Modeling in systems biology: Causal understanding before prediction?

Integrating system biology and intratumor gene therapy by trans-complementing the appropriate co-stimulatory molecule as payload in oncolytic herpes virus

Systems biology has been applied at the multi-scale level within the cancer field, improving cancer prevention, diagnosis and enabling precision medicine approaches. While systems biology can expand the knowledge and skills for oncological treatment, it also represents a challenging expedition due to cancer complexity, heterogeneity and diversity not only between different cancer indications, but also in its evolution process through space and time. Here, by characterizing the transcriptional perturbations of the tumor microenvironment induced by oncolytic, we aimed to rationally design a novel armed oncolytic herpes virus. We found that intratumor oncovirotherapy with HSV-1 induces T-cell activation signatures and transcriptionally activates several costimulatory molecules. We identified differentially expressed costimulatory receptors and binding partners, where inducible co-stimulators (ICOS) resulted in the potentially most beneficial targeted therapy. Through an ex-vivo transcriptomic analysis, we explored the potential of arming an oncolytic virus as a combination therapy strategy; in particular, we engineered a targeted herpes virus encoding ICOSL (THV_ICOSL), which resulted in a significant improvement in tumor size control compared to unarmed parental virus. Also, combination with a PD-1 inhibitor enhanced antitumor efficacy as predictable by upregulation of PD-1 and ligands pair (PD-L1/PD-L2) upon oncolytic virus injection. Generation of the human version of this virus encoding hICOSL orthologue effectively and specifically activated human T cells by triggering the ICOS pathway. Our data support the data-driven generation of armed oncolytic viruses as combination immunotherapeutic with checkpoint inhibitors.

Prioritizing drug targets by perturbing biological network response functions

Therapeutic interventions are designed to perturb the function of a biological system. However, there are many types of proteins that cannot be targeted with conventional small molecule drugs. Accordingly, many identified gene-regulatory drivers and downstream effectors are currently undruggable. Drivers and effectors are often connected by druggable signaling and regulatory intermediates. Methods to identify druggable intermediates therefore have general value in expanding the set of targets available for hypothesis-driven validation. Here we identify and prioritize potential druggable intermediates by developing a network perturbation theory, termed NetPert, for response functions of biological networks. Dynamics are defined by a network structure in which vertices represent genes and proteins, and edges represent gene-regulatory interactions and protein-protein interactions. Perturbation theory for network dynamics prioritizes targets that interfere with signaling from driver to response genes. Applications to organoid models for metastatic breast cancer demonstrate the ability of this mathematical framework to identify and prioritize druggable intermediates. While the short-time limit of the perturbation theory resembles betweenness centrality, NetPert is superior in generating target rankings that correlate with previous wet-lab assays and are more robust to incomplete or noisy network data. NetPert also performs better than a related graph diffusion approach. Wet-lab assays demonstrate that drugs for targets identified by NetPert, including targets that are not themselves differentially expressed, are active in suppressing additional metastatic phenotypes.

Dealing with dimensionality: the application of machine learning to multi-omics data

MOTIVATION

Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets.

RESULTS

Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments.

AVAILABILITY AND IMPLEMENTATION

All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analyzing causal relationships in proteomic profiles using CausalPath

CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).