Bauters, Christophe

Heyse, Wilfried

Marot, Guillemette

Pinet, Florence

Vandewalle, Vincent

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HAL Id: hal-03942723https://hal.inria.fr/hal-03942723Submitted on 17 Jan 2023HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Clinical score building from high-throughput proteomicdataGuillemette Marot, Wilfried Heyse, Vincent Vandewalle, Christophe Bauters,Florence PinetTo cite this version:Guillemette Marot, Wilfried Heyse, Vincent Vandewalle, Christophe Bauters, Florence Pinet. Clinicalscore building from high-throughput proteomic data. ICM/Inria/SCAI workshop “Computational andmathematical approaches for neuroscience”, Jun 2022, Paris, France. ￿hal-03942723￿Clinical score buildingfrom high-throughput proteomic dataGuillemette MarotUniv. Lille, CHU Lille METRICS & Inria MODAL8 June, 2022Acknowledgements : W. Heyse, V. Vandewalle, F. Pinet, C. Bauters1 / 19Precision medicine - score buildingOverview1 Precision medicine - score building2 Motivating example3 Prediction using only the baseline timepoint4 Longitudinal data analysis5 Conclusions - Perspectives2 / 19Precision medicine - score buildingPrecision medicine - Score buildingPrecision medicine :Identifying which treatments will be effective for which patients based ongenetic, environmental, and lifestyle factors.Related questions :clustering in order to separate patients into different groupsselection of clinical variables or biomarkers to predict the groupLarge datasets are necessary to deal with the complexities of diseases.Talk example : ∼ 5200 proteins (variables) measured simultaneously bySomascan technology, 2 cohorts with ∼ 240 patients3 / 19Precision medicine - score buildingScore buildingScore : tool for supporting decision-making for patient stratification(severity score, risk score), for example :linear combination from a discriminant analysis or a generalized linearmodelprobability to belong to a classAlso called prediction clinical ruleThe score, after choice of a threshold, will enable to assign a patient to agroup in order to adapt his treatment.Long-standing tradition in clinics using clinical variables to help non expertpeople and personalize treatments.Many scores are unused (too many alerts for clinicians, unrealistichypotheses, not understandable, too expensive, . . .)4 / 19Precision medicine - score buildingScore buildingThe added value of omic data (here proteomic) must be clearly highlightedto convince clinicians to use a score mixing clinical and omic data.Omic data : many variables in exploratory phasis⇒ necessity to select some variables (”markers”) for a use in clinics.Selected variables must be biologically interpretable, ideally veryfew and easy to measure.Variable selection is as important as prediction ⇒ eliminates manymachine learning techniques, favouring the ones assuming sparsity.Example : Penalized regressions framework, e.g. Lasso (Tibshirani, 1996)5 / 19Precision medicine - score buildingScore buildingLasso example in the high dimension context :β̂(λ) = arg minβ∈Rp{C (β) + λpen(β)}with pen(β) = ‖β‖1 =∑pj=1 |βj |6 / 19Motivating exampleOverview1 Precision medicine - score building2 Motivating example3 Prediction using only the baseline timepoint4 Longitudinal data analysis5 Conclusions - Perspectives7 / 19Motivating exampleExample data : REVE-1 and REVE-2Two cohorts designed by Pr Bauters (F Pinet’s team, RID-AGE U1167) :Patients included after a 1st myocardial infarctionREVE-1 : (2002-2004)255 patients1 plasma sampleREVE-2 : (2006-2008)238 patients4 plasma samplesLongterm follow-up for heart failure and death for cardiovascularreasonsResearch of proteins of interest among 5284 proteins to predict :LVR (left ventricular remodeling)heart failure8 / 19Prediction using only the baseline timepointOverview1 Precision medicine - score building2 Motivating example3 Prediction using only the baseline timepoint4 Longitudinal data analysis5 Conclusions - Perspectives9 / 19Prediction using only the baseline timepointPenalized regressionsy ∈ Rn : response variable (e.g. LVR, time before heart failure)X ∈Mn,p(R) : measures of p proteins on n patientsHigh dimension context :β̂(λ) = arg minβ∈Rp{C (β) + λpen(β)}with for exampleC (β) =‖y − Xβ‖22 =∑ni=1(yi −∑pj=1 xijβj)2 (linear reg.)−∑ni=1(yi ln pi + (1− yi ) ln(1− pi )) with pi =exTi β1+exTiβ(logistic reg.)