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Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso

BMC BIOINFOrMATICS 2023年第1期24卷 1-20页

作者： Zhu, Wencan Levy-Leduc, Celine Ternes, Nils Univ Paris Saclay AgroParisTech INRAE UMR MIA Paris Saclay F-91120 Palaiseau France Sanofi R&D Biostat & Programming Dept F-91380 Chilly Mazarin France

In clinical trials, identification of prognostic and predictive biomarkers has became essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to identify patients with potential benefit from the treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied. A new method is required to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data where biomarkers are highly correlated. We propose a novel approach called PPLasso, that integrates prognostic and predictive effects into one statistical model. PPLasso also takes into account the correlations between biomarkers that can alter the biomarker selection accuracy. Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the traditional Lasso and other extensions on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic and proteomic data.

关键词： Variable selection Highly correlated predictors Genomic data

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Bayesian dose regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics

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BIOMETrICS 2022年第1期78卷 300-312页

作者： Gerard, Emma Zohar, Sarah Hoai-Thu Thai Lorenzato, Christelle riviere, Marie-Karelle Ursino, Moreno Univ Paris Sorbonne Univ Ctr Rech Cordeliers INSERM F-75006 Paris France Sanoli R&D Oncol Biostat Biostat & Programming Dept Vitry Sur Seine France Sanofi R&D Biostat & Programming Dept Stat Methodol Grp Chilly Mazarin France Sanofi R&D Translat Dis Modeling Digital & Data Sci Chilly Mazarin France Univ Paris AP HP F CRIN Partners Platform Paris France

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose of a drug under a specific schedule. Evaluating drug schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose regimen assessment method (drtox) using pharmacokinetics/pharmacodynamics (PK/Pd) to estimate the maximum tolerated dose regimen (MTd-regimen) at the end of the dose-escalation stage of a trial. We modeled the binary toxicity via a Pd endpoint and estimated the dose regimen toxicity relationship through the integration of a dose regimen Pd model and a Pd toxicity model. For the first model, we considered nonlinear mixed-effects models, and for the second one, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. In an extensive simulation study, the drtox outperformed traditional designs in terms of proportion of correctly selecting the MTd-regimen. Moreover, the inclusion of PK/Pd information helped provide more precise estimates for the entire dose regimen toxicity curve;therefore the drtox may recommend alternative untested regimens for expansion cohorts. The drtox was developed to be applied at the end of the dose-escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/Pd data are collected.

关键词： Bayesian inference dose regimen early phase oncology hierarchical model pharmacokinetics pharmacodynamics toxicity

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Enhancement of Basket Trial designs with Incorporation of a Bayesian Three-Outcome decision-Making Framework

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STATISTICS IN BIOPHArMACEUTICAL rESEArCH 2024年第2期16卷 229-240页

作者： Mi, Gu Yang, Yue Jin, Zhumengmeng Lin, Ji Lorenzato, Christelle Sanofi Biostat & Programming Cambridge MA USA Univ Florida Dept Stat Gainesville FL USA Sanofi R&D Biostat & Programming Vitry Sur Seine France Sanofi Biostat & Programming Cambridge MA 02141 USA

With recent accelerated approvals of histology-agnostic novel agents, conducting basket trials that evaluate an investigational therapy on different histologies is gaining momentum, thanks to the underlying common biological anti-cancer mechanism of action. Statistical models are proposed to boost statistical efficiency by leveraging information across cohorts to harvest the shared response signal. However, limited research exists about establishing a quantitative decision-making framework for basket trials. robustness of a dichotomized "Go/No-Go" decision may be suboptimal when an "inconclusive" decision is more appropriate. Accordingly, the three-outcome decision-making (3OdM) framework with an additional "Consider" zone has gained popularity, and we propose to incorporate 3OdM into basket trials for the benefit of robustness and flexibility, and for formal incorporation of between-cohort shared signal into 3OdM to improve its performance in basket trials. Our simulation study is the first to compare modeling of log odds ratio (on top of benchmark response rates, rrs) with modeling of rrs in most current basket trial designs, considering the potential drastic differences for reference and target rrs across cohorts. We used the exchangeability-nonexchangeability (EXNEX) model to evaluate operating characteristics of the EXNEX + 3OdM framework (with/without an interim analysis), although the proposed enhancement could be readily extended to different basket trial designs.

