Lung Squamous Cell Carcinoma: Correlation between RPPA expression and clinical features
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 11 clinical features across 190 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

- 1 gene correlated to 'Time to Death'.

- MSH6|MSH6-R-C

- 4 genes correlated to 'AGE'.

- PCNA|PCNA-M-V , RAD51|RAD51-M-C , CCNE1|CYCLIN_E1-M-V , ERBB2|HER2_PY1248-R-V

- 1 gene correlated to 'GENDER'.

- NOTCH3|NOTCH3-R-C

- 5 genes correlated to 'HISTOLOGICAL.TYPE'.

- EGFR|EGFR_PY1173-R-C , CCNE2|CYCLIN_E2-R-C , NCOA3|AIB1-M-V , ARID1A|ARID1A-M-V , KIT|C-KIT-R-V

- 1 gene correlated to 'PATHOLOGY.T'.

- BID|BID-R-C

- 2 genes correlated to 'PATHOLOGY.N'.

- MAPK1 MAPK3|MAPK_PT202_Y204-R-V , STAT3|STAT3_PY705-R-V

- 3 genes correlated to 'TUMOR.STAGE'.

- MAPK1 MAPK3|MAPK_PT202_Y204-R-V , CCND1|CYCLIN_D1-R-V , STAT3|STAT3_PY705-R-V

- No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGICSPREAD(M)', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

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=1 | shorter survival | N=0 | longer survival | N=1
AGE | Spearman correlation test | N=4 | older | N=1 | younger | N=3
GENDER | t test | N=1 | male | N=1 | female | N=0
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
HISTOLOGICAL TYPE | ANOVA test | N=5 |  |  |  |
PATHOLOGY T | Spearman correlation test | N=1 | higher pT | N=1 | lower pT | N=0
PATHOLOGY N | Spearman correlation test | N=2 | higher pN | N=0 | lower pN | N=2
PATHOLOGICSPREAD(M) | t test | N=0 |  |  |  |
TUMOR STAGE | Spearman correlation test | N=3 | higher stage | N=1 | lower stage | N=2
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |  |  |  |
NEOADJUVANT THERAPY | t test | N=0 |  |  |  |

Clinical variable #1: 'Time to Death'

One gene related to 'Time to Death'.

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

Time to Death | Duration (Months) | 0-173.8 (median=16.6)
| censored | N = 105
| death | N = 72
| Significant markers | N = 1
| associated with shorter survival | 0
| associated with longer survival | 1

List of one gene significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
MSH6|MSH6-R-C | 0.5 | 0.0002504 | 0.044 | 0.342

Figure S1. Get High-res Image As an example, this figure shows the association of MSH6|MSH6-R-C to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 0.00025 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

4 genes related to 'AGE'.

Table S3. Basic characteristics of clinical feature: 'AGE'

AGE | Mean (SD) | 67.51 (9.4)
| Significant markers | N = 4
| pos. correlated | 1
| neg. correlated | 3

List of 4 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4. Get Full Table List of 4 genes significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
PCNA|PCNA-M-V | -0.3306 | 5.164e-06 | 0.000899
RAD51|RAD51-M-C | -0.2802 | 0.0001275 | 0.0221
CCNE1|CYCLIN_E1-M-V | -0.2715 | 0.0002089 | 0.0359
ERBB2|HER2_PY1248-R-V | 0.2673 | 0.0002649 | 0.0453

Figure S2. Get High-res Image As an example, this figure shows the association of PCNA|PCNA-M-V to 'AGE'. P value = 5.16e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

One gene related to 'GENDER'.

Table S5. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 48
| MALE | 142
| Significant markers | N = 1
| Higher in MALE | 1
| Higher in FEMALE | 0

List of one gene differentially expressed by 'GENDER'

Table S6. Get Full Table List of one gene differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
NOTCH3|NOTCH3-R-C | 3.75 | 0.0002692 | 0.0468 | 0.6463

Figure S3. Get High-res Image As an example, this figure shows the association of NOTCH3|NOTCH3-R-C to 'GENDER'. P value = 0.000269 with T-test analysis.

