(primary solid tumor cohort)
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 6 clinical features across 32 patients, no 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 'CN CNMF'. These subtypes do not correlate to any clinical features.
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4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
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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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CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
Clinical Features |
Time to Death |
AGE |
RADIATIONS RADIATION REGIMENINDICATION |
NUMBERPACKYEARSSMOKED | STOPPEDSMOKINGYEAR | TOBACCOSMOKINGHISTORYINDICATOR |
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | ANOVA | ANOVA |
CN CNMF |
100 (1.00) |
0.982 (1.00) |
1 (1.00) |
0.524 (1.00) |
||
METHLYATION CNMF |
0.0604 (1.00) |
0.817 (1.00) |
0.789 (1.00) |
0.834 (1.00) |
||
RNAseq CNMF subtypes |
100 (1.00) |
0.837 (1.00) |
0.0228 (0.548) |
0.966 (1.00) |
||
RNAseq cHierClus subtypes |
100 (1.00) |
0.617 (1.00) |
1 (1.00) |
0.637 (1.00) |
||
MIRseq CNMF subtypes |
0.956 (1.00) |
0.42 (1.00) |
1 (1.00) |
0.862 (1.00) |
||
MIRseq cHierClus subtypes |
0.183 (1.00) |
0.259 (1.00) |
0.856 (1.00) |
0.975 (1.00) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 11 | 3 | 12 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 25 | 1 | 0.1 - 95.1 (2.7) |
subtype1 | 10 | 0 | 0.3 - 49.5 (2.2) |
subtype2 | 3 | 0 | 1.4 - 70.8 (69.9) |
subtype3 | 12 | 1 | 0.1 - 95.1 (3.8) |
P value = 0.982 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 26 | 48.5 (11.8) |
subtype1 | 11 | 48.5 (11.0) |
subtype2 | 3 | 49.7 (4.2) |
subtype3 | 12 | 48.2 (14.3) |
P value = 1 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 4 | 22 |
subtype1 | 2 | 9 |
subtype2 | 0 | 3 |
subtype3 | 2 | 10 |
P value = 0.524 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 25 | 1.8 (1.1) |
subtype1 | 10 | 1.7 (1.1) |
subtype2 | 3 | 1.3 (0.6) |
subtype3 | 12 | 2.1 (1.2) |
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 6 | 4 | 12 | 8 | 2 |
P value = 0.0604 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 29 | 4 | 0.1 - 101.8 (6.0) |
subtype1 | 6 | 0 | 2.7 - 101.8 (45.9) |
subtype2 | 4 | 0 | 1.0 - 95.1 (21.4) |
subtype3 | 11 | 1 | 0.6 - 36.8 (2.7) |
subtype4 | 8 | 3 | 0.1 - 69.9 (5.2) |
P value = 0.817 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 30 | 50.4 (12.4) |
subtype1 | 6 | 48.5 (10.0) |
subtype2 | 4 | 51.5 (8.3) |
subtype3 | 12 | 48.8 (12.8) |
subtype4 | 8 | 53.9 (16.1) |
P value = 0.789 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 8 | 22 |
subtype1 | 1 | 5 |
subtype2 | 2 | 2 |
subtype3 | 3 | 9 |
subtype4 | 2 | 6 |
P value = 0.834 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 27 | 2.0 (1.2) |
subtype1 | 5 | 2.4 (1.5) |
subtype2 | 4 | 2.0 (1.4) |
subtype3 | 11 | 2.0 (1.2) |
subtype4 | 7 | 1.7 (1.1) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 16 | 5 | 7 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 27 | 2 | 0.1 - 101.8 (2.7) |
subtype1 | 15 | 1 | 0.1 - 70.8 (1.7) |
subtype2 | 5 | 1 | 12.4 - 95.1 (30.4) |
subtype3 | 7 | 0 | 1.0 - 101.8 (2.2) |
P value = 0.837 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 28 | 48.1 (10.6) |