关键词： Basket trials Bayesian three-outcome decision-making Oncology Proof-of-concept studies

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Variable Selection in High-dimensional Logistic regression Models Using a Whitening Approach

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TrANSACTIONS ON COMPUTATIONAL BIOLOGY ANd BIOINFOrMATICS 2025年第2期22卷 800-807页

作者： Zhu, Wencan Levy-Leduc, Celine Ternes, Nils Univ Paris Saclay UMR MIA Paris AgroParisTech INRAE F-91190 Gif Sur Yvette France Sanofi R&D Dept Biostat & Programming Bridgewater NJ 08807 USA Univ Paris Cite CNRS Lab Probabil Stat Modelisat LPSM F-75006 Paris France Sanofi R&D Dept Biostat & Programming Bridgewater NJ 08807 USA

In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the central problems in biomedical research. However, omics data usually has a limited sample size but high feature dimensions, and it is assumed that only a few features (biomarkers) are active, i.e. informative to discriminate between different categories. Identifying active biomarkers for classification has therefore become fundamental for omics data analysis. Focusing on binary classification, we propose an innovative feature selection method aiming at dealing with the high correlations between the biomarkers. Our method, WLogit, consists in whitening the design matrix to remove the correlations between biomarkers, then using a penalized criterion adapted to the logistic regression model to select features. The results from numerical experiments suggest that WLogit can identify almost all active biomarkers even in the cases where the biomarkers are highly correlated, while the other methods fail, which consequently leads to higher classification accuracy. The performance of WLogit is also evaluated on two publicly available datasets, and the obtained classifier outperformed other methods in terms of prediction accuracy. Our method is implemented in the WLogit r package available from the Comprehensive r Archive Network (CrAN).

关键词： Biomarkers Correlation Feature extraction Biological system modeling Bioinformatics Logistic regression Integrated circuit modeling diseases Computational modeling Computational biology Feature selection highly correlated predictors binary classification regularized approches

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Efficacy and safety of dupilumab with concomitant topical corticosteroids in Japanese pediatric patients with moderate-to-severe atopic dermatitis: A randomized, double-blind, placebo-controlled phase 3 study

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ALLErGOLOGY INTErNATIONAL 2024年第4期73卷 532-542页

作者： Ebisawa, Motohiro Kataoka, Yoko Tanaka, Akio Nagao, Mizuho Laws, Elizabeth Mortensen, Eric Nawata, Hisakatsu Arima, Kazuhiko Watanabe, daisuke Lu, Xin Maloney, Jennifer dubost-Brama, Ariane Bansal, Ashish Yahata, Kenji NHO Sagamihara Natl Hosp Clin Res Ctr Allergy & Rheumatol Sagamihara Japan Osaka Habikino Med Ctr Dept Dermatol Osaka Japan Hiroshima Univ Grad Sch Biomed Sci Dept Dermatol Hiroshima Japan NHO Mie Natl Hosp Allergy Ctr Tsu Japan NHO Mie Natl Hosp Infect Dis Ctr Tsu Japan Sanofi Bridgewater NJ USA Regeneron Pharmaceut Tarrytown NY USA Sanofi KK Immunol Med Tokyo Japan Sanofi KK Biostat & Programming Tokyo Japan Sanofi R&D Chilly Mazarin France Sanofi KK Res & Dev Div Tokyo Japan

Background We investigated the efficacy and safety of dupilumab in Japanese patients aged >= 6 months to = 75% improvement in Eczema Area and Severity Index (EASI) score from baseline (EASI-75) to Week 16. Key seco... 详细信息

Background We investigated the efficacy and safety of dupilumab in Japanese patients aged >= 6 months to <18 years old with moderate-to-severe atopic dermatitis not adequately controlled with existing therapies. Methods In this randomized, double-blind, phase 3 study, patients received dupilumab (n = 30) or placebo (n = 32) with concomitant topical corticosteroids for 16 weeks, then all patients received dupilumab from 16 to 52 weeks. The primary endpoint was the proportion of patients with >= 75% improvement in Eczema Area and Severity Index (EASI) score from baseline (EASI-75) to Week 16. Key secondary endpoints included changes in EASI score, proportion of patients with investigator global assessment (IGA) scores of 0/1, and changes in worst daily itch numerical rating scale (NrS) scores (evaluated in patients aged >= 6 to <12 years [n = 35]). results At Week 16, more patients achieved EASI-75 with dupilumab than placebo (43.3% vs 18.8%;P = 0.0304), and the least squares mean (LSM) difference in percent change in EASI scores at Week 16 of dupilumab vs placebo was -39.4% (P = 0.0003). However, no significant difference in the proportion of patients achieving IGA scores of 0/1 at Week 16 with dupilumab versus placebo were seen (10.0% vs 9.4%;P = 0.8476). The percent change in worst daily itch NrS scores at Week 16 was higher with dupilumab (LSM difference: -33.3%;nominal P = 0.0117). dupilumab was well tolerated;no new safety signals were identified. Conclusions dupilumab showed consistent efficacy and was well tolerated in Japanese patients aged >= 6 months to <18 years with moderate-to-severe atopic dermatitis previously insufficiently controlled with existing therapies.(c) 2024 Japanese Society of Allergology. Published by Elsevier B.V. This is an open access article under the CC BY-NC-Nd license (http://***/licenses/by-nc-nd/4.0/).

