This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 8 different clustering approaches and 6 clinical features across 156 patients, 4 significant findings 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 correlate to 'AGE', 'LYMPH.NODE.METASTASIS', and 'NUMBER.OF.LYMPH.NODES'.
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4 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.
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5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
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2 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.
Clinical Features |
Time to Death |
AGE |
LYMPH NODE METASTASIS |
COMPLETENESS OF RESECTION |
NUMBER OF LYMPH NODES |
TUMOR STAGECODE |
Statistical Tests | logrank test | t-test | Fisher's exact test | Chi-square test | t-test | t-test |
Copy Number Ratio CNMF subtypes |
100 (1.00) |
0.00635 (0.235) |
0.00524 (0.204) |
0.647 (1.00) |
0.00336 (0.134) |
|
METHLYATION CNMF |
100 (1.00) |
0.00605 (0.23) |
0.152 (1.00) |
0.456 (1.00) |
0.229 (1.00) |
|
RNAseq CNMF subtypes |
100 (1.00) |
0.211 (1.00) |
0.169 (1.00) |
0.258 (1.00) |
0.173 (1.00) |
|
RNAseq cHierClus subtypes |
100 (1.00) |
0.871 (1.00) |
0.0567 (1.00) |
0.166 (1.00) |
0.157 (1.00) |
|
MIRSEQ CNMF |
100 (1.00) |
0.0345 (1.00) |
0.362 (1.00) |
0.454 (1.00) |
0.0522 (1.00) |
|
MIRSEQ CHIERARCHICAL |
100 (1.00) |
0.428 (1.00) |
0.215 (1.00) |
0.697 (1.00) |
0.381 (1.00) |
|
MIRseq Mature CNMF subtypes |
100 (1.00) |
0.204 (1.00) |
0.0787 (1.00) |
0.514 (1.00) |
0.0786 (1.00) |
|
MIRseq Mature cHierClus subtypes |
100 (1.00) |
0.187 (1.00) |
0.265 (1.00) |
0.452 (1.00) |
0.0782 (1.00) |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 28 | 72 | 53 | 2 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 153 | 1 | 0.3 - 66.0 (15.0) |
subtype1 | 28 | 0 | 0.3 - 63.3 (11.2) |
subtype2 | 72 | 0 | 1.0 - 65.9 (16.9) |
subtype3 | 53 | 1 | 0.9 - 66.0 (16.0) |
P value = 0.00635 (ANOVA), Q value = 0.24
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 152 | 60.6 (6.7) |
subtype1 | 28 | 62.1 (5.7) |
subtype2 | 71 | 58.8 (7.1) |
subtype3 | 53 | 62.2 (6.1) |
P value = 0.00524 (Fisher's exact test), Q value = 0.2
nPatients | N0 | N1 |
---|---|---|
ALL | 119 | 15 |
subtype1 | 26 | 0 |
subtype2 | 55 | 4 |
subtype3 | 38 | 11 |
P value = 0.647 (Chi-square test), Q value = 1
nPatients | R0 | R1 | RX |
---|---|---|---|
ALL | 116 | 29 | 2 |
subtype1 | 19 | 6 | 0 |
subtype2 | 56 | 13 | 2 |
subtype3 | 41 | 10 | 0 |
P value = 0.00336 (ANOVA), Q value = 0.13
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 134 | 0.2 (0.7) |
subtype1 | 26 | 0.0 (0.0) |
subtype2 | 59 | 0.1 (0.3) |
subtype3 | 49 | 0.5 (1.2) |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 48 | 52 | 43 | 13 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 156 | 1 | 0.3 - 66.0 (14.9) |
subtype1 | 48 | 0 | 0.3 - 65.9 (15.0) |
subtype2 | 52 | 1 | 1.0 - 66.0 (14.8) |
subtype3 | 43 | 0 | 1.1 - 54.9 (14.8) |
subtype4 | 13 | 0 | 1.8 - 33.1 (13.1) |
P value = 0.00605 (ANOVA), Q value = 0.23
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 155 | 60.4 (6.9) |
subtype1 | 47 | 61.6 (6.2) |
subtype2 | 52 | 61.3 (6.9) |
subtype3 | 43 | 57.4 (7.0) |
subtype4 | 13 | 62.6 (5.8) |
P value = 0.152 (Fisher's exact test), Q value = 1
nPatients | N0 | N1 |
---|---|---|
ALL | 122 | 15 |
subtype1 | 38 | 5 |
subtype2 | 39 | 6 |
subtype3 | 35 | 1 |
subtype4 | 10 | 3 |
P value = 0.456 (Chi-square test), Q value = 1
