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

This is an experimental feature. Location of data archives could not be determined.

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)
Meta

- Maintainer = TCGA GDAC Team

Copyright © 2011 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
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