Biology subjects

Ghislat, G.

Publications and source records attributed to Ghislat, G..

5 recordsLinked to original sources

bioRxiv2025

A Pan-Cancer, Pan-Treatment (PCPT) Model For Predicting Drug Responses From Patient-Derived Xenografts With Application To Cancer Types With Sparse Training Data

Predicting personalized treatment-specific responses in cancer patients requires not only robust experimental models such as PDX, which generate accurate training data on different treatment responses for the same model, but also computational models that can translate this knowledge into decisions for real patients in a timely manner. The translatability of PDX data into patient-specific outcomes is limited by the challenges in the data size requirements for training machine learning models. Previously, ML models have been developed for the two most abundant cancer types, viz. BRCA and CRC, but the same could not be scaled up to other cancer types with a smaller number of PDX samples. Here, we provide an ML framework to train a single pan-cancer, pan-treatment model for predicting treatment outcomes. We show that such models give promising results for all cancer types considered and reproduce the accuracy levels of individually trained cancer types. In the proposed model, all PDX genomic profiles from all cancer types are used as the training data, and instead of partitioning them into cancer types for each model, the cancer type and treatment name are appended as the input features of the training model. Using genomic-only and treatment-only embeddings and combining them with PCA-based dimensionality reduction, our models show promising results and provide a framework for further improvements and real-time use for best treatment selections for cancer patients. Data and Code AvailabilityAll raw and processed data, along with analysis scripts, are available at: https://github.com/Sciwhylab/pcpt

bioinformatics↗

bioRxiv2024

Predicting temozolomide response in low-grade glioma patients with large-scale machine learning

BackgroundTemozolomide is the primary chemotherapeutic agent and first-line treatment for low-grade glioma (LGG). Although LGGs are generally less aggressive than high-grade gliomas (HGGs), they can eventually progress into HGGs, making it crucial to maximise the efficacy of initial LGG treatment. MethodsIn this study, we analysed data from 109 LGG patients in The Cancer Genome Atlas (TCGA) to assess the predictive performance of 12 machine learning (ML) classification algorithms in forecasting temozolomide response, utilising six types of omics data. Cross-validation and bootstrapping bias correction were employed to compare the performance of these models with that of a conventional biomarker-based model using MGMT promoter methylation status. ResultsAmong the models, the miRNA-based approach using the XGBoost algorithm showed the most promising predictive performance, with a Matthews Correlation Coefficient (MCC) of 0.447, outperforming the auto-ML method JADBio (MCC = 0.250) and the MGMT biomarker-based model. Converting the task into a regression framework by encoding cancer response as a continuous variable generally weakened predictive performance, with best model based on methylation profile (MCC = 0.344). Additionally, ML models focusing on biomarker-relevant features demonstrated improved prediction accuracy, though not to the extent of omics-based models, with the support vector machine (SVM) model achieving an MCC of 0.331. Incorporating clinical variables, such as patient age and Karnofsky score, further enhanced predictive power, with the LR-OMC model achieving the highest MCC (0.483). Feature importance analysis identified six significant miRNA factors, including three tumour-related miRNAs (miR-335, let-7f, and miR-7-2) and three potential biomarkers (miR-204, miR-6513, and miR-376). DiscussionOverall, this study systematically demonstrates the potential of large-scale analyses combining machine learning and omics data to predict temozolomide response, offering superior predictive accuracy compared to standard MGMT biomarkers.

bioinformatics↗

bioRxiv2020

An NF-κB/IRF1 axis programs cDC1s to drive anti-tumor immunity

Conventional type 1 dendritic cells (cDC1s) are critical for anti-tumor immunity. They acquire antigens from dying tumor cells and cross-present them to CD8+ T cells, promoting the expansion of tumor-specific cytotoxic T cells. However, the signaling pathways that govern the anti-tumor functions of cDC1s are poorly understood. We mapped the molecular pathways regulating intra-tumoral cDC1 maturation using single cell RNA sequencing. We identified NF-{kappa}B and IFN pathways as being highly enriched in a subset of functionally mature cDC1s. The specific targeting of NF-{kappa}B or IFN pathways in cDC1s prevented the recruitment and activation of CD8+ T cells and the control of tumor growth. We identified an NF-{kappa}B-dependent IFN{gamma}-regulated gene network in cDC1s, including cytokines and chemokines specialized in the recruitment and activation of cytotoxic T cells. We used single cell transcriptomes to map the trajectory of intra-tumoral cDC1 maturation which revealed the dynamic reprogramming of tumor-infiltrating cDC1s by NF-{kappa}B and IFN signaling pathways. This maturation process was perturbed by specific inactivation of either NF-{kappa}B or IRF1 in cDC1s, resulting in impaired expression of IFN-{gamma}-responsive genes and consequently a failure to efficiently recruit and activate anti-tumoral CD8+ T cells. Finally, we demonstrate the relevance of these findings to cancer patients, showing that activation of the NF-{kappa}B/IRF1 axis in association with cDC1s is linked with improved clinical outcome. The NF-{kappa}B/IRF1 axis in cDC1s may therefore represent an important focal point for the development new diagnostic and therapeutic approaches to improve cancer immunotherapy. One Sentence SummaryNF-{kappa}B and IRF1 coordinate intra-tumoral cDC1 maturation and control of immunogenic tumor growth.

immunology↗

bioRxiv2020

Characterizing the relationship between the chemical structures of drugs and their activities on primary cultures of pediatric solid tumors

Better drugs are required to manage pediatric cancers. A high-throughput screen of drugs in primary cultures derived from orthotopic patient-derived xenografts (O-PDX) of pediatric solid tumours has been recently published. Here we analyzed these data sets to find out whether it is possible to leverage them for identifying new drug leads in a phenotypic manner. We found that drugs bearing a higher number of heterocyclic rings, two carbon-hetero bonds and halogens are associated to submicromolar potency in alveolar rhabdomyosarcoma and osteosarcoma O-PDXs. Furthermore, Murcko scaffolds 1-cyclopentyl-octahydro-1H-indene and tetradecahydroanthracene can be utilized as starting scaffolds to selectively optimize potency against osteosarcoma since drugs bearing this scaffold displayed superior O-PDX culture potency. Lastly, we have generated QSAR (Quantitative Structure-Activity Relationship) models able to predict the potency of drugs on each O-PDX tumor. To permit their use to guide drug repositioning on these 30 O-PDX cell cultures, we are providing a user-friendly web server implementing these QSAR models at https://rnewbie.shinyapps.io/Shobek-master

pharmacology and toxicology↗

bioRxiv2018

Machine learning models to predict in vivo drug response via optimal dimensionality reduction of tumour molecular profiles

Inter-tumour heterogeneity is one of cancers most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, lessons from the past indicate that single-gene markers of response are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. Results show that combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: Paclitaxel (breast cancer), Binimetinib (breast cancer) and Cetuximab (colorectal cancer). Interestingly, each of these ML models identify some responsive PDXs not harbouring the best actionable mutation for that case (such PDXs were missed by those single-gene markers). Moreover, ML multi-gene predictors generally retrieve a much higher proportion of treatment-sensitive PDXs than the corresponding single-gene marker. As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if multiple ML algorithms were applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

bioinformatics↗