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

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

Table S7. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 28.79 (39)
| Significant markers | N = 0

Clinical variable #5: 'HISTOLOGICAL.TYPE'

5 genes related to 'HISTOLOGICAL.TYPE'.

Table S8. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE | Labels | N
| LUNG BASALOID SQUAMOUS CELL CARCINOMA | 3
| LUNG PAPILLARY SQUAMOUS CELL CARCINOMA | 1
| LUNG PAPILLARY SQUAMOUS CELL CARICNOMA | 1
| LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | 185
| Significant markers | N = 5

List of 5 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S9. Get Full Table List of 5 genes differentially expressed by 'HISTOLOGICAL.TYPE'

| ANOVA_P | Q
--- | --- | ---
EGFR|EGFR_PY1173-R-C | 1.793e-06 | 0.000312
CCNE2|CYCLIN_E2-R-C | 1.718e-05 | 0.00297
NCOA3|AIB1-M-V | 7.623e-05 | 0.0131
ARID1A|ARID1A-M-V | 8.367e-05 | 0.0143
KIT|C-KIT-R-V | 0.0001123 | 0.0191

Figure S4. Get High-res Image As an example, this figure shows the association of EGFR|EGFR_PY1173-R-C to 'HISTOLOGICAL.TYPE'. P value = 1.79e-06 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

One gene related to 'PATHOLOGY.T'.

Table S10. Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T | Mean (SD) | 1.99 (0.75)
|  | N
| T1 | 43
| T2 | 117
| T3 | 19
| T4 | 11
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S11. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
BID|BID-R-C | 0.2758 | 0.0001176 | 0.0205

Figure S5. Get High-res Image As an example, this figure shows the association of BID|BID-R-C to 'PATHOLOGY.T'. P value = 0.000118 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGY.N'

2 genes related to 'PATHOLOGY.N'.

Table S12. Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N | Mean (SD) | 0.48 (0.73)
|  | N
| N0 | 121
| N1 | 50
| N2 | 15
| N3 | 4
| Significant markers | N = 2
| pos. correlated | 0
| neg. correlated | 2

List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S13. Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
MAPK1 MAPK3|MAPK_PT202_Y204-R-V | -0.3195 | 6.994e-06 | 0.00122
STAT3|STAT3_PY705-R-V | -0.2693 | 0.0001722 | 0.0298

Figure S6. Get High-res Image As an example, this figure shows the association of MAPK1 MAPK3|MAPK_PT202_Y204-R-V to 'PATHOLOGY.N'. P value = 6.99e-06 with Spearman correlation analysis.

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

No gene related to 'PATHOLOGICSPREAD(M)'.

Table S14. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) | Labels | N
| M0 | 172
| MX | 15
| Significant markers | N = 0

Clinical variable #9: 'TUMOR.STAGE'

3 genes related to 'TUMOR.STAGE'.

Table S15. Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE | Mean (SD) | 1.68 (0.78)
|  | N
| Stage 1 | 96
| Stage 2 | 55
| Stage 3 | 36
| Significant markers | N = 3
| pos. correlated | 1
| neg. correlated | 2

List of 3 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S16. Get Full Table List of 3 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
MAPK1 MAPK3|MAPK_PT202_Y204-R-V | -0.2812 | 9.706e-05 | 0.0169
CCND1|CYCLIN_D1-R-V | 0.2783 | 0.0001149 | 0.0199
STAT3|STAT3_PY705-R-V | -0.2702 | 0.0001836 | 0.0316

Figure S7. Get High-res Image As an example, this figure shows the association of MAPK1 MAPK3|MAPK_PT202_Y204-R-V to 'TUMOR.STAGE'. P value = 9.71e-05 with Spearman correlation analysis.

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S17. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N
| NO | 8
| YES | 182
| Significant markers | N = 0

Clinical variable #11: 'NEOADJUVANT.THERAPY'

No gene related to 'NEOADJUVANT.THERAPY'.

Table S18. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY | Labels | N
| NO | 23
| YES | 167
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = LUSC.rppa.txt

- Clinical data file = LUSC.clin.merged.picked.txt

- Number of patients = 190

- Number of genes = 174

- Number of clinical features = 11

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
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