关键词： Atopic dermatitis dupilumab Japanese Pediatrics

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A variable selection approach for highly correlated predictors in high-dimensional genomic data

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BIOINFOrMATICS 2021年第16期37卷 2238-2244页

作者： Zhu, Wencan Levy-Leduc, Celine Ternes, Nils Univ Paris Saclay AgroParisTech UMR MIA Paris INRAE F-75005 Paris France Sanofi R&D Biostat & Programming Dept F-91380 Chilly Mazarin France

Motivation: In genomic studies, identifying biomarkers associated with a variable of interest is a major concern in biomedical research. regularized approaches are classically used to perform variable selection in high-dimensional linear models. However, these methods can fail in highly correlated settings. results: We propose a novel variable selection approach called WLasso, taking these correlations into account. It consists in rewriting the initial high-dimensional linear model to remove the correlation between the biomarkers (predictors) and in applying the generalized Lasso criterion. The performance of WLasso is assessed using synthetic data in several scenarios and compared with recent alternative approaches. The results show that when the biomarkers are highly correlated, WLasso outperforms the other approaches in sparse high-dimensional frameworks. The method is also illustrated on publicly available gene expression data in breast cancer.

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Additive efficacy of a bispecific anti-TNF/IL-6 nanobody compound in translational models of rheumatoid arthritis

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SCIENCE TrANSLATIONAL MEdICINE 2023年第681期15卷 eabq4419页

作者： Biesemann, Nadine Margerie, daniel Asbrand, Christian rehberg, Markus Savova, Virginia Agueusop, Inoncent Klemmer, daniel ding-Pfennigdorff, danping Schwahn, Uwe dudek, Markus Heyninck, Karen de Tavernier, Evelyn Cornelis, Sigrid Kohlmann, Markus Nestle, Frank O. Herrmann, Matthias Industriepark Hoechst Sanofi R&D Immunol & Inflammat Therapeut Area Type 1 17 Immunol Cluster D-65926 Frankfurt Germany Industriepark Hoechst Sanofi R&D Digital & Data Sci D-65926 Frankfurt Germany Sanofi R&D Precis Med & Computat Biol 350 Water St Cambridge MA 02141 USA Industriepark Hoechst Sanofi R&D Biostatisticsand Programming D-65926 Frankfurt Germany Industriepark Hoechst Sanofi R&D Biostat & Programming Nonclin Efficacy & Safety D-65926 Frankfurt Germany Industriepark Hoechst Sanofi R&D Translat Med Early Dev Biomarkers & Clin Bioanal D-65926 Frankfurt Germany NANOBODY Res Platform Sanofi R&D Technologiepk 21 B-9052 Zwijnaarde Belgium Industriepark Hoechst Sanofi R&D Early Clin Dev Therapeut Area Immunol & Inflammat D-65926 Frankfurt Germany Sanofi R&D 350 Water St Cambridge MA 02141 USA

