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 5 different clustering approaches and 4 clinical features across 124 patients, 2 significant findings detected with P value < 0.05.

3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 9 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'NEOADJUVANT.THERAPY'.
CNMF clustering analysis on sequencing-based miR expression data identified 4 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 5 different clustering approaches and 4 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 2 significant findings detected.


Clinical Features	Time to Death	AGE	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Fisher's exact test
METHLYATION CNMF	1	0.00334	1	0.315
RNAseq CNMF subtypes	1	0.057	0.61	0.252
RNAseq cHierClus subtypes	1	0.0522	1	0.0124
MIRseq CNMF subtypes	1	0.306	1	0.249
MIRseq cHierClus subtypes	1	0.138	1	0.145

Clustering Approach #1: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	36	41	47

'METHLYATION CNMF' versus 'Time to Death'

P value = 1 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	124	1	0.3 - 66.0 (19.1)
subtype1	36	0	0.3 - 65.9 (21.0)
subtype2	41	0	1.0 - 62.4 (17.6)
subtype3	47	1	1.0 - 66.0 (19.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00334 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	124	61.2 (6.6)
subtype1	36	62.9 (5.7)
subtype2	41	58.4 (7.0)
subtype3	47	62.3 (6.2)

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

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

P value = 1 (Fisher's exact test)

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

nPatients	NO	YES
ALL	5	119
subtype1	1	35
subtype2	2	39
subtype3	2	45

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.315 (Fisher's exact test)

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

nPatients	NO	YES
ALL	4	120
subtype1	1	35
subtype2	0	41
subtype3	3	44

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

Clustering Approach #2: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4	5	6	7	8	9
Number of samples	7	3	7	7	8	5	4	8	4

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

	nPatients	Duration Range (Median), Month
ALL	53	1.0 - 65.9 (21.8)
subtype1	7	3.0 - 47.4 (15.9)
subtype2	3	1.0 - 44.0 (7.0)
subtype3	7	2.0 - 35.0 (24.0)
subtype4	7	1.0 - 65.9 (19.5)
subtype5	8	1.0 - 34.9 (13.9)
subtype6	5	12.0 - 27.9 (26.4)
subtype7	4	1.0 - 33.0 (11.3)
subtype8	8	13.1 - 46.1 (22.2)
subtype9	4	17.5 - 54.9 (35.2)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.057 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	53	61.3 (7.2)
subtype1	7	64.6 (4.7)
subtype2	3	67.3 (6.4)
subtype3	7	65.9 (8.3)
subtype4	7	58.9 (4.5)
subtype5	8	55.8 (6.3)
subtype6	5	63.6 (8.6)
subtype7	4	62.2 (9.1)
subtype8	8	57.9 (6.7)
subtype9	4	61.5 (4.7)

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

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

P value = 0.61 (Chi-square test)

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

nPatients	NO	YES
ALL	4	49
subtype1	1	6
subtype2	1	2
subtype3	0	7
subtype4	1	6
subtype5	1	7
subtype6	0	5
subtype7	0	4
subtype8	0	8
subtype9	0	4

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.252 (Chi-square test)

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

nPatients	NO	YES
ALL	3	50
subtype1	0	7
subtype2	0	3
subtype3	2	5
subtype4	1	6
subtype5	0	8
subtype6	0	5
subtype7	0	4
subtype8	0	8
subtype9	0	4

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

Clustering Approach #3: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	11	10	6	26

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

	nPatients	Duration Range (Median), Month
ALL	53	1.0 - 65.9 (21.8)
subtype1	11	1.0 - 47.4 (15.9)
subtype2	10	1.0 - 65.9 (14.8)
subtype3	6	1.0 - 35.0 (24.0)
subtype4	26	1.0 - 54.9 (23.5)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0522 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	53	61.3 (7.2)
subtype1	11	62.0 (5.7)
subtype2	10	60.1 (8.3)
subtype3	6	68.5 (2.4)
subtype4	26	59.8 (7.3)

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

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

P value = 1 (Fisher's exact test)

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

nPatients	NO	YES
ALL	4	49
subtype1	1	10
subtype2	1	9
subtype3	0	6
subtype4	2	24

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

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0124 (Fisher's exact test)

Table S15. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	3	50
subtype1	0	11
subtype2	1	9
subtype3	2	4
subtype4	0	26

Figure S12. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'MIRseq CNMF subtypes'

Table S16. Get Full Table Description of clustering approach #4: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	18	28	19	16

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

	nPatients	Duration Range (Median), Month
ALL	81	0.9 - 65.9 (23.0)
subtype1	18	1.0 - 65.9 (20.8)
subtype2	28	0.9 - 39.3 (21.2)
subtype3	19	11.7 - 54.9 (19.9)
subtype4	16	8.0 - 47.4 (26.2)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.306 (ANOVA)

Table S18. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	81	61.1 (6.7)
subtype1	18	62.7 (6.7)
subtype2	28	61.6 (6.3)
subtype3	19	58.7 (7.1)
subtype4	16	61.4 (6.7)

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

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

P value = 1 (Fisher's exact test)

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

nPatients	NO	YES
ALL	5	76
subtype1	1	17
subtype2	2	26
subtype3	1	18
subtype4	1	15

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

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.249 (Fisher's exact test)

Table S20. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	3	78
subtype1	0	18
subtype2	3	25
subtype3	0	19
subtype4	0	16

Figure S16. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'MIRseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	15	32	34

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

	nPatients	Duration Range (Median), Month
ALL	81	0.9 - 65.9 (23.0)
subtype1	15	3.0 - 47.4 (25.9)
subtype2	32	0.9 - 65.9 (22.5)
subtype3	34	1.0 - 54.9 (19.7)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.138 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	81	61.1 (6.7)
subtype1	15	61.7 (6.1)
subtype2	32	62.6 (6.9)
subtype3	34	59.4 (6.6)

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

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

P value = 1 (Fisher's exact test)

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

nPatients	NO	YES
ALL	5	76
subtype1	1	14
subtype2	2	30
subtype3	2	32

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

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.145 (Fisher's exact test)

Table S25. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	3	78
subtype1	0	15
subtype2	3	29
subtype3	0	34

Figure S20. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Methods & Data

Input

Cluster data file = PRAD.mergedcluster.txt
Clinical data file = PRAD.clin.merged.picked.txt
Number of patients = 124
Number of clustering approaches = 5
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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

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)

[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

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