Breast Invasive Carcinoma: Correlation between gene mutation status and selected clinical features<BR>(primary solid tumor cohort)

Breast Invasive Carcinoma: Correlation between gene mutation status and selected clinical features
(primary solid tumor cohort)

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Overview

Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 52 genes and 4 clinical features across 507 patients, one significant finding detected with Q value < 0.25.

MAP3K1 mutation correlated to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 52 genes and 4 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.


		Clinical Features	Time to Death	AGE	GENDER	RADIATIONS RADIATION REGIMENINDICATION
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test
MAP3K1	38 (7%)	469	0.916 (1.00)	0.000845 (0.176)	1 (1.00)	0.346 (1.00)
CDH1	33 (7%)	474	0.909 (1.00)	0.0619 (1.00)	1 (1.00)	0.84 (1.00)
TP53	184 (36%)	323	0.966 (1.00)	0.186 (1.00)	0.0914 (1.00)	0.47 (1.00)
MAP2K4	20 (4%)	487	0.0878 (1.00)	0.134 (1.00)	1 (1.00)	0.611 (1.00)
PIK3CA	178 (35%)	329	0.455 (1.00)	0.0485 (1.00)	1 (1.00)	1 (1.00)
GATA3	54 (11%)	453	0.812 (1.00)	0.00918 (1.00)	0.493 (1.00)	0.147 (1.00)
RUNX1	18 (4%)	489	0.236 (1.00)	0.481 (1.00)	1 (1.00)	0.422 (1.00)
PTEN	17 (3%)	490	0.896 (1.00)	0.579 (1.00)	1 (1.00)	1 (1.00)
MLL3	36 (7%)	471	0.473 (1.00)	0.0145 (1.00)	1 (1.00)	0.563 (1.00)
TBX3	13 (3%)	494	0.235 (1.00)	0.0275 (1.00)	1 (1.00)	1 (1.00)
PIK3R1	14 (3%)	493	0.213 (1.00)	0.88 (1.00)	1 (1.00)	1 (1.00)
CBFB	8 (2%)	499	0.632 (1.00)	0.367 (1.00)	1 (1.00)	1 (1.00)
AKT1	12 (2%)	495	0.223 (1.00)	0.548 (1.00)	1 (1.00)	0.194 (1.00)
TBL1XR1	8 (2%)	499	0.811 (1.00)	0.0425 (1.00)	1 (1.00)	0.69 (1.00)
CTCF	13 (3%)	494	0.149 (1.00)	0.365 (1.00)	1 (1.00)	1 (1.00)
NCOR1	17 (3%)	490	0.624 (1.00)	0.53 (1.00)	1 (1.00)	0.58 (1.00)
FOXA1	8 (2%)	499	0.0014 (0.29)	0.0133 (1.00)	1 (1.00)	1 (1.00)
AFF2	13 (3%)	494	0.887 (1.00)	0.0186 (1.00)	1 (1.00)	0.357 (1.00)
VASN	6 (1%)	501	0.13 (1.00)	0.858 (1.00)	1 (1.00)	0.668 (1.00)
ZFP36L1	7 (1%)	500	0.951 (1.00)	0.0859 (1.00)	1 (1.00)	0.198 (1.00)
RB1	9 (2%)	498	0.69 (1.00)	0.305 (1.00)	1 (1.00)	0.455 (1.00)
C1ORF65	7 (1%)	500	0.254 (1.00)	0.656 (1.00)	1 (1.00)	0.0935 (1.00)
CDKN1B	5 (1%)	502	0.469 (1.00)	0.982 (1.00)	1 (1.00)	1 (1.00)
GPS2	6 (1%)	501	0.242 (1.00)	0.986 (1.00)	1 (1.00)	0.668 (1.00)
NEK5	8 (2%)	499	0.672 (1.00)	0.106 (1.00)	1 (1.00)	0.456 (1.00)
MYB	8 (2%)	499	0.219 (1.00)	0.255 (1.00)	1 (1.00)	0.224 (1.00)
OR5I1	5 (1%)	502	0.686 (1.00)	0.213 (1.00)	1 (1.00)	0.13 (1.00)
KRAS	4 (1%)	503	0.532 (1.00)	0.209 (1.00)	1 (1.00)	0.579 (1.00)
UBC	7 (1%)	500	0.292 (1.00)	0.548 (1.00)	1 (1.00)	1 (1.00)
