XMR: an explainable multimodal neural network for drug response prediction

MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction

BACKGROUND

Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, while promising, frequently neglect the critical role of atomic interactions and fail to integrate drug fingerprints with SMILES for comprehensive molecular graph construction.

RESULTS

We introduce multimodal multi-channel graph attention network with adaptive fusion (MGATAF), a framework designed to enhance drug response predictions by capturing both local and global interactions among graph nodes. MGATAF improves drug representation by integrating SMILES and fingerprints, resulting in more precise predictions of drug effects. The methodology involves constructing multimodal molecular graphs, employing multi-channel graph attention networks to capture diverse interactions, and using adaptive fusion to integrate these interactions at multiple abstraction levels. Empirical results demonstrate MGATAF's superior performance compared to traditional and other graph-based techniques. For example, on the GDSC dataset, MGATAF achieved a 5.12% improvement in the Pearson correlation coefficient (PCC), reaching 0.9312 with an RMSE of 0.0225. Similarly, in new cell-line tests, MGATAF outperformed baselines with a PCC of 0.8536 and an RMSE of 0.0321 on the GDSC dataset, and a PCC of 0.7364 with an RMSE of 0.0531 on the CCLE dataset.

CONCLUSIONS

MGATAF significantly advances drug response prediction by effectively integrating multiple molecular data types and capturing complex interactions. This framework enhances prediction accuracy and offers a robust tool for personalized medicine, potentially leading to more effective and safer treatments for patients. Future research can expand on this work by exploring additional data modalities and refining the adaptive fusion mechanisms.

Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma

Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. This study explores the genetic basis of GR to improve prediction, prevention, and treatment strategies. We utilized public databases to obtain GR gene sets, single-cell data, and transcriptome data, applying univariate and multivariate regression analyses alongside machine learning to identify key genes and develop a predictive signature. The signature's performance was evaluated using survival analysis and time-dependent ROC curves on internal and external datasets. Enrichment and tumor immune microenvironment analyses were conducted to understand the mechanistic roles of the signature genes in GR. Our analysis identified a robust 22-gene signature with strong predictive performance across validation datasets. This signature was significantly associated with chromosomal processes, DNA replication, immune cell infiltration, and various immune scores based on enrichment and tumor microenvironment analyses. Importantly, the signature also showed potential in predicting the efficacy of immunotherapy in LUAD patients. Moreover, we identified alternative agents to gefitinib that could offer improved therapeutic outcomes for high-risk and low-risk patient groups, thereby guiding treatment strategies for gefitinib-resistant patients. In conclusion, the 22-gene signature not only predicts prognosis and immunotherapy efficacy in gefitinib-resistant LUAD patients but also provides novel insights into non-immunotherapy treatment options.

Integrated multi‐omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma

Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. This study explores the genetic basis of GR to improve prediction, prevention, and treatment strategies. We utilized public databases to obtain GR gene sets, single‐cell data, and transcriptome data, applying univariate and multivariate regression analyses alongside machine learning to identify key genes and develop a predictive signature. The signature's performance was evaluated using survival analysis and time‐dependent ROC curves on internal and external datasets. Enrichment and tumor immune microenvironment analyses were conducted to understand the mechanistic roles of the signature genes in GR. Our analysis identified a robust 22‐gene signature with strong predictive performance across validation datasets. This signature was significantly associated with chromosomal processes, DNA replication, immune cell infiltration, and various immune scores based on enrichment and tumor microenvironment analyses. Importantly, the signature also showed potential in predicting the efficacy of immunotherapy in LUAD patients. Moreover, we identified alternative agents to gefitinib that could offer improved therapeutic outcomes for high‐risk and low‐risk patient groups, thereby guiding treatment strategies for gefitinib‐resistant patients. In conclusion, the 22‐gene signature not only predicts prognosis and immunotherapy efficacy in gefitinib‐resistant LUAD patients but also provides novel insights into non‐immunotherapy treatment options.

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.