Correlation between gene mutation status and selected clinical features

Overview

Introduction

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

Summary

Testing the association between mutation status of 15 genes and 10 clinical features across 155 patients, 6 significant findings detected with Q value < 0.25.

PIK3CA mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
BRAF mutation correlated to 'HISTOLOGICAL.TYPE'.
ACVR2A mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
SMAD2 mutation correlated to 'NEOPLASM.DISEASESTAGE'.
PCBP1 mutation correlated to 'LYMPH.NODE.METASTASIS' and 'NEOPLASM.DISEASESTAGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 15 genes and 10 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 6 significant findings detected.


		Clinical Features	Time to Death	AGE	GENDER	HISTOLOGICAL TYPE	DISTANT METASTASIS	LYMPH NODE METASTASIS	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES	TUMOR STAGECODE	NEOPLASM DISEASESTAGE
	nMutated (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Chi-square test	Fisher's exact test	t-test	t-test	Chi-square test
PCBP1	4 (3%)	151		0.00453 (0.557)	1 (1.00)	1 (1.00)	0.0344 (1.00)	5.23e-08 (6.85e-06)	1 (1.00)	0.135 (1.00)		5.56e-06 (0.000717)
PIK3CA	26 (17%)	129	0.646 (1.00)	0.198 (1.00)	0.83 (1.00)	0.134 (1.00)	0.332 (1.00)	0.0597 (1.00)	0.445 (1.00)	0.000236 (0.03)		0.028 (1.00)
BRAF	20 (13%)	135	0.785 (1.00)	0.0257 (1.00)	0.0586 (1.00)	1e-05 (0.00128)	0.401 (1.00)	0.931 (1.00)	0.59 (1.00)	0.822 (1.00)		0.123 (1.00)
ACVR2A	8 (5%)	147	0.237 (1.00)	0.703 (1.00)	0.0337 (1.00)	1 (1.00)	1 (1.00)	0.651 (1.00)	1 (1.00)	1.69e-06 (0.00022)		0.506 (1.00)
SMAD2	10 (6%)	145	0.58 (1.00)	0.805 (1.00)	0.327 (1.00)	0.192 (1.00)	0.398 (1.00)	0.907 (1.00)	0.404 (1.00)	0.281 (1.00)		0.000677 (0.0853)
APC	103 (66%)	52	0.28 (1.00)	0.627 (1.00)	0.042 (1.00)	0.815 (1.00)	0.165 (1.00)	0.574 (1.00)	0.0378 (1.00)	0.561 (1.00)		0.82 (1.00)
KRAS	58 (37%)	97	0.241 (1.00)	0.00304 (0.38)	0.246 (1.00)	0.651 (1.00)	0.317 (1.00)	0.35 (1.00)	0.315 (1.00)	0.4 (1.00)		0.694 (1.00)
TP53	75 (48%)	80	0.688 (1.00)	0.0728 (1.00)	0.336 (1.00)	0.00663 (0.809)	0.56 (1.00)	0.343 (1.00)	0.803 (1.00)	0.122 (1.00)		0.171 (1.00)
FBXW7	29 (19%)	126	0.869 (1.00)	0.0362 (1.00)	0.154 (1.00)	0.00423 (0.524)	0.0168 (1.00)	0.827 (1.00)	0.0795 (1.00)	0.774 (1.00)		0.0445 (1.00)
NRAS	15 (10%)	140	0.309 (1.00)	0.168 (1.00)	0.0269 (1.00)	1 (1.00)	0.493 (1.00)	0.2 (1.00)	0.276 (1.00)	0.134 (1.00)		0.468 (1.00)
SMAD4	18 (12%)	137	0.635 (1.00)	0.833 (1.00)	0.625 (1.00)	0.164 (1.00)	1 (1.00)	0.947 (1.00)	0.772 (1.00)	0.414 (1.00)		0.95 (1.00)
FAM123B	19 (12%)	136	0.725 (1.00)	0.527 (1.00)	0.811 (1.00)	0.503 (1.00)	1 (1.00)	0.562 (1.00)	1 (1.00)	0.655 (1.00)		0.811 (1.00)
SOX9	9 (6%)	146		0.28 (1.00)	0.746 (1.00)	1 (1.00)	1 (1.00)	0.971 (1.00)	1 (1.00)	0.334 (1.00)		0.151 (1.00)
TNFRSF10C	6 (4%)	149		0.978 (1.00)	0.681 (1.00)	0.591 (1.00)	0.0727 (1.00)	0.526 (1.00)	0.105 (1.00)	0.178 (1.00)		0.325 (1.00)
ACOT4	3 (2%)	152		0.0294 (1.00)	1 (1.00)	0.403 (1.00)	0.374 (1.00)	0.931 (1.00)	0.366 (1.00)	0.647 (1.00)		0.982 (1.00)