DCAF4L2	7 (1%)	500	0.595 (1.00)	0.625 (1.00)	1 (1.00)	1 (1.00)
HLA-DRB1	4 (1%)	503	0.551 (1.00)	0.616 (1.00)	1 (1.00)	0.579 (1.00)
SF3B1	10 (2%)	497	0.386 (1.00)	0.378 (1.00)	1 (1.00)	1 (1.00)
SYCE1L	4 (1%)	503	0.278 (1.00)	0.717 (1.00)	1 (1.00)	1 (1.00)
AVPI1	4 (1%)	503	0.672 (1.00)	0.5 (1.00)	1 (1.00)	1 (1.00)
ATP10B	12 (2%)	495	0.686 (1.00)	0.5 (1.00)	1 (1.00)	1 (1.00)
PIWIL1	8 (2%)	499	0.189 (1.00)	0.718 (1.00)	1 (1.00)	1 (1.00)
CCT6B	6 (1%)	501	0.167 (1.00)	0.565 (1.00)	0.0693 (1.00)	1 (1.00)
PPEF1	7 (1%)	500	0.7 (1.00)	0.533 (1.00)	1 (1.00)	0.68 (1.00)
C12ORF36	4 (1%)	503	0.357 (1.00)	0.157 (1.00)	1 (1.00)	1 (1.00)
SHD	6 (1%)	501	0.334 (1.00)	0.603 (1.00)	1 (1.00)	1 (1.00)
CBLB	9 (2%)	498	0.367 (1.00)	0.556 (1.00)	1 (1.00)	1 (1.00)
ZNF268	4 (1%)	503	0.638 (1.00)	0.726 (1.00)	1 (1.00)	0.303 (1.00)
HIST1H2BC	4 (1%)	503	0.556 (1.00)	0.657 (1.00)	1 (1.00)	1 (1.00)
C8ORF31	4 (1%)	503	0.6 (1.00)	0.803 (1.00)	1 (1.00)	0.303 (1.00)
FAM47C	10 (2%)	497	0.248 (1.00)	0.103 (1.00)	1 (1.00)	0.735 (1.00)
DALRD3	6 (1%)	501	0.613 (1.00)	0.0641 (1.00)	1 (1.00)	0.353 (1.00)
ITPKB	8 (2%)	499	0.381 (1.00)	0.0126 (1.00)	1 (1.00)	0.456 (1.00)
C4ORF40	4 (1%)	503	0.33 (1.00)	0.486 (1.00)	1 (1.00)	0.579 (1.00)
CD5L	5 (1%)	502	0.895 (1.00)	0.846 (1.00)	1 (1.00)	0.33 (1.00)
ATN1	8 (2%)	499	0.235 (1.00)	0.72 (1.00)	1 (1.00)	0.456 (1.00)
KCNT2	9 (2%)	498	0.717 (1.00)	0.251 (1.00)	1 (1.00)	0.455 (1.00)
ATP1A4	9 (2%)	498	0.059 (1.00)	0.482 (1.00)	0.102 (1.00)	0.123 (1.00)

'MAP3K1 MUTATION STATUS' versus 'AGE'

P value = 0.000845 (t-test), Q value = 0.18

Table S1. Gene #6: 'MAP3K1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	507	57.8 (13.1)
MAP3K1 MUTATED	38	64.7 (12.3)
MAP3K1 WILD-TYPE	469	57.2 (13.1)

Figure S1. Get High-res Image Gene #6: 'MAP3K1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Mutation data file = BRCA-TP.mutsig.cluster.txt
Clinical data file = BRCA-TP.clin.merged.picked.txt
Number of patients = 507
Number of significantly mutated genes = 52
Number of selected clinical features = 4
Exclude genes that fewer than K tumors have mutations, K = 3

Survival analysis

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

Student's t-test analysis

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 tumors with and without gene mutations using 't.test' function in R

Fisher's exact test

For binary or multi-class clinical features (nominal or ordinal), two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' 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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[4] 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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