Lung Adenocarcinoma: Correlation between RPPA expression and clinical features
(primary solid tumor cohort)
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 174 genes and 14 clinical features across 236 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 2 genes correlated to 'AGE'.

    • PTGS2|COX-2-R-C ,  ARID1A|ARID1A-M-V

  • 1 gene correlated to 'GENDER'.

    • CLDN7|CLAUDIN-7-R-V

  • 9 genes correlated to 'HISTOLOGICAL.TYPE'.

    • PARP1|PARP_CLEAVED-M-C ,  ERCC1|ERCC1-M-C ,  CASP8|CASPASE-8-M-C ,  MET|C-MET-M-C ,  RB1|RB-M-V ,  ...

  • 1 gene correlated to 'PATHOLOGICSPREAD(M)'.

    • EGFR|EGFR_PY1173-R-C

  • 9 genes correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.

    • EIF4EBP1|4E-BP1-R-V ,  EIF4EBP1|4E-BP1_PT70-R-C ,  AKT1 AKT2 AKT3|AKT_PS473-R-V ,  EGFR|EGFR_PY1068-R-V ,  CASP7|CASPASE-7_CLEAVEDD198-R-C ,  ...

  • No genes correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'NUMBERPACKYEARSSMOKED', 'STOPPEDSMOKINGYEAR', and 'YEAROFTOBACCOSMOKINGONSET'.

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1.  Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=2 older N=0 younger N=2
GENDER t test N=1 male N=1 female N=0
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=9        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=1        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
STOPPEDSMOKINGYEAR Spearman correlation test   N=0        
TOBACCOSMOKINGHISTORYINDICATOR ANOVA test N=9        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0-224 (median=12.3)
  censored N = 147
  death N = 66
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 64.82 (9.9)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
PTGS2|COX-2-R-C -0.2905 1.504e-05 0.00262
ARID1A|ARID1A-M-V -0.2464 0.0002644 0.0457

Figure S1.  Get High-res Image As an example, this figure shows the association of PTGS2|COX-2-R-C to 'AGE'. P value = 1.5e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

One gene related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 131
  MALE 105
     
  Significant markers N = 1
  Higher in MALE 1
  Higher in FEMALE 0
List of one gene differentially expressed by 'GENDER'

Table S5.  Get Full Table List of one gene differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
CLDN7|CLAUDIN-7-R-V 3.71 0.0002821 0.0491 0.6035

Figure S2.  Get High-res Image As an example, this figure shows the association of CLDN7|CLAUDIN-7-R-V to 'GENDER'. P value = 0.000282 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 68 (36)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

9 genes related to 'HISTOLOGICAL.TYPE'.

Table S7.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG ACINAR ADENOCARCINOMA 6
  LUNG ADENOCARCINOMA MIXED SUBTYPE 52
  LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 150
  LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS 3
  LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS 10
  LUNG CLEAR CELL ADENOCARCINOMA 1
  LUNG MICROPAPILLARY ADENOCARCINOMA 2
  LUNG MUCINOUS ADENOCARCINOMA 2
  LUNG PAPILLARY ADENOCARCINOMA 6
  LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA 1
  MUCINOUS (COLLOID) ADENOCARCINOMA 3
     
  Significant markers N = 9
List of 9 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S8.  Get Full Table List of 9 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
PARP1|PARP_CLEAVED-M-C 1.496e-07 2.6e-05
ERCC1|ERCC1-M-C 1.768e-07 3.06e-05
CASP8|CASPASE-8-M-C 2.317e-07 3.99e-05
MET|C-MET-M-C 7.458e-07 0.000128
RB1|RB-M-V 1.256e-06 0.000214
SNAI2|SNAIL-M-C 4.051e-06 0.000685
C12ORF5|TIGAR-R-V 4.355e-05 0.00732
DIABLO|SMAC-M-V 9.081e-05 0.0152
FOXO3|FOXO3A-R-C 0.0001313 0.0218

Figure S3.  Get High-res Image As an example, this figure shows the association of PARP1|PARP_CLEAVED-M-C to 'HISTOLOGICAL.TYPE'. P value = 1.5e-07 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.91 (0.75)
  N
  T1 64
  T2 141
  T3 17
  T4 13
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.59 (0.8)
  N
  N0 140
  N1 46
  N2 43
  N3 1
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

One gene related to 'PATHOLOGICSPREAD(M)'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 165
  M1 12
  MX 55
     
  Significant markers N = 1
List of one gene differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S12.  Get Full Table List of one gene differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
EGFR|EGFR_PY1173-R-C 0.0001653 0.0288

Figure S4.  Get High-res Image As an example, this figure shows the association of EGFR|EGFR_PY1173-R-C to 'PATHOLOGICSPREAD(M)'. P value = 0.000165 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S13.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.8 (0.96)
  N
  Stage 1 122
  Stage 2 49
  Stage 3 49
  Stage 4 13
     
  Significant markers N = 0
Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S14.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 15
  YES 221
     
  Significant markers N = 0
Clinical variable #11: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S15.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 40.54 (27)
  Significant markers N = 0
Clinical variable #12: 'STOPPEDSMOKINGYEAR'

No gene related to 'STOPPEDSMOKINGYEAR'.

Table S16.  Basic characteristics of clinical feature: 'STOPPEDSMOKINGYEAR'

STOPPEDSMOKINGYEAR Mean (SD) 1994.3 (14)
  Significant markers N = 0
Clinical variable #13: 'TOBACCOSMOKINGHISTORYINDICATOR'

9 genes related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

Table S17.  Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'

TOBACCOSMOKINGHISTORYINDICATOR Labels N
  CURRENT REFORMED SMOKER FOR < OR = 15 YEARS 84
  CURRENT REFORMED SMOKER FOR > 15 YEARS 59
  CURRENT SMOKER 52
  LIFELONG NON-SMOKER 31
     
  Significant markers N = 9
List of 9 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

Table S18.  Get Full Table List of 9 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

ANOVA_P Q
EIF4EBP1|4E-BP1-R-V 2.226e-06 0.000387
EIF4EBP1|4E-BP1_PT70-R-C 2.068e-05 0.00358
AKT1 AKT2 AKT3|AKT_PS473-R-V 7.546e-05 0.0129
EGFR|EGFR_PY1068-R-V 7.482e-05 0.0129
CASP7|CASPASE-7_CLEAVEDD198-R-C 0.0001259 0.0214
JUN|C-JUN_PS73-R-C 0.0001316 0.0222
PDK1|PDK1_PS241-R-V 0.0001349 0.0227
MAPK9|JNK2-R-C 0.000177 0.0296
CHEK1|CHK1-R-C 0.0002427 0.0403

Figure S5.  Get High-res Image As an example, this figure shows the association of EIF4EBP1|4E-BP1-R-V to 'TOBACCOSMOKINGHISTORYINDICATOR'. P value = 2.23e-06 with ANOVA analysis.

Clinical variable #14: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S19.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1964.73 (14)
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = LUAD-TP.rppa.txt

  • Clinical data file = LUAD-TP.clin.merged.picked.txt

  • Number of patients = 236

  • Number of genes = 174

  • Number of clinical features = 14

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Q value calculation

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.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle