Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model

Abstract

Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.

Keywords

Compound covariate copula dependent censoring risk prediction semi-competing risk surrogate endpoint

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References

Graf

Schmoor

Sauerbrei

et al.

Assessment and comparison of prognostic classification schemes for survival data. Stat Med 1999; 18: 2529–2545.

Gerds

Schumacher

. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical J 2006; 48: 1029–1040.

Cox

. Regression models and life-tables (with discussion). J R Stat Soc Ser B 1972; 34: 187–220.

Jenssen

Kuo

Stokke

et al.

Association between gene expressions in breast cancer and patient survival. Hum Genet 2002; 111: 411–420.

Sabatier

Finetti

Adelaide

et al.

Down-regulation of ECRG4, a candidate tumor suppressor gene, in human breast cancer. PLoS One 2011; 6: e27656–e27656.

Lossos

Czerwinski

Alizadeh

et al.

Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. N Engl J Med 2004; 350: 1828–1837.

Alizadeh

Gentles

Alencar

et al.

Prediction of survival in diffuse large B-cell lymphoma based on the expression of 2 genes reflecting tumor and microenvironment. Blood 2011; 118: 1350–1358.

Beer

Kardia

SLR

Huang

et al.

Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 2002; 8: 816–824.

Chen

et al.

A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med 2007; 356: 11–20.

10.

Shedden

Taylor

JMG

Enkemann

et al.

Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 2008; 14: 822–827.

11.

Popple

Durrant

Spendlove

et al.

The chemokine, CXCL12, is an independent predictor of poor survival in ovarian cancer. Br J Canc 2012; 106: 1306–1313.

12.

Yoshihara

Tsunoda

Shigemizu

et al.

High-risk ovarian cancer based on 126-gene expression signature is uniquely characterized by downregulation of antigen presentation pathway. Clin Canc Res 2012; 18: 1374–1385.

13.

Waldron

Haibe-Kains

Culhane

et al.

Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. J Natl Canc Inst 2014; 106: dju049–dju049.

14.

Michiels

Koscielny

Hill

. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 2005; 365: 488–492.

15.

Matsui

. Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays. BMC Bioinformatics 2006; 7: 156–156.

16.

Schumacher

Binder

Gerds

. Assessment of survival prediction models based on microarray data. Bioinformatics 2007; 23: 1768–1774.

17.

Bøvelstad

Nygård

Storvold

et al.

Predicting survival from microarray data – a comparative study. Bioinformatics 2007; 23: 2080–2087.

18.

Binder

Schumacher

. Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics 2008; 9: 14–14.

19.

Bøvelstad

Nygård

Borgan

. Survival prediction from clinico-genomic models – a comparative study. BMC Bioinformatics 2009; 10: 1–1.

20.

Simon

Subramanian

et al.

Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief Bioinformatics 2011; 12: 203–214.

21.

Goeman

Meijer

Chaturvedi

. R penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model. CRAN 2016. version 0.9-47.

22.

Van Houwelingen

. Dynamic prediction by landmarking in event history analysis. Scand J Stat 2007; 34: 70–85.

23.

Van Houwelingen

Putter

. Dynamic prediction in clinical survival analysis, Boca Raton: CRC Press, 2011.

24.

Mauguen

Rachet

Mathoulin-Pélissier

et al.

Dynamic prediction of risk of death using history of cancer recurrences in joint frailty models. Stat Med 2013; 32: 5366–5380.

25.

Mauguen

Rachet

Mathoulin-Pélissier

et al.

Validation of death prediction after breast cancer relapses using joint models. BMC Med Res Methodol 2015; 15: 27–27.

26.

Rizopoulos

. Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics 2011; 67: 819–829.

27.

Taylor

Park

Ankerst

et al.

Real‐time individual predictions of prostate cancer recurrence using joint models. Biometrics 2013; 69: 206–213.

28.

Sène

Taylor

Dignam

et al.

Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: development and validation. Stat Methods in Med Res 2016; 25: 2972–2991.

29.

Proust-Lima

Séne

Taylor

et al.

Joint latent class models for longitudinal and time-to-event data: a review. Stat Methods Med Res 2014; 23: 74–90.

30.

Rondeau

Mauguen

Laurent

et al.

Dynamic prediction models for clustered and interval-censored outcomes: investigating the intra-couple correlation in the risk of dementia. Stat Methods Med Res 2015; 26: 2168–2183.

31.

Król

Ferrer

Pignon

et al.

Joint model for left-censored longitudinal data, recurrent events and terminal event: predictive abilities of tumor burden for cancer evolution with application to the FFCD 2000-05 trial. Biometrics. 2016. DOI: 10.1111/biom.12490.

32.

Emura

Nakatochi

Murotani

et al.