'PIK3CA MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000236 (t-test), Q value = 0.03

Table S1. Gene #2: 'PIK3CA MUTATION STATUS' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	154	2.2 (4.5)
PIK3CA MUTATED	26	0.6 (1.6)
PIK3CA WILD-TYPE	128	2.6 (4.8)

Figure S1. Get High-res Image Gene #2: 'PIK3CA MUTATION STATUS' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'BRAF MUTATION STATUS' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0013

Table S2. Gene #3: 'BRAF MUTATION STATUS' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	129	24
BRAF MUTATED	9	11
BRAF WILD-TYPE	120	13

Figure S2. Get High-res Image Gene #3: 'BRAF MUTATION STATUS' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'ACVR2A MUTATION STATUS' versus 'NUMBER.OF.LYMPH.NODES'

P value = 1.69e-06 (t-test), Q value = 0.00022

Table S3. Gene #11: 'ACVR2A MUTATION STATUS' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	154	2.2 (4.5)
ACVR2A MUTATED	8	0.2 (0.5)
ACVR2A WILD-TYPE	146	2.3 (4.6)

Figure S3. Get High-res Image Gene #11: 'ACVR2A MUTATION STATUS' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'SMAD2 MUTATION STATUS' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.000677 (Chi-square test), Q value = 0.085

Table S4. Gene #13: 'SMAD2 MUTATION STATUS' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	31	14	39	4	14	2	12	16	21	1
SMAD2 MUTATED	1	4	0	1	0	1	0	0	2	0
SMAD2 WILD-TYPE	30	10	39	3	14	1	12	16	19	1

Figure S4. Get High-res Image Gene #13: 'SMAD2 MUTATION STATUS' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

'PCBP1 MUTATION STATUS' versus 'LYMPH.NODE.METASTASIS'

P value = 5.23e-08 (Chi-square test), Q value = 6.8e-06

Table S5. Gene #15: 'PCBP1 MUTATION STATUS' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1B	N2	N2A
ALL	91	29	1	33	1
PCBP1 MUTATED	3	0	1	0	0
PCBP1 WILD-TYPE	88	29	0	33	1

Figure S5. Get High-res Image Gene #15: 'PCBP1 MUTATION STATUS' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'PCBP1 MUTATION STATUS' versus 'NEOPLASM.DISEASESTAGE'

P value = 5.56e-06 (Chi-square test), Q value = 0.00072

Table S6. Gene #15: 'PCBP1 MUTATION STATUS' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	31	14	39	4	14	2	12	16	21	1
PCBP1 MUTATED	1	0	2	0	0	0	0	0	0	1
PCBP1 WILD-TYPE	30	14	37	4	14	2	12	16	21	0

Figure S6. Get High-res Image Gene #15: 'PCBP1 MUTATION STATUS' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Mutation data file = COAD-TP.mutsig.cluster.txt
Clinical data file = COAD-TP.clin.merged.picked.txt
Number of patients = 155
Number of significantly mutated genes = 15
Number of selected clinical features = 10
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

Chi-square test

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

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.

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[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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