Bergh, W. | bioRxiv Science

bioRxiv2024

Signaling pathway evaluation of leading ATRi, PARPi and CDK7i cancer compounds targeting the DNA Damage Response using Causal Inference

IntroductionThere are many cancer drugs in development which target the DNA damage response (DDR), following early successes of drugs such as olaparib. However, various challenges to the success of these inhibitors exist, including the emergence of resistance, the identification of appropriate biomarkers to identify patients who will respond to treatment, as well as the identification of combination therapies that improve efficacy without a concomitant increase in toxicity. While the identification of biomarkers of resistance could aid in overcoming these challenges, current methods mostly generate lists of potential genes, proteins that display changes in cancer patients, without exposing the underlying, and often critical, mechanisms of resistance. MethodsWe have developed the Adaptable Large-Scale Causal Analysis (ALaSCA) software platform, which applies Pearlian Causal Inference (PCI) techniques to specifically transcriptomic, proteomic and phenotypic multi-omics data. ALaSCA quantifies the causal contributions of different biological pathways to an outcome such as responsiveness to treatment. The strength of applying PCI to biological pathways lies in quantifying the causal contributions of targets, through their related pathways, to drug sensitivity - as opposed to merely enriching or grouping lists of genes into pathways. We applied ALaSCA to transcriptomic data for several different compounds related to three known inhibitor types that target DDR proteins: an ATR, a CDK7, and several PARP inhibitors. Our aim was to use causal methods to evaluate biological signaling pathways to identify resistance mechanisms that can be used for patient stratification and development of combination therapies in breast, ovarian and non-small cell lung cancer (NSCLC). Key findingsWe observed that niraparib seems to have a different resistance mechanism than other PARPi inhibitors in breast and NSCLC, which is driven by CDK1 as opposed to base excision or nucleotide excision repair. Additionally, CDK7 appears to be a significant driver of PARP inhibitor resistance, especially for niraparib, through predominantly the G2/M cell cycle phase and to a lesser extent nucleotide excision repair in breast and ovarian cancer, but not in NSCLC. Lastly, we identified that genes from the homologous recombination pathway drive resistance to AZD6738, an ATR inhibitor, in breast cancer, and that AZD6738 and irinotecan have differing resistance mechanisms in ovarian cancer, indicating the potential of combining these treatments. Next stepsOur findings demonstrate the potential of ALaSCA to generate interesting insights for treatments when applied to public data and well-known inhibitors. Partnership with industry drug discovery groups using proprietary data to rerun the above evaluations will further refine and confirm these findings.

bioinformatics↗

bioRxiv2023

ALaSCA: A novel in silico simulation platform to untangle biological pathway mechanisms, with a case study in Type 1 Diabetes progression

IntroductionThe analysis of signaling pathways is a cornerstone in clarifying the biological mechanisms involved in complex genetic disorders. These pathways have intricate topologies, and the existing methods that are used for the interpretation of these pathways, remain limited. We have therefore developed the Adaptable Large-Scale Causal Analysis (ALaSCA) computational platform, which uses causal analysis and counterfactual simulation techniques. ALaSCA offers the ability to simulate the outcome of a number of different hypotheses to gain insight into the complex dynamics of biological mechanisms prior to, or even without, wet lab experimentation. ALaSCA is offered as a proprietary Python library for bioinformaticians and data scientists to use in their life sciences workflows. Here we demonstrate the ability of ALaSCA to untangle the pivots and redundancies within biological pathways of various drivers of a specific phenotypic process. This is achieved by studying a major disease of global relevance, namely Type 1 Diabetes (T1D), and quantifying causal relationships between antioxidant proteins and T1D progression. ALaSCA is also