Correlation between molecular cancer subtypes and selected clinical features
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

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[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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