subtype1 | 16 | 47.4 (11.4) |
subtype2 | 5 | 47.6 (2.2) |
subtype3 | 7 | 50.3 (13.0) |
P value = 0.0228 (Fisher's exact test), Q value = 0.55
nPatients | NO | YES |
---|---|---|
ALL | 6 | 22 |
subtype1 | 1 | 15 |
subtype2 | 3 | 2 |
subtype3 | 2 | 5 |
P value = 0.966 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 25 | 2.0 (1.2) |
subtype1 | 14 | 2.0 (1.2) |
subtype2 | 4 | 2.0 (1.4) |
subtype3 | 7 | 1.9 (1.1) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 4 | 5 | 19 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 27 | 2 | 0.1 - 101.8 (2.7) |
subtype1 | 4 | 0 | 0.1 - 95.1 (35.5) |
subtype2 | 5 | 1 | 1.2 - 101.8 (2.2) |
subtype3 | 18 | 1 | 0.3 - 70.8 (4.3) |
P value = 0.617 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 28 | 48.1 (10.6) |
subtype1 | 4 | 43.5 (9.8) |
subtype2 | 5 | 47.4 (12.3) |
subtype3 | 19 | 49.3 (10.6) |
P value = 1 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 6 | 22 |
subtype1 | 1 | 3 |
subtype2 | 1 | 4 |
subtype3 | 4 | 15 |
P value = 0.637 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 25 | 2.0 (1.2) |
subtype1 | 4 | 2.0 (1.4) |
subtype2 | 5 | 2.4 (1.5) |
subtype3 | 16 | 1.8 (1.0) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 11 | 14 | 7 |
P value = 0.956 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 31 | 4 | 0.1 - 101.8 (5.8) |
subtype1 | 11 | 2 | 0.1 - 95.1 (12.4) |
subtype2 | 13 | 2 | 0.6 - 101.8 (5.5) |
subtype3 | 7 | 0 | 1.0 - 6.0 (1.2) |
P value = 0.42 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 32 | 49.9 (12.7) |
subtype1 | 11 | 47.5 (12.6) |
subtype2 | 14 | 53.3 (11.9) |
subtype3 | 7 | 46.9 (14.6) |
P value = 1 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 8 | 24 |
subtype1 | 3 | 8 |
subtype2 | 3 | 11 |
subtype3 | 2 | 5 |
P value = 0.862 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 29 | 2.0 (1.2) |
subtype1 | 10 | 1.8 (1.2) |
subtype2 | 13 | 2.1 (1.2) |
subtype3 | 6 | 2.0 (1.3) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 3 | 18 | 11 |
P value = 0.183 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 31 | 4 | 0.1 - 101.8 (5.8) |
subtype1 | 3 | 0 | 1.2 - 6.0 (2.2) |
subtype2 | 18 | 4 | 0.3 - 101.8 (7.3) |
subtype3 | 10 | 0 | 0.1 - 95.1 (4.8) |
P value = 0.259 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 32 | 49.9 (12.7) |
subtype1 | 3 | 43.0 (13.7) |
subtype2 | 18 | 53.1 (13.6) |
subtype3 | 11 | 46.5 (10.2) |
P value = 0.856 (Fisher's exact test), Q value = 1
nPatients | NO | YES |
---|---|---|
ALL | 8 | 24 |
subtype1 | 1 | 2 |
subtype2 | 5 | 13 |
subtype3 | 2 | 9 |
P value = 0.975 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 29 | 2.0 (1.2) |
subtype1 | 3 | 2.0 (1.0) |
subtype2 | 17 | 2.0 (1.2) |
subtype3 | 9 | 1.9 (1.3) |
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Cluster data file = CESC-TP.mergedcluster.txt
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Clinical data file = CESC-TP.clin.merged.picked.txt
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Number of patients = 32
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Number of clustering approaches = 6
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Number of selected clinical features = 6
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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.