Kidney Renal Papillary 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 4 different clustering approaches and 4 clinical features across 16 patients, no significant finding detected with P value < 0.05.

CNMF clustering analysis on array-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-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 2 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 2 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 4 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	Time to Death	AGE	GENDER	PATHOLOGY T
Statistical Tests	logrank test	t-test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.666	0.182	0.585	0.0601
mRNA cHierClus subtypes	0.0699	0.948	1	0.131
MIRseq CNMF subtypes	0.197	0.374	0.0885	0.0601
MIRseq cHierClus subtypes	0.0833	0.647	0.245	0.308

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2
Number of samples	7	9

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.666 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	16	2	0.5 - 58.5 (7.8)
subtype1	7	1	0.5 - 53.8 (5.9)
subtype2	9	1	1.1 - 58.5 (10.8)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.182 (t-test)

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

	nPatients	Mean (Std.Dev)
ALL	16	57.9 (11.5)
subtype1	7	53.6 (10.3)
subtype2	9	61.3 (11.7)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.585 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	12	4
subtype1	6	1
subtype2	6	3

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

'mRNA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0601 (Fisher's exact test)

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2+T3
ALL	7	9
subtype1	1	6
subtype2	6	3

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S6. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	4	7	5

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0699 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	16	2	0.5 - 58.5 (7.8)
subtype1	4	1	10.8 - 58.5 (40.4)
subtype2	7	1	0.5 - 25.1 (4.4)
subtype3	5	0	0.7 - 53.8 (4.1)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.948 (ANOVA)

Table S8. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	16	57.9 (11.5)
subtype1	4	57.0 (5.0)
subtype2	7	57.4 (13.0)
subtype3	5	59.4 (14.8)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test)

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	12	4
subtype1	3	1
subtype2	5	2
subtype3	4	1

Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.131 (Fisher's exact test)

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2+T3
ALL	7	9
subtype1	3	1
subtype2	1	6
subtype3	3	2

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

Clustering Approach #3: 'MIRseq CNMF subtypes'

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

Cluster Labels	1	2
Number of samples	7	9

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.197 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	16	2	0.5 - 58.5 (7.8)
subtype1	7	1	0.7 - 30.3 (5.9)
subtype2	9	1	0.5 - 58.5 (10.8)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.374 (t-test)

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

	nPatients	Mean (Std.Dev)
ALL	16	57.9 (11.5)
subtype1	7	55.0 (10.4)
subtype2	9	60.2 (12.4)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0885 (Fisher's exact test)

Table S14. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	12	4
subtype1	7	0
subtype2	5	4

Figure S11. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.0601 (Fisher's exact test)

Table S15. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2+T3
ALL	7	9
subtype1	1	6
subtype2	6	3

Figure S12. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

Clustering Approach #4: 'MIRseq cHierClus subtypes'

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

Cluster Labels	1	2
Number of samples	5	11

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0833 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	16	2	0.5 - 58.5 (7.8)
subtype1	5	1	3.6 - 25.1 (5.9)
subtype2	11	1	0.5 - 58.5 (10.8)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.647 (t-test)

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

	nPatients	Mean (Std.Dev)
ALL	16	57.9 (11.5)
subtype1	5	55.8 (12.3)
subtype2	11	58.9 (11.6)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.245 (Fisher's exact test)

Table S19. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	12	4
subtype1	5	0
subtype2	7	4

Figure S15. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.308 (Fisher's exact test)

Table S20. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients	T1	T2+T3
ALL	7	9
subtype1	1	4
subtype2	6	5

Figure S16. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

Methods & Data

Input

Cluster data file = KIRP.mergedcluster.txt
Clinical data file = KIRP.clin.merged.picked.txt
Number of patients = 16
Number of clustering approaches = 4
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

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

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

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

Download Results

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

[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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)