Biology subjects

Boulesteix, A.-L.

Publications and source records attributed to Boulesteix, A.-L..

4 recordsLinked to original sources

bioRxiv2026

Multi-Lab Testing of Early Preclinical Discoveries Identifies Promising Treatments

A fundamental challenge in drug development is the frequent failure of early laboratory research to translate into clinical benefit. One promising solution is to confirm findings from exploratory single-laboratory studies across multiple laboratories before clinical testing. We investigated this approach following the conduct of preclinical multi-laboratory studies across different fields of medicine. For this, we evaluated effect sizes, experimental rigor, and a set of criteria to identify determinants of confirmation success. When tested under increased rigor, only a fraction of multi-laboratory studies confirmed the initial results. The underlying effect size reduction was associated with outcome-relevant experimental differences between exploratory and confirmatory stages. In summary, multi-laboratory studies proved highly informative and served as an effective filter for promising treatments.

scientific communication and education↗

bioRxiv2026

Multicenter preclinical validation of next-generation CAR T cells: a strategy for harmonization, reproducibility, and its feasibility in clinical translation

PurposeClinical translation of CAR T cell therapies has accelerated, yet preclinical evidence still often originates from single-center studies lacking sufficient robustness. Preclinical confirmatory multicenter studies have been proposed to improve the translational success, but their feasibility in cellular therapies remains unexplored. MethodsWe performed a confirmatory multicenter study validating C-C-motive-receptor-8 (CCR8) overexpression in CAR T cells--a strategy previously shown to enhance solid tumor infiltration. In vitro experiments covering activation, cytotoxicity, and migration using three CAR constructs were conducted across two centers with harmonized materials, preregistered protocols, randomization, and blinding. ResultsThe data from the two centers confirmed key findings of the exploratory study: CCR8 overexpression in anti-EpCAM and anti-mesothelin CAR T cells leads to enhanced selective migration towards a CCL1-gradient, while not compromising antigen-specific T cell activatory capacity and cytotoxicity in vitro. The study furthermore broadened the applicability of CCR8 overexpression to anti-CEA CAR T cells. ConclusionsThis first-of-its-kind preclinical confirmatory CAR T study demonstrates the feasibility of a multicenter confirmation in cellular therapy, with technical and logistical challenges resolved through transparent communication between all parties involved. Both exploratory and confirmatory studies aim to downselect CAR candidates with the highest clinical success potential, as they compete for limited resources in preclinical research. It is therefore mandatory to clarify the extent of replications required to validate the experimental methodology and identify CAR candidates with most likelihood of success. TRANSLATIONAL RELEVANCEPreclinical evidence for novel CAR T cell therapeutic strategies relies mostly on exploratory single-center studies lacking robustness, with recent findings substantiating their limited predictive value for cellular therapies tested outside hematology. Here, the function of CCR8-armored CARs in vitro was confirmed in a preclinical confirmatory multicenter study, demonstrating the feasibility of such studies in adding value to the transition of preclinical concepts to clinical development. Our first-of-its-kind study may contribute to define new routes for preclinical testing and further raises the general question of what level of preclinical evidence is reasonably achievable in an academic context. It indicates the need for strong collaborative efforts to realize dedicated preclinical infrastructure for clinical translation of reprogrammed immune cellular therapeutics.

synthetic biology↗

bioRxiv2022

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes three unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, and differential microbial network analysis. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the "best" method combination to the validation dataset. The results are then compared between discovery and validation data. In all three research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.

bioinformatics↗

bioRxiv2020

NetCoMi: Network Construction and Comparison for Microbiome Data in R

Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization, and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analyzing, and comparing microbial association networks from high-throughput sequencing data. Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analyzing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa, or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMis wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from childrens rooms between samples from two study centers (Ulm and Munich). AvailabilityA script with R code used for producing the examples shown in this manuscript are provided as Supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi.

bioinformatics↗