∑ni=1 δi(ln(∑r∈R e∑j xrjβj)−∑j xijβj)with δi indicative of eventand R indices of individuals at risk (Cox model)10 / 19Prediction using only the baseline timepointPenalized regressionsProblems when using Lasso-based regressions :Difficult choice of λ :cross-validation led us to over-fittingby selecting too many variables.Highly correlated data : correlationstructures due to high dimensionand to biological relations.Solutions tested (PhD W. Heyse) :Stability selection (Meinshausen and Bühlmann, 2010)Use of variations of lasso : group-Lasso (Yuan et Lin, 2006), adaptiveLasso (Zou, 2006), multi-layer group-Lasso (Grimonprez et al., 2022).11 / 19Prediction using only the baseline timepointPenalized regressions vs univariate competing risks analysesNegative results when studying LVR : no validation on REVE-2, difficultiesto interpret selected variables.Possible explanations :LVR is a too complex mechanism to be predicted only with circulatingbiomarkersNot enough patients to build a model with enough proteins⇒ Change of endpoint (study of heart failure or death) and change ofstatistical strategy (use of univariate analyses and correction for multipletesting)The use of Fine-Gray competing risks model led to the selection of 50proteins significantly associated to the occurence of heart failure onREVE-1.12 / 19Prediction using only the baseline timepointClustering and occurence of heart failureBased on these 50 proteins, k-means clustering of patients was performedin order to identify groups of patients whose centers can be used as aprognosis tool.2 clusters of patients were identifiedon REVE-1 and applied to REVE-2showing opposite proteomic expressionprofiles and distinct clinical profiles (age,diabetes, hypertension).Clusters were used as a new variable topredict occurence of heart failure.Cluster effect was significant on both co-horts even when adjusted on clinical va-riables.REVE-1REVE-213 / 19Longitudinal data analysisOverview1 Precision medicine - score building2 Motivating example3 Prediction using only the baseline timepoint4 Longitudinal data analysis5 Conclusions - Perspectives14 / 19Longitudinal data analysisLongitudinal data analysisObjective :Select proteins whose evolution is linked to the occurence of the outcome”heart failure or cardiovascular death”Statistical framework : construction of a predictive model for a variable Ywith a set of variables X1, . . . ,Xp measured over n individuals at t times.Notations :Y ∈ Rn : time before the occurrence of a heart failure orcardiovascular death (survival analysis)X (t) ∈Mn,p(R) : Proteomic variables (p ' 5000, n ' 240, t = 4)Need to jointly model temporal evolution and survival⇒ Use of joint models (shared random effects models) (Wulfsohn andTsiatis, 1997)15 / 19Longitudinal data analysisShared random effects models (SREM)xij(tl) = x∗ij (tl) + εij(tl)x∗ij (tl) = βjl + bijlλij(tl |bij) = λ0(tl)ex∗ij (tl )γjwith :• xij(tl) measurement for patient i ofprotein j at time tl (l ∈ {1, 2, 3, 4})• εij(tl) ∼ N (0, σij)• βjl fixed effect• bjl random effect• λ0(t) baseline risk• γj effect of protein j(survival)9111315A B C DtimeABCDP16860 (N−terminal pro−BNP)Estimation within the maximum likelihood framework by considering thejoint likelihood from the two submodels.16 / 19Longitudinal data analysisRegularized latent class model (rJLCM)Around 480 proteins found to be of interest following univariate analyses.Need to be more selective.⇒ Use of regularized latent class model for joint analysis ofhigh-dimensional longitudinal biomarkers and a time to-event outcome(Sun et al., Biometrics 2019)Work in progress :analysis of selected proteins when obtaining 2 clusters of patients forwhich survival and longitudinal evolution of proteins are different.Extend the model to incorporate biological information such as GOcategories.17 / 19Conclusions - PerspectivesOverview1 Precision medicine - score building2 Motivating example3 Prediction using only the baseline timepoint4 Longitudinal data analysis5 Conclusions - Perspectives18 / 19Conclusions - PerspectivesConclusion - perspectivesHigh dimension problem (n p) of omic data offers challenges forstatisticiansUnivariate analyses followed by multiple testing enable to modelcompeting risks : better solution than penalized regressions whenusing the baseline timepoint.Aggregation of proteins through clustering helps summarize the dataand avoid over-fitting.Joint models are appropriate to jointly analyse the repeated measuresand the survival. Statistical reasearch is needed to improve thescalability to a high number of biomarkers.19 / 19

Clinical score building from high-throughput proteomic data

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Clinical score building from high-throughput proteomic data

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