nPatients | R0 | R1 | RX |
---|---|---|---|
ALL | 118 | 30 | 2 |
subtype1 | 36 | 11 | 0 |
subtype2 | 40 | 9 | 0 |
subtype3 | 32 | 8 | 2 |
subtype4 | 10 | 2 | 0 |
P value = 0.229 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 137 | 0.2 (0.7) |
subtype1 | 43 | 0.2 (0.5) |
subtype2 | 45 | 0.4 (1.1) |
subtype3 | 36 | 0.0 (0.2) |
subtype4 | 13 | 0.3 (0.6) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 51 | 49 | 54 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 154 | 1 | 0.3 - 66.0 (14.8) |
subtype1 | 51 | 0 | 0.3 - 65.9 (16.0) |
subtype2 | 49 | 0 | 1.0 - 62.4 (12.8) |
subtype3 | 54 | 1 | 1.0 - 66.0 (14.8) |
P value = 0.211 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 153 | 60.5 (6.9) |
subtype1 | 50 | 60.8 (6.6) |
subtype2 | 49 | 59.1 (7.1) |
subtype3 | 54 | 61.4 (6.8) |
P value = 0.169 (Fisher's exact test), Q value = 1
nPatients | N0 | N1 |
---|---|---|
ALL | 121 | 15 |
subtype1 | 39 | 5 |
subtype2 | 42 | 2 |
subtype3 | 40 | 8 |
P value = 0.258 (Chi-square test), Q value = 1
nPatients | R0 | R1 | RX |
---|---|---|---|
ALL | 117 | 29 | 2 |
subtype1 | 39 | 12 | 0 |
subtype2 | 38 | 7 | 2 |
subtype3 | 40 | 10 | 0 |
P value = 0.173 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 136 | 0.2 (0.7) |
subtype1 | 44 | 0.2 (0.9) |
subtype2 | 44 | 0.0 (0.2) |
subtype3 | 48 | 0.3 (0.8) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 43 | 65 | 46 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 154 | 1 | 0.3 - 66.0 (14.8) |
subtype1 | 43 | 0 | 1.0 - 65.9 (16.0) |
subtype2 | 65 | 1 | 0.9 - 66.0 (13.1) |
subtype3 | 46 | 0 | 0.3 - 62.4 (12.4) |
P value = 0.871 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 153 | 60.5 (6.9) |
subtype1 | 42 | 60.7 (6.9) |
subtype2 | 65 | 60.6 (6.9) |
subtype3 | 46 | 60.0 (6.9) |
P value = 0.0567 (Fisher's exact test), Q value = 1
nPatients | N0 | N1 |
---|---|---|
ALL | 121 | 15 |
subtype1 | 33 | 4 |
subtype2 | 48 | 10 |
subtype3 | 40 | 1 |
P value = 0.166 (Chi-square test), Q value = 1
nPatients | R0 | R1 | RX |
---|---|---|---|
ALL | 117 | 29 | 2 |
subtype1 | 32 | 11 | 0 |
subtype2 | 49 | 12 | 0 |
subtype3 | 36 | 6 | 2 |
P value = 0.157 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 136 | 0.2 (0.7) |
subtype1 | 37 | 0.2 (1.0) |
subtype2 | 58 | 0.3 (0.8) |
subtype3 | 41 | 0.0 (0.2) |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 44 | 40 | 26 | 45 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 155 | 1 | 0.3 - 66.0 (14.8) |
subtype1 | 44 | 0 | 0.9 - 66.0 (15.8) |
subtype2 | 40 | 0 | 1.0 - 54.9 (19.1) |
subtype3 | 26 | 0 | 0.3 - 64.1 (16.0) |
subtype4 | 45 | 1 | 1.0 - 66.0 (5.6) |
P value = 0.0345 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 154 | 60.4 (6.8) |
subtype1 | 44 | 62.8 (6.2) |
subtype2 | 40 | 58.7 (6.7) |
subtype3 | 26 | 59.4 (6.8) |
subtype4 | 44 | 60.0 (7.1) |
P value = 0.362 (Fisher's exact test), Q value = 1
nPatients | N0 | N1 |
---|---|---|
ALL | 122 | 14 |
subtype1 | 34 | 7 |
subtype2 | 33 | 4 |
subtype3 | 20 | 1 |
subtype4 | 35 | 2 |
P value = 0.454 (Chi-square test), Q value = 1
nPatients | R0 | R1 | RX |
---|---|---|---|
ALL | 118 | 29 | 2 |
subtype1 | 33 | 10 | 0 |
subtype2 | 30 | 8 | 1 |
subtype3 | 18 | 7 | 0 |
subtype4 | 37 | 4 | 1 |
P value = 0.0522 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 136 | 0.2 (0.7) |
subtype1 | 41 | 0.5 (1.2) |
subtype2 | 37 | 0.1 (0.3) |
subtype3 | 21 | 0.0 (0.2) |