rheumatoid arthritis (rA) is one of the most common autoimmune diseases affecting primarily the joints. despite successful therapies including antibodies against tumor necrosis factor (TNF) and interleukin-6 (IL-6) receptor, only 20 to 30% of patients experience remission. We studied whether inhibiting both TNF and IL-6 would result in improved efficacy. Using backtranslation from single-cell rNA sequencing (scrNA-seq) data from individuals with rA, we hypothesized that TNF and IL-6 act synergistically on fibroblast-like synoviocytes (FLS) and T cells. Coculture of FLS from individuals with rA and T cells supported this hypothesis, revealing effects on both disease-driving pathways and biomarkers. Combining anti-TNF and anti-IL-6 antibodies in collagen-induced arthritis (CIA) mouse models resulted in sustained long-term remission, improved histology, and effects on bone remodeling pathways. These promising data initiated the development of an anti-TNF/IL-6 bispecific nanobody compound 1, with similar potencies against TNF and IL-6. We observed additive efficacy of compound 1 in a FLS/T cell coculture affecting arthritis and T helper 17 (TH17) pathways. This nanobody compound transcript signature inversely overlapped with described rA endotypes, indicating a potential efficacy in a broader patient population. In summary, we showed superiority of a bispecific anti-TNF/IL-6 nanobody compound or combination treatment over monospecific treatments in both in vitro and in vivo models. We anticipate improved efficacy in upcoming clinical studies.

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An innovative phase 2 proof-of-concept trial design to evaluate SAr445088, a monoclonal antibody targeting complement C1s in chronic inflammatory demyelinating polyneuropathy

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JOUrNAL OF THE PErIPHErAL NErVOUS SYSTEM 2023年第2期28卷 276-285页

作者： Querol, Luis Lewis, richard A. Hartung, Hans-Peter Van doorn, Pieter A. Wallstroem, Erik Luo, Xiaodong Alonso-Alonso, Miguel Atassi, Nazem Hughes, richard A. C. Hosp Santa Creu & Sant Pau Dept Neurol Neuromuscular Dis Unit Barcelona Spain Ctr Invest Biomed Red Enfermedades Raras CIBERER Madrid Spain Cedars Sinai Med Ctr Los Angeles CA USA Heinrich Heine Univ Fac Med Dept Neurol Dusseldorf Germany Univ Sydney Brain & Mind Ctr Sydney NSW Australia Med Univ Vienna Dept Neurol Vienna Austria Palacky Univ Olomouc Dept Neurol Olomouc Czech Republic Univ Med Ctr Erasmus MC Rotterdam Netherlands Sanofi R&D Neurol Dev Cambridge MA USA Sanofi R&D Biostat & Programming Bridgewater NJ USA UCL UCL Queen Sq Inst Neurol London England

Background and AimsChronic inflammatory demyelinating polyneuropathy (CIdP) is a rare immune-mediated disease of the peripheral nerves, with significant unmet treatment needs. Clinical trials in CIdP are challenging;thus, new trial designs are needed. We present design of an open-label phase 2 study (NCT04658472) evaluating efficacy and safety of SAr445088, a monoclonal antibody targeting complement C1s, in CIdP. MethodsThis phase 2, proof-of-concept, multicenter, open-label trial will evaluate the efficacy, and safety of SAr445088 in 90 patients with CIdP across three groups: (1) currently treated with standard-of-care (SOC) therapies, including immunoglobulin or corticosteroids (SOC-Treated);(2) refractory to SOC (SOC-refractory);and (3) naive to SOC (SOC-Naive). Enrolled participants undergo a 24-week treatment period (part A), followed by an optional treatment extension for up to an additional 52 weeks (part B).In part A, the primary endpoint for the SOC-Treated group is the percentage of participants with a relapse after switching from SOC to SAr445088. The primary endpoint for the SOC-refractory and SOC-Naive groups is the percentage of participants with a response, compared to baseline. Secondary endpoints include safety, tolerability, immunogenicity, and efficacy of SAr445088 during 12-week overlapping period (SOC-Treated). Part B evaluates long-term safety and durability of efficacy. data analysis will be performed using Bayesian statistics (predefined efficacy thresholds) and historical data-based placebo assumptions to support program decision-making. InterpretationThis innovative trial design based on patient groups and Bayesian statistics provides an efficient paradigm to evaluate new treatment candidates across the CIdP spectrum and can help accelerate development of new therapies.

关键词： Bayesian analysis CIdP complement classical pathway complement C1s SAr445088 trial design

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Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens

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STATISTICS IN MEdICINE 2021年第23期40卷 5096-5114页

作者： Gerard, Emma Zohar, Sarah Lorenzato, Christelle Ursino, Moreno riviere, Marie-Karelle Univ Paris Sorbonne Univ Ctr Rech Cordeliers INSERM 15 Rue Ecole Med F-75006 Paris France INRIA HeKA Paris France Sanofi R&D Oncol Biostat Biostat & Programming Dept Vitry Sur Seine France Sanofi R&D Stat Methodol Grp Biostat & Programming Dept Chilly Mazarin France Univ Paris CHU Robert Debre AP HP Unit Clin EpidemiolINSERMU1123Sorbonne Paris C Paris France CIC EC 1426 Paris France

Most phase I trials in oncology aim to find the maximum tolerated dose (MTd) based on the occurrence of dose limiting toxicities (dLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance. Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose-finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of dLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/Pd), whereas the other dLT (dLTo) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of dLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose-allocation stage of the trial, once all data, including PK/Pd measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTd-regimen compared to the recommendation of the dose-allocation method implemented. Our joint approaches can also predict the dLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.

