Correlation between aggregated 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 8 different clustering approaches and 3 clinical features across 82 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'GENDER'.
3 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 'GENDER'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes 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 8 different clustering approaches and 3 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 2 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER
Statistical Tests	logrank test	t-test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.354 (1.00)	0.366 (1.00)	0.00039 (0.00937)
METHLYATION CNMF	0.376 (1.00)	0.0844 (1.00)	0.328 (1.00)
RNAseq CNMF subtypes	0.812 (1.00)	0.711 (1.00)	0.0014 (0.0321)
RNAseq cHierClus subtypes	0.369 (1.00)	0.0302 (0.665)	0.0922 (1.00)
MIRSEQ CNMF	0.0512 (1.00)	0.145 (1.00)	0.187 (1.00)
MIRSEQ CHIERARCHICAL	0.833 (1.00)	0.229 (1.00)	0.213 (1.00)
MIRseq Mature CNMF subtypes	0.533 (1.00)	0.163 (1.00)	0.0422 (0.887)
MIRseq Mature cHierClus subtypes	0.99 (1.00)	0.35 (1.00)	0.0563 (1.00)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	29	30	23

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.354 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	82	24	0.1 - 143.4 (18.1)
subtype1	29	10	0.5 - 108.1 (15.0)
subtype2	30	9	0.1 - 74.7 (22.0)
subtype3	23	5	0.1 - 143.4 (25.1)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.366 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	82	62.1 (12.8)
subtype1	29	63.1 (13.3)
subtype2	30	63.6 (11.5)
subtype3	23	58.9 (13.8)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00039 (Fisher's exact test), Q value = 0.0094

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	40	42
subtype1	8	21
subtype2	23	7
subtype3	9	14

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #2: 'METHLYATION CNMF'

Table S5. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	24	25	33

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.376 (logrank test), Q value = 1

Table S6. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	82	24	0.1 - 143.4 (18.1)
subtype1	24	8	1.7 - 76.4 (25.7)
subtype2	25	8	0.1 - 143.4 (10.7)
subtype3	33	8	0.1 - 116.9 (25.1)

Figure S4. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0844 (ANOVA), Q value = 1

Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	82	62.1 (12.8)
subtype1	24	63.9 (14.7)
subtype2	25	65.4 (10.5)
subtype3	33	58.4 (12.4)

Figure S5. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.328 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	40	42
subtype1	9	15
subtype2	12	13
subtype3	19	14

Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S9. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	30	21	21

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.812 (logrank test), Q value = 1

Table S10. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	72	23	0.1 - 143.4 (19.3)
subtype1	30	11	1.7 - 108.1 (18.1)
subtype2	21	6	0.1 - 143.4 (12.8)
subtype3	21	6	0.1 - 75.5 (26.5)

Figure S7. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.711 (ANOVA), Q value = 1

Table S11. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	72	61.5 (12.5)
subtype1	30	62.6 (13.7)
subtype2	21	61.8 (11.2)
subtype3	21	59.6 (12.4)

Figure S8. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0014 (Fisher's exact test), Q value = 0.032

Table S12. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	35
subtype1	9	21
subtype2	17	4
subtype3	11	10

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S13. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	29	41	2

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.369 (logrank test), Q value = 1

Table S14. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	70	22	0.1 - 143.4 (19.3)
subtype1	29	8	0.1 - 116.9 (25.2)
subtype2	41	14	0.1 - 143.4 (15.0)

Figure S10. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0302 (t-test), Q value = 0.66

Table S15. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	70	61.8 (12.5)
subtype1	29	58.0 (12.3)
subtype2	41	64.6 (12.1)

Figure S11. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0922 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	33
subtype1	19	10
subtype2	18	23

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

Clustering Approach #5: 'MIRSEQ CNMF'

Table S17. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	6	31	12	19

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0512 (logrank test), Q value = 1

Table S18. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	68	21	0.1 - 143.4 (18.9)
subtype1	6	3	0.1 - 14.0 (8.0)
subtype2	31	10	0.5 - 143.4 (19.3)
subtype3	12	4	4.5 - 70.5 (23.6)
subtype4	19	4	0.1 - 116.9 (24.3)

Figure S13. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.145 (ANOVA), Q value = 1

Table S19. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	68	62.8 (12.2)
subtype1	6	71.5 (13.6)
subtype2	31	63.7 (10.9)
subtype3	12	62.2 (11.0)
subtype4	19	58.7 (13.7)

Figure S14. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.187 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	33
subtype1	4	2
subtype2	14	17
subtype3	4	8
subtype4	13	6

Figure S15. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S21. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	2	28	38

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.833 (logrank test), Q value = 1

Table S22. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	66	21	0.1 - 143.4 (19.7)
subtype2	28	9	0.1 - 116.9 (18.2)
subtype3	38	12	0.1 - 143.4 (19.7)

Figure S16. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.229 (t-test), Q value = 1

Table S23. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	66	62.8 (12.4)
subtype2	28	60.5 (14.0)
subtype3	38	64.4 (10.9)

Figure S17. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.213 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	33
subtype2	17	11
subtype3	16	22

Figure S18. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S25. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	13	31	24

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.533 (logrank test), Q value = 1

Table S26. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	68	21	0.1 - 143.4 (18.9)
subtype1	13	5	3.2 - 70.5 (14.0)
subtype2	31	10	0.1 - 143.4 (19.3)
subtype3	24	6	0.1 - 116.9 (23.4)

Figure S19. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.163 (ANOVA), Q value = 1

Table S27. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	68	62.8 (12.2)
subtype1	13	66.1 (13.4)
subtype2	31	64.2 (11.0)
subtype3	24	59.0 (12.5)

Figure S20. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0422 (Fisher's exact test), Q value = 0.89

Table S28. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	33
subtype1	4	9
subtype2	14	17
subtype3	17	7

Figure S21. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S29. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	2	3
Number of samples	37	31

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.99 (logrank test), Q value = 1

Table S30. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	68	21	0.1 - 143.4 (18.9)
subtype2	37	12	0.1 - 143.4 (19.3)
subtype3	31	9	0.1 - 116.9 (15.0)

Figure S22. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.35 (t-test), Q value = 1

Table S31. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	68	62.8 (12.2)
subtype2	37	64.1 (10.9)
subtype3	31	61.2 (13.7)

Figure S23. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0563 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	35	33
subtype2	15	22
subtype3	20	11

Figure S24. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Methods & Data

Input

Cluster data file = SARC-TP.mergedcluster.txt
Clinical data file = SARC-TP.merged_data.txt
Number of patients = 82
Number of clustering approaches = 8
Number of selected clinical features = 3
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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