A joint frailty-copula model between tumour progression and death for meta-analysis. Stat Methods Med Res 2015; 26: 2649–2666.

33.

Ganzfried

Riester

Haibe-Kains

et al.

Curated ovarian data: clinically annotated data for the ovarian cancer transcriptome. Database 2013. Article ID bat013. DOI:10.1093/database/bat013.

34.

Rondeau

Pignon

Michiels

. A joint model for dependence between clustered times to tumour progression and deaths: a meta-analysis of chemotherapy in head and neck cancer. Stat Methods Med Res 2015; 24: 711–729.

35.

Haneuse

Lee

. Semi-competing risks data analysis, accounting for death as a competing risk when the outcome of interest is nonterminal. Circ Cardiovasc Qual Outcomes 2016; 9: 322–331.

36.

Joly

Commenges

Letenneur

. A penalized likelihood approach for arbitrary censored and truncated data: application to age-specific incidence of dementia. Biometrics 1998; 54: 185–194.

37.

Emura

Chen

. Gene selection for survival data under dependent censoring, a copula-based approach. Stat Methods Med Res 2016; 25: 2840–2857.

38.

Nelsen

. An introduction to copulas, 2nd ed. New York: Springer, 2006.

39.

Tibshirani

. The lasso method for variable selection in the Cox model. Stat Med 1997; 16: 385–395.

40.

Van Houwelingen

Bruinsma

Hart

et al.

Cross-validated Cox regression on microarray gene expression data. Stat Med 2006; 25: 3201–3216.

41.

Tukey

. Tightening the clinical trial. Contr Clin Trials 1993; 14: 266–285.

42.

Emura

Chen

. Survival prediction based on compound covariate under Cox proportional hazard models. PLoS One 2012; 7: e47627–e47627.

43.

Radmacher

Mcshane

Simon

. A paradigm for class prediction using gene expression profiles. J Comput Biol 2002; 9: 505–511.

44.

Matsui

Simon

et al.

Developing and validating continuous genomic signatures in randomized clinical trials for predictive medicine. Clin Canc Res 2012; 18: 6065–6073.

45.

Simon

. Design and analysis of DNA microarray investigations, New York: Springer Science & Business Media, 2003.

46.

Emura

. R joint.Cox: penalized likelihood estimation and dynamic prediction under the joint frailty-copula models between tumour progression and death for meta-analysis. CRAN. version 2.10 2016-10-30.

47.

Rondeau

Gonzalez

Mazroui

et al.

R frailtypack: general frailty models: shared, joint and nested frailty models with prediction. CRAN. version 2.8.3 2016-01-13.

48.

Hastie

Tibshirani

Friedman

. The elements of statistical learning, New York: Springer, 2009.

49.

Schleidgen

Klingler

Bertram

et al.

What is personalized medicine: sharpening a vague term based on a systematic literature review. BMC Med Ethic 2013; 14: 55–55.

50.

Commenges

Proust-Lima

Samieri

et al.

A universal approximate cross-validation criterion for regular risk functions. Int J Biostat 2015; 11: 51–67.

51.

Burzykowski

Molenberghs

Buyse

et al.

Validation of surrogate end points in multiple randomized clinical trials with failure time end points. Appl Stat 2001; 50: 405–422.

52.

Burzykowski

Molenberghs

Buyse

. The evaluation of surrogate endpoints, New York: Springer, 2005.

53.

Michiels

Le Maître

Buyse

et al.

Surrogate endpoints for overall survival in locally advanced head and neck cancer: meta-analyses of individual patient data. Lancet Oncol 2009; 10: 341–350.

54.

Buyse

Sargent

Saad

. Survival is not a good outcome for randomized trials with effective subsequent therapies. J Clin Oncol 2011; 29: 4719–4720.

55.

Oba

Paoletti

Alberts

et al.

Disease-free survival as a surrogate for overall survival in adjuvant trials of gastric cancer: a meta-analysis. J Natl Canc Inst 2013; 105: 1600–1607.

56.

Bellera

MacGrogan

Debled

et al.

Variables with time-varying effects and the Cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer. BMC Med Res Methodol 2010; 10: 1–1.

57.

Mazroui

Mauguen

Mathoulin-Pélissier

et al.

Time-varying coefficients in a multivariate frailty model: application to breast cancer recurrences of several types and death. Lifetime Data Anal 2016; 22: 191–215.

58.

Baulies

Belin

Mallon

et al.

Time-varying effect and long-term survival analysis in breast cancer patients treated with neoadjuvant chemotherapy. Br J Canc 2015; 113: 30–36.

59.

Putter

Sasako

Hartgrink

et al.

Long-term survival with non-proportional hazards: results from the Dutch Gastric Cancer Trial. Stat Med 2005; 24: 2807–2821.

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