4 | 22
subtype1 | 1 | 9
subtype2 | 1 | 3
subtype3 | 2 | 10

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

'CN CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.574 (ANOVA)

Table S5. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 25 | 1.8 (1.1)
subtype1 | 9 | 1.7 (1.1)
subtype2 | 4 | 1.5 (0.6)
subtype3 | 12 | 2.1 (1.2)

Figure S4. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #2: 'METHLYATION CNMF'

Table S6. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 6 | 4 | 12 | 8 | 2

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0604 (logrank test)

Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 29 | 4 | 0.1 - 101.8 (6.0)
subtype1 | 6 | 0 | 2.7 - 101.8 (45.9)
subtype2 | 4 | 0 | 1.0 - 95.1 (21.4)
subtype3 | 11 | 1 | 0.6 - 36.8 (2.7)
subtype4 | 8 | 3 | 0.1 - 69.9 (5.2)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.817 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 30 | 50.4 (12.4)
subtype1 | 6 | 48.5 (10.0)
subtype2 | 4 | 51.5 (8.3)
subtype3 | 12 | 48.8 (12.8)
subtype4 | 8 | 53.9 (16.1)

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

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

P value = 0.789 (Fisher's exact test)

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 8 | 22
subtype1 | 1 | 5
subtype2 | 2 | 2
subtype3 | 3 | 9
subtype4 | 2 | 6

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

'METHLYATION CNMF' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.834 (ANOVA)

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 27 | 2.0 (1.2)
subtype1 | 5 | 2.4 (1.5)
subtype2 | 4 | 2.0 (1.4)
subtype3 | 11 | 2.0 (1.2)
subtype4 | 7 | 1.7 (1.1)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S11. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 16 | 5 | 7

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test)

Table S12. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 27 | 2 | 0.1 - 101.8 (2.7)
subtype1 | 15 | 1 | 0.1 - 70.8 (1.7)
subtype2 | 5 | 1 | 12.4 - 95.1 (30.4)
subtype3 | 7 | 0 | 1.0 - 101.8 (2.2)

Figure S9. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.837 (ANOVA)

Table S13. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 28 | 48.1 (10.6)
subtype1 | 16 | 47.4 (11.4)
subtype2 | 5 | 47.6 (2.2)
subtype3 | 7 | 50.3 (13.0)

Figure S10. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0228 (Fisher's exact test)

Table S14. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 6 | 22
subtype1 | 1 | 15
subtype2 | 3 | 2
subtype3 | 2 | 5

Figure S11. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.966 (ANOVA)

Table S15. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 25 | 2.0 (1.2)
subtype1 | 14 | 2.0 (1.2)
subtype2 | 4 | 2.0 (1.4)
subtype3 | 7 | 1.9 (1.1)

Figure S12. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S16. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 4 | 5 | 19

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test)

Table S17. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 27 | 2 | 0.1 - 101.8 (2.7)
subtype1 | 4 | 0 | 0.1 - 95.1 (35.5)
subtype2 | 5 | 1 | 1.2 - 101.8 (2.2)
subtype3 | 18 | 1 | 0.3 - 70.8 (4.3)

Figure S13. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.617 (ANOVA)

Table S18. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 28 | 48.1 (10.6)
subtype1 | 4 | 43.5 (9.8)
subtype2 | 5 | 47.4 (12.3)
subtype3 | 19 | 49.3 (10.6)

Figure S14. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1 (Fisher's exact test)

Table S19. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 6 | 22
subtype1 | 1 | 3
subtype2 | 1 | 4
subtype3 | 4 | 15

Figure S15. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.637 (ANOVA)

Table S20. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 25 | 2.0 (1.2)
subtype1 | 4 | 2.0 (1.4)
subtype2 | 5 | 2.4 (1.5)
subtype3 | 16 | 1.8 (1.0)

Figure S16. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #5: 'MIRseq CNMF subtypes'

Table S21. Get Full Table Description of clustering approach #5: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 11 | 14 | 7

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.956 (logrank test)

Table S22. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 31 | 4 | 0.1 - 101.8 (5.8)
subtype1 | 11 | 2 | 0.1 - 95.1 (12.4)
subtype2 | 13 | 2 | 0.6 - 101.8 (5.5)
subtype3 | 7 | 0 | 1.0 - 6.0 (1.2)

Figure S17. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.42 (ANOVA)

Table S23. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 32 | 49.9 (12.7)
subtype1 | 11 | 47.5 (12.6)
subtype2 | 14 | 53.3 (11.9)
subtype3 | 7 | 46.9 (14.6)

Figure S18. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1 (Fisher's exact test)

Table S24. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 8 | 24
subtype1 | 3 | 8
subtype2 | 3 | 11
subtype3 | 2 | 5

Figure S19. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.862 (ANOVA)

Table S25. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 29 | 2.0 (1.2)
subtype1 | 10 | 1.8 (1.2)
subtype2 | 13 | 2.1 (1.2)
subtype3 | 6 | 2.0 (1.3)

Figure S20. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

Clustering Approach #6: 'MIRseq cHierClus subtypes'

Table S26. Get Full Table Description of clustering approach #6: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 3 | 18 | 11

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.183 (logrank test)

Table S27. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 31 | 4 | 0.1 - 101.8 (5.8)
subtype1 | 3 | 0 | 1.2 - 6.0 (2.2)
subtype2 | 18 | 4 | 0.3 - 101.8 (7.3)
subtype3 | 10 | 0 | 0.1 - 95.1 (4.8)

Figure S21. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.259 (ANOVA)

Table S28. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 32 | 49.9 (12.7)
subtype1 | 3 | 43.0 (13.7)
subtype2 | 18 | 53.1 (13.6)
subtype3 | 11 | 46.5 (10.2)

Figure S22. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.856 (Fisher's exact test)

Table S29. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 8 | 24
subtype1 | 1 | 2
subtype2 | 5 | 13
subtype3 | 2 | 9

Figure S23. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'TOBACCOSMOKINGHISTORYINDICATOR'

P value = 0.975 (ANOVA)

Table S30. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 29 | 2.0 (1.2)
subtype1 | 3 | 2.0 (1.0)
subtype2 | 17 | 2.0 (1.2)
subtype3 | 9 | 1.9 (1.3)

Figure S24. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'TOBACCOSMOKINGHISTORYINDICATOR'

Methods & Data
Input

- Cluster data file = CESC-TP.mergedcluster.txt

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

- Number of patients = 32

- Number of clustering approaches = 6

- Number of selected clinical features = 6

- 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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