A modeling platform to predict cancer survival and therapy outcomes using tumor tissue derived metabolomics data.

Cancer is a complex and broad disease that is challenging to treat, partially due to the vast molecular heterogeneity among patients even within the same subtype. Currently, no reliable method exists to determine which potential first-line therapy would be most effective for a specific patient, as randomized clinical trials have concluded that no single regimen may be significantly more effective than others. One ongoing challenge in the field of oncology is the search for personalization of cancer treatment based on patient data. With an interdisciplinary approach, we show that tumor-tissue derived metabolomics data is capable of predicting clinical response to systemic therapy classified as disease control vs. progressive disease and pathological stage classified as stage I/II/III vs. stage IV via data analysis with machine-learning techniques (AUROC = 0.970; AUROC=0.902). Patient survival was also analyzed via statistical methods and machine-learning, both of which show that tumor-tissue derived metabolomics data is capable of risk stratifying patients in terms of long vs. short survival (OS AUROC = 0.940TEST; PFS AUROC = 0.875TEST). A set of key metabolites as potential biomarkers and associated metabolic pathways were also found for each outcome, which may lead to insight into biological mechanisms. Additionally, we developed a methodology to calibrate tumor growth related parameters in a well-established mathematical model of cancer to help predict the potential nuances of chemotherapeutic response. The proposed methodology shows results consistent with clinical observations in predicting individual patient response to systemic therapy and helps lay the foundation for further investigation into the calibration of mathematical models of cancer with patient-tissue derived molecular data. Chapters 6 and 8 were published in the Annals of Biomedical Engineering. Chapters 2, 3, and 7 were published in Metabolomics, Lung Cancer, and Pharmaceutical Research, respectively. Chapters 4 has been accepted for publication at the journal Metabolomics (in press) and Chapter 5 is in review at the journal Metabolomics. Chapter 9 is currently undergoing preparation for submission

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact on performance. Drug features appeared to be more predictive of drug response. Molecular fingerprint-based drug representations performed slightly better than learned representations, and gene expression data of cancer or drug response-specific genes also improved performance. In general, fully connected feature-encoding subnetworks outperformed other architectures, with DL outperforming other ML methods. Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.Author summary Cancer therapies often fail because tumor cells become resistant to treatment. One way to overcome resistance is by treating patients with a combination of two or more drugs. Some combinations may be more effective than when considering individual drug effects, a phenomenon called drug synergy. Computational drug synergy prediction methods can help to identify new, clinically relevant drug combinations. In this study, we developed several deep learning models for drug synergy prediction. We examined the effect of using different types of deep learning architectures, and different ways of representing drugs and cancer cell lines. We explored the use of biological prior knowledge to select relevant cell line features, and also tested data-driven feature reduction methods. We tested both precomputed drug features and deep learning methods that can directly learn features from raw representations of molecules. We also evaluated whether including genomic features, in addition to gene expression data, improves the predictive performance of the models. Through these experiments, we were able to identify strategies that will help guide the development of new deep learning models for drug synergy prediction in the future.Competing Interest StatementThe authors have declared no competing interest.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit and through a PhD scholarship (SFRH/BD/130913/2017) awarded to Delora Baptista.info:eu-repo/semantics/publishedVersio

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction

Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases

CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models

Large pre-trained language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology, has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Our proposed few-shot learning approach uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. Our experiments, which involved seven rare tissues from different cancer types, demonstrated that the LLM-based prediction model achieved significant accuracy with very few or zero samples. Our proposed model, the CancerGPT (with $\sim$ 124M parameters), was even comparable to the larger fine-tuned GPT-3 model (with $\sim$ 175B parameters). Our research is the first to tackle drug pair synergy prediction in rare tissues with limited data. We are also the first to utilize an LLM-based prediction model for biological reaction prediction tasks