Presentation
Anti-Cancer Drug Response Prediction on Patient-Derived Xenografts: Learning from Extremely Limited Data
DescriptionCancer is a family of complex genetic disorders characterized by the accumulation of genetic and epigenetic alterations that drive uncontrolled cell growth and metastasis. It is becoming a leading cause of death worldwide, accounting for 10 million deaths in 2020. In 2024, NIH projects that roughly 2 million people will be diagnosed with cancer in the United States. However, developing novel therapies or selecting a best-suited treatment for a patient poses a challenge to the scientific community due to the individualized nature of the disease.
Cancer research is often hampered by limited data, particularly in the context of rare cancer types and heterogenous disease mechanisms, restricting the utility of the latest advances in Artificial Intelligence (AI) in this domain. This issue is dire for more refined biological models, such as patient-derived xenografts (PDX), that are more accurate in reflecting patient treatment responses than immortalized cell lines but more costly in terms of time and resources for data generation. Due to the high complexity and cost of PDX experiments, it is unlikely to garner datasets on the scale of traditional AI domains like computer vision or natural language processing. This situation calls for more efficient approaches to learning from data.
Human reasoning heavily relies on the context surrounding the problem. To achieve generalizability, we compare various settings and examples to find key differences in subjects and outcomes. This strategy is critical for efficiently utilizing limited available data for learning. Currently, the closest analog to this strategy in the AI field is Contrastive Learning (CL). This approach leverages the relatively abundant cell line drug screening data for transfer learning to drug response prediction of PDXs. CL utilizes not only positive data (responsive samples) but also negative data (non-responsive samples), which is ubiquitously generated during drug response experiments. The most famous example of CL is Contrastive Language-Image Pretraining (CLIP), which bridges the gap between textual and visual information. We adopt a similar approach to create compatible representations between different biological models, resulting in Contrastive Transfer Learning. This emerging machine learning paradigm improves the performance and stability of AI models across different application domains by emphasizing the learning of more generalizable feature representations. To enhance model explainability, we create a biologically guided neural network based on the KEGG pathways and BRITE hierarchy so we can elucidate the decision process via its activation pass.
We explored the utility of the proposed architecture in drug-specific response modeling, where we constructed an individual response model for each drug based on cancer gene expressions. We used the area under the receiver-operator curve (AUROC) to evaluate prediction performance. We compare our approach to the stand-alone fully connected neural network (FCNN) via 5-fold cross-validation (CV). Our approach improves the average AUROC score and reduces its standard deviation (std) from CV trials, producing more accurate and stable prediction results. Baseline FCNN performance for Selumetinib, Bortezomib and Paclitaxel is AUROC=0.87, std=0.188; AUROC=0.92, std=0.068; AUROC=0.87, std=0.077, respectively; while our approach achieves AUROC=0.99, std=0.003; AUROC=0.99, std=0.008; AUROC= 0.97, std=0.018 for these three drugs, correspondingly.
Cancer research is often hampered by limited data, particularly in the context of rare cancer types and heterogenous disease mechanisms, restricting the utility of the latest advances in Artificial Intelligence (AI) in this domain. This issue is dire for more refined biological models, such as patient-derived xenografts (PDX), that are more accurate in reflecting patient treatment responses than immortalized cell lines but more costly in terms of time and resources for data generation. Due to the high complexity and cost of PDX experiments, it is unlikely to garner datasets on the scale of traditional AI domains like computer vision or natural language processing. This situation calls for more efficient approaches to learning from data.
Human reasoning heavily relies on the context surrounding the problem. To achieve generalizability, we compare various settings and examples to find key differences in subjects and outcomes. This strategy is critical for efficiently utilizing limited available data for learning. Currently, the closest analog to this strategy in the AI field is Contrastive Learning (CL). This approach leverages the relatively abundant cell line drug screening data for transfer learning to drug response prediction of PDXs. CL utilizes not only positive data (responsive samples) but also negative data (non-responsive samples), which is ubiquitously generated during drug response experiments. The most famous example of CL is Contrastive Language-Image Pretraining (CLIP), which bridges the gap between textual and visual information. We adopt a similar approach to create compatible representations between different biological models, resulting in Contrastive Transfer Learning. This emerging machine learning paradigm improves the performance and stability of AI models across different application domains by emphasizing the learning of more generalizable feature representations. To enhance model explainability, we create a biologically guided neural network based on the KEGG pathways and BRITE hierarchy so we can elucidate the decision process via its activation pass.
We explored the utility of the proposed architecture in drug-specific response modeling, where we constructed an individual response model for each drug based on cancer gene expressions. We used the area under the receiver-operator curve (AUROC) to evaluate prediction performance. We compare our approach to the stand-alone fully connected neural network (FCNN) via 5-fold cross-validation (CV). Our approach improves the average AUROC score and reduces its standard deviation (std) from CV trials, producing more accurate and stable prediction results. Baseline FCNN performance for Selumetinib, Bortezomib and Paclitaxel is AUROC=0.87, std=0.188; AUROC=0.92, std=0.068; AUROC=0.87, std=0.077, respectively; while our approach achieves AUROC=0.99, std=0.003; AUROC=0.99, std=0.008; AUROC= 0.97, std=0.018 for these three drugs, correspondingly.