Critical involvement of the α(1,2)-fucosyltransferase in multidrug resistance of human chronic myeloid leukemia

Discovering the mechanism of action of drugs with a sparse explainable network

BACKGROUND

Although Deep Neural Networks (DDNs) have been successful in predicting the efficacy of cancer drugs, the lack of explainability in their decision-making process is a significant challenge. Previous research proposed mimicking the Gene Ontology structure to allow for interpretation of each neuron in the network. However, these previous approaches require huge amount of GPU resources and hinder its extension to genome-wide models.

METHODS

We developed SparseGO, a sparse and interpretable neural network, for predicting drug response in cancer cell lines and their Mechanism of Action (MoA). To ensure model generalization, we trained it on multiple datasets and evaluated its performance using three cross-validation schemes. Its efficiency allows it to be used with gene expression. In addition, SparseGO integrates an eXplainable Artificial Intelligence (XAI) technique, DeepLIFT, with Support Vector Machines to computationally discover the MoA of drugs.

FINDINGS

SparseGO's sparse implementation significantly reduced GPU memory usage and training speed compared to other methods, allowing it to process gene expression instead of mutations as input data. SparseGO using expression improved the accuracy and enabled its use on drug repositioning. Furthermore, gene expression allows the prediction of MoA using 265 drugs to train it. It was validated on understudied drugs such as parbendazole and PD153035.

INTERPRETATION

SparseGO is an effective XAI method for predicting, but more importantly, understanding drug response.

FUNDING

The Accelerator Award Programme funded by Cancer Research UK [C355/A26819], Fundación Científica de la AECC and Fondazione AIRC, Project PIBA_2020_1_0055 funded by the Basque Government and the Synlethal Project (RETOS Investigacion, Spanish Government).

Advances in the relationship between glycosyltransferases and multidrug resistance in cancer

Despite great progress in clinical treatment, cancer remains a serious health problem contributing to significant morbidity and mortality worldwide. Although chemotherapy is a common therapeutic measure, multidrug resistance (MDR) presents a major challenge that often leads to poor prognosis. The abnormal expression of glycosyltransferases (GTs) leading to aberrant glycosylation patterns are considered a marker of cancer. Furthermore, the biosynthesis of these glycoconjugates has been associated with tumor proliferation, invasion and metastasis. Recently, studies have found that GTs are involved in mediating MDR in cancer cells through complex mechanisms and can influence therapeutic effect. In this review, we focus on several types of cancers and summarize previous studies on the correlation between GTs and MDR.