subtype4 | 37 | 0.1 (0.4) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 31 | 59 | 65 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 155 | 1 | 0.3 - 66.0 (14.8) |
subtype1 | 31 | 0 | 0.3 - 64.1 (18.2) |
subtype2 | 59 | 0 | 1.0 - 54.9 (19.5) |
subtype3 | 65 | 1 | 0.9 - 66.0 (5.6) |
P value = 0.428 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 154 | 60.4 (6.8) |
subtype1 | 30 | 59.1 (7.1) |
subtype2 | 59 | 60.3 (6.8) |
subtype3 | 65 | 61.1 (6.8) |
P value = 0.215 (Fisher's exact test), Q value = 1
nPatients | N0 | N1 |
---|---|---|
ALL | 122 | 14 |
subtype1 | 22 | 1 |
subtype2 | 46 | 9 |
subtype3 | 54 | 4 |
P value = 0.697 (Chi-square test), Q value = 1
nPatients | R0 | R1 | RX |
---|---|---|---|
ALL | 118 | 29 | 2 |
subtype1 | 22 | 8 | 0 |
subtype2 | 46 | 12 | 1 |
subtype3 | 50 | 9 | 1 |
P value = 0.381 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 136 | 0.2 (0.7) |
subtype1 | 23 | 0.0 (0.2) |
subtype2 | 55 | 0.3 (0.8) |
subtype3 | 58 | 0.2 (0.8) |
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 44 | 22 | 22 | 21 | 46 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 155 | 1 | 0.3 - 66.0 (14.8) |
subtype1 | 44 | 0 | 0.9 - 65.9 (18.1) |
subtype2 | 22 | 0 | 7.0 - 54.9 (21.8) |
subtype3 | 22 | 0 | 1.0 - 61.1 (14.2) |
subtype4 | 21 | 0 | 0.3 - 65.9 (15.0) |
subtype5 | 46 | 1 | 1.0 - 66.0 (5.6) |
P value = 0.204 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 154 | 60.4 (6.8) |
subtype1 | 44 | 61.6 (6.4) |
subtype2 | 22 | 59.9 (7.4) |
subtype3 | 22 | 59.5 (8.0) |
subtype4 | 21 | 57.6 (6.7) |
subtype5 | 45 | 61.1 (6.3) |
P value = 0.0787 (Chi-square test), Q value = 1
nPatients | N0 | N1 |
---|---|---|
ALL | 122 | 14 |
subtype1 | 33 | 7 |
subtype2 | 18 | 2 |
subtype3 | 17 | 4 |
subtype4 | 19 | 0 |
subtype5 | 35 | 1 |
P value = 0.514 (Chi-square test), Q value = 1
nPatients | R0 | R1 | RX |
---|---|---|---|
ALL | 118 | 29 | 2 |
subtype1 | 34 | 10 | 0 |
subtype2 | 16 | 5 | 1 |
subtype3 | 16 | 2 | 1 |
subtype4 | 16 | 5 | 0 |
subtype5 | 36 | 7 | 0 |
P value = 0.0786 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 136 | 0.2 (0.7) |
subtype1 | 40 | 0.3 (0.9) |
subtype2 | 20 | 0.1 (0.3) |
subtype3 | 21 | 0.5 (1.4) |
subtype4 | 19 | 0.0 (0.0) |
subtype5 | 36 | 0.0 (0.2) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 2 | 81 | 72 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 153 | 1 | 0.3 - 66.0 (15.0) |
subtype2 | 81 | 0 | 0.9 - 65.9 (23.8) |
subtype3 | 72 | 1 | 0.3 - 66.0 (5.7) |
P value = 0.187 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 152 | 60.4 (6.8) |
subtype2 | 81 | 61.1 (6.7) |
subtype3 | 71 | 59.7 (7.0) |
P value = 0.265 (Fisher's exact test), Q value = 1
nPatients | N0 | N1 |
---|---|---|
ALL | 120 | 14 |
subtype2 | 65 | 10 |
subtype3 | 55 | 4 |
P value = 0.452 (Chi-square test), Q value = 1
nPatients | R0 | R1 | RX |
---|---|---|---|
ALL | 116 | 29 | 2 |
subtype2 | 61 | 19 | 1 |
subtype3 | 55 | 10 | 1 |
P value = 0.0782 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 134 | 0.2 (0.7) |
subtype2 | 75 | 0.3 (0.9) |
subtype3 | 59 | 0.1 (0.3) |
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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 = 156
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Number of clustering approaches = 8
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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 multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R
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
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.