关键词： Bayesian joint modeling bivariate toxicity cumulative probability of toxicity dose regimen early phase oncology pharmacokinetics pharmacodynamics

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Venglustat combined with imiglucerase for neurological disease in adults with Gaucher disease type 3: the LEAP trial

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BrAIN 2023年第2期146卷 461-474页

作者： Schiffmann, raphael Cox, Timothy M. dedieu, Jean-Francois Gaemers, Sebastiaan J. M. Hennermann, Julia B. Ida, Hiroyuki Mengel, Eugen Minini, Pascal Mistry, Pramod Musholt, Petra B. Scott, david Sharma, Jyoti Peterschmitt, M. Judith Baylor Scott & White Res Inst Inst Metab Dis Dallas TX 75246 USA Univ Cambridge Dept Med Cambridge CB2 0QQ England Addenbrookes Hosp Cambridge CB2 0QQ England Sanofi Neurol Dev F-91385 Chilly Mazarin France Sanofi Global Pharmacovigilance 1105 BP Amsterdam Netherlands Univ Med Ctr Mainz Ctr Pediat & Adolescent Med Villa Metabol D-55131 Mainz Germany Jikei Univ Dept Pediat Sch Med Tokyo 1058461 Japan SphinCS Clin Sci LSD D-65239 Hochheim Germany Sanofi Biostat & Programming F-91385 Chilly Mazarin France Yale Sch Med Yale Lysosomal Dis Ctr New Haven CT 06510 USA Yale Sch Med Gaucher Dis Treatment Ctr New Haven CT 06510 USA Sanofi R&D Global Dev D-65926 Frankfurt Germany Clario Med & Sci Affairs Neurosci San Mateo CA 94404 USA Sanofi Pharmacokinet Dynam & Metab Bridgewater NJ 08807 USA Sanofi Neurol Clin Dev R&D Cambridge MA 02141 USA

Gaucher disease type 3 is a chronic neuronopathic disorder with wide-ranging effects, including hepatosplenomegaly, anaemia, thrombocytopenia, skeletal disease and diverse neurological manifestations. Biallelic mutations in GBA1 reduce lysosomal acid beta-glucosidase activity, and its substrates, glucosylceramide and glucosylsphingosine, accumulate. Enzyme replacement therapy and substrate reduction therapy ameliorate systemic features of Gaucher disease, but no therapies are approved for neurological manifestations. Venglustat is an investigational, brain-penetrant, glucosylceramide synthase inhibitor with potential to improve the disease by rebalancing influx of glucosylceramide with impaired lysosomal recycling. The Phase 2, open-label LEAP trial (NCT02843035) evaluated orally administered venglustat 15 mg once-daily in combination with maintenance dose of imiglucerase enzyme replacement therapy during 1 year of treatment in 11 adults with Gaucher disease type 3. Primary endpoints were venglustat safety and tolerability and change in concentration of glucosylceramide and glucosylsphingosine in CSF from baseline to Weeks 26 and 52. Secondary endpoints included change in plasma concentrations of glucosylceramide and glucosylsphingosine, venglustat pharmacokinetics in plasma and CSF, neurologic function, infiltrative lung disease and systemic disease parameters. Exploratory endpoints included changes in brain volume assessed with volumetric MrI using tensor-based morphometry, and resting functional MrI analysis of regional brain activity and connectivity between resting state networks. Mean (Sd) plasma venglustat AUC(0-24) on day 1 was 851 (282) ng.h/ml;C-max of 58.1 (26.4) ng/ml was achieved at a median t(max) 2.00 h. After oncedaily venglustat, plasma concentrations (4 h post-dose) were higher compared with day 1, indicating similar to 2-fold accumulation. One participant (Patient 9) had low-to-undetectable venglustat exposure at Weeks 26 and 52. Based on mean pla

关键词： Gaucher disease type 3 venglustat substrate reduction therapy glycosphingolipids CNS

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