# Predicting Non-Response to Cystic Fibrosis Modulators from Microbiota Using Standard and Custom Machine Learning

Diego Kauer, Céline Hosteins, Marta Avalos, Laurence Delhaes, Raphaël Enaud

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## To cite this version:

Diego Kauer, Céline Hosteins, Marta Avalos, Laurence Delhaes, Raphaël Enaud. Predicting Non-Response to Cystic Fibrosis Modulators from Microbiota Using Standard and Custom Machine Learning. JOBIM 2025 - Journées Ouvertes Biologie Informatique Mathématiques, Jul 2025, Bordeaux, France. hal-05337115

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Submitted on 29 Oct 2025

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# Predicting Non-Response to Cystic Fibrosis Modulators from Microbiota Using Standard and Custom Machine Learning

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« Data Transformations in Machine Learning Approaches for Studying Microbiota as a Biomarker of Non-Response Risk to CFTR Modulator »

Diego KAUER¹,²*; Céline HOSTEINS¹*; Chloé RENAULT¹

Marta AVALOS¹; Laurence DELHAES³; Raphaël ENAUD³

¹Univ. Bordeaux, INSERM, INRIA, BPH, U1219, Bordeaux, FR; ²Univ Chile, Santiago, CL; ³CHU de Bordeaux, Univ. Bordeaux, INSERM, U1045, Bordeaux, FR

*Contributed equally

## Introduction

Cystic fibrosis (CF) modulators, such as Lumacaftor/Ivacaftor (LUM-IVA), have significantly improved patient health in recent years. However, number of individuals show non-response, with little to no clinical benefit. Anticipating non-response in early treatment could be essential for adapting care strategies.

The lung microbiota, characterized by 16S rRNA sequencing, is increasingly recognized as a valuable biomarker candidate [4] but its analysis remains challenging due to high dimensionality, sparsity, and compositional data structure.

In this study, we evaluated whether the baseline lung microbiota (M0) could predict non-response to LUM-IVA after one year (M12) in a pediatric CF cohort. We assessed the predictive performance of several supervised learning models. This included some state-of-the-art methods like Lasso and Random Forest - combined with microbiome data transformations to address compositionality -, as well as methods specifically designed for compositional data, such as FLORAL and Graph Neural Networks.

## Methods and Materials

We analyzed data from 73 children with CF (homozygous for F508del) treated with Lumacaftor/Ivacaftor and followed for 12 months. Lung bacterial samples collected at M0 via 16S rRNA gene sequencing were used to predict nutritional status at M12, defined as a binary outcome distinguishing « Thin » (BMI z-score below threshold) from « Normal » patients.

After taxonomic aggregation at the genus level and filtering (prevalence ≥4 samples, dominance ≥1%), the absolute abundances (« None » transformation) of 62 taxa were transformed using six strategies [2]:

* Relative abundance

* Presence-absence

* arcSine

* ALR

* CLR

* rCLR

* ILR

Some α-diversity indexes such as Chao1, Simpson, Shannon, Pielou were also considered as predictors.

We tested several supervised learning models:

* Logistic Lasso for interpretable feature selection,

* Random Forests (RF),

* FLORAL [1], designed for log-ratio compositional data,

* a Graph Neural Network (GNN) [3] incorporating phylogenetic structure as input

All models (except the GNN, which requires more complex user manipulation) used the same outer loop (for performance metric calculation) and inner loop (for tuning) of stratified 20-fold cross-validation. Demographic and clinical covariates at M0 were forced into the models to control for confounding effects and to ensure that microbial predictors are evaluated on top of these key clinical features. Indeed, the objective is to complement clinical information with microbiome data, rather than replace it with measures that remain more invasive and costly. Class weights accounted for outcome class imbalance. Predictive performance was assessed using ROC-AUC and PR-AUC metrics.

<figure><img src="image_5.png" /><figcaption>**Figure 1.** Scatter plot showing the performance of each model (ROC-AUC vs PR-AUC)</figcaption></figure>

## Results

As shown in Figure 1, the GNN achieved the highest overall performance, while standard logistic regression - trained without microbiome predictors - reached the lowest, establishing the upper and lower bounds of model performance. Lasso and RF performed best among interpretable models, with similar ROC-AUC and PR-AUC scores, suggesting limited impact of nonlinearity between transformed abundances and the logit.

FLORAL, although designed for high-dimensional compositional data, showed limited improvement over logistic regression, suggesting it may offer no clear advantage in this context or may be sensitive to preprocessing choices, such as handling zeros.

As illustrated in Figures 2 and 3, transformations had limited impact. Lasso remained robust across all, while RF performed best with presence/absence, ILR, and ALR, and slightly worse with others (CLR, relative, rCLR, arcSine).

<table><caption>**Table 1.** Best model by method</caption><thead><tr><td>Method</td><td>ROC-AUC</td><td>PR-AUC</td></tr></thead><tbody><tr><td>GNN</td><td>0.971 ± 0.001</td><td>0.938 ± 0.001</td></tr><tr><td>Lasso</td><td>0.940 ± 0.155</td><td>0.905 ± 0.241</td></tr><tr><td>Random Forest</td><td>0.929 ± 0.193</td><td>0.902 ± 0.250</td></tr><tr><td>FLORAL</td><td>0.784 ± 0.165</td><td>0.444 ± 0.420</td></tr><tr><td>Logistic</td><td>0.744 ± 0.210</td><td>0.417 ± 0.274</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Figure 2.** Top transformations (based on ROC-AUC of best RF/Lasso models)</figcaption></figure>

## Discussion and Conclusion

Baseline lung microbiota showed potential to predict nutritional response to CFTR modulators. Most transformations did not substantially change model performance compared to using simple relative abundances. However, they may affect the interpretation of selected microbial features, especially in penalized models like Lasso [2]. In our setting, all clinical covariates were forced into the models, which may have slightly reduced predictive performance (in favor of interpretability), particularly in Random Forest.

A more flexible approach - where covariates are softly penalized rather than strictly included - could help balance clinical relevance and model stability (e.g., using Ridge regression). Although it did not show an impact in our data, Lasso assumes a linear effect of quantitative predictors on the logit function. Exploring nonlinear functions, such as splines, could better capture complex associations.

In this study, we did not analyze which bacteria, combinations of bacteria, or α-diversity indexes were frequently and consistently selected by each method, nor the agreement of selections across different data transformations or machine learning methods. This will be addressed in a subsequent step aimed at identifying complex microbial signals.

## References

[1] Fei et al. (2024). Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL. Cell Rep Methods. https://doi.org/10.1016/j.crmeth.2024.100899
[2] Karwowska et al. (2025). Effects of data transformation and model selection on feature importance in microbiome classification data. Microbiome. https://doi.org/10.1186/s40168-024-01996-6
[3] Khan et al. (2020). Multiclass disease classification from microbial whole-community metagenomes. Pac Symp Biocomput, 25, 55-66. : https://doi.org/10.1142/9789811215636_0006
[4] Li et al. (2024). Lung microbiome: new insights into the pathogenesis of respiratory diseases. Signal Transduct Target Ther. https://doi.org/10.1038/s41392-023-01722-y

This work was supported by the French-Chilean INRIA Associate Team VALPO (DRI-012557).

DK was supported by the Inria Chile - Inria DRI Research Internship Mobility Program.

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