This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 6 different clustering approaches and 3 clinical features across 147 patients, one significant finding detected with P value < 0.05 and Q value < 0.25.
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3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
Clinical Features |
Time to Death |
AGE |
RADIATIONS RADIATION REGIMENINDICATION |
Statistical Tests | logrank test | ANOVA | Fisher's exact test |
Copy Number Ratio CNMF subtypes |
100 (1.00) |
0.0253 (0.429) |
0.853 (1.00) |
METHLYATION CNMF |
100 (1.00) |
0.00439 (0.0789) |
0.861 (1.00) |
RNAseq CNMF subtypes |
100 (1.00) |
0.207 (1.00) |
0.86 (1.00) |
RNAseq cHierClus subtypes |
100 (1.00) |
0.302 (1.00) |
1 (1.00) |
MIRSEQ CNMF |
100 (1.00) |
0.0907 (1.00) |
0.576 (1.00) |
MIRSEQ CHIERARCHICAL |
100 (1.00) |
0.436 (1.00) |
0.846 (1.00) |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 32 | 67 | 46 | 1 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 145 | 1 | 0.3 - 66.0 (16.8) |
subtype1 | 32 | 0 | 0.3 - 63.3 (11.2) |
subtype2 | 67 | 0 | 1.0 - 65.9 (17.6) |
subtype3 | 46 | 1 | 0.9 - 66.0 (16.8) |
P value = 0.0253 (ANOVA), Q value = 0.43
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 145 | 60.5 (6.9) |
subtype1 | 32 | 61.6 (6.4) |
subtype2 | 67 | 58.8 (7.4) |
subtype3 | 46 | 62.1 (6.0) |
P value = 0.853 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 5 | 140 |
subtype1 | 1 | 31 |
subtype2 | 3 | 64 |
subtype3 | 1 | 45 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 47 | 43 | 57 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 147 | 1 | 0.3 - 66.0 (16.6) |
subtype1 | 47 | 0 | 0.3 - 65.9 (15.9) |
subtype2 | 43 | 0 | 1.1 - 54.9 (13.1) |
subtype3 | 57 | 1 | 1.0 - 66.0 (17.5) |
P value = 0.00439 (ANOVA), Q value = 0.079
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 147 | 60.4 (7.0) |
subtype1 | 47 | 61.9 (6.3) |
subtype2 | 43 | 57.5 (7.2) |
subtype3 | 57 | 61.4 (6.8) |
P value = 0.861 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 5 | 142 |
subtype1 | 1 | 46 |
subtype2 | 2 | 41 |
subtype3 | 2 | 55 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 40 | 35 | 42 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 117 | 1 | 0.3 - 66.0 (14.8) |
subtype1 | 40 | 0 | 0.3 - 65.9 (16.0) |
subtype2 | 35 | 0 | 1.0 - 54.9 (13.0) |
subtype3 | 42 | 1 | 1.0 - 66.0 (14.8) |
P value = 0.207 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 117 | 60.7 (7.2) |
subtype1 | 40 | 61.5 (6.9) |
subtype2 | 35 | 58.9 (7.3) |
subtype3 | 42 | 61.4 (7.2) |
P value = 0.86 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 5 | 112 |
subtype1 | 1 | 39 |
subtype2 | 2 | 33 |
subtype3 | 2 | 40 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 34 | 36 | 47 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 117 | 1 | 0.3 - 66.0 (14.8) |
subtype1 | 34 | 0 | 1.0 - 65.9 (15.5) |
subtype2 | 36 | 1 | 1.0 - 66.0 (13.0) |
subtype3 | 47 | 0 | 0.3 - 54.9 (13.1) |
P value = 0.302 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 117 | 60.7 (7.2) |
subtype1 | 34 | 60.9 (7.1) |
subtype2 | 36 | 62.0 (7.5) |
subtype3 | 47 | 59.5 (6.9) |
P value = 1 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 5 | 112 |
subtype1 | 1 | 33 |
subtype2 | 2 | 34 |
subtype3 | 2 | 45 |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 44 | 41 | 22 | 39 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 146 | 1 | 0.3 - 66.0 (16.3) |
subtype1 | 44 | 0 | 0.9 - 66.0 (22.5) |
subtype2 | 41 | 0 | 1.0 - 54.9 (19.5) |
subtype3 | 22 | 0 | 0.3 - 65.9 (16.6) |
subtype4 | 39 | 1 | 1.0 - 66.0 (5.6) |
P value = 0.0907 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 146 | 60.4 (7.0) |
subtype1 | 44 | 62.1 (6.4) |
subtype2 | 41 | 59.3 (6.9) |
subtype3 | 22 | 58.0 (7.2) |
subtype4 | 39 | 60.8 (7.2) |
P value = 0.576 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 5 | 141 |
subtype1 | 2 | 42 |
subtype2 | 2 | 39 |
subtype3 | 1 | 21 |
subtype4 | 0 | 39 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 26 | 67 | 53 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 146 | 1 | 0.3 - 66.0 (16.3) |
subtype1 | 26 | 0 | 0.3 - 64.1 (17.8) |
subtype2 | 67 | 0 | 0.9 - 65.9 (19.9) |
subtype3 | 53 | 1 | 1.0 - 66.0 (7.5) |
P value = 0.436 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 146 | 60.4 (7.0) |
subtype1 | 26 | 59.0 (6.6) |
subtype2 | 67 | 60.3 (6.7) |
subtype3 | 53 | 61.2 (7.4) |
P value = 0.846 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 5 | 141 |
subtype1 | 1 | 25 |
subtype2 | 3 | 64 |
subtype3 | 1 | 52 |
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Cluster data file = PRAD-TP.mergedcluster.txt
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Clinical data file = PRAD-TP.clin.merged.picked.txt
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Number of patients = 147
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Number of clustering approaches = 6
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Number of selected clinical features = 3
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Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
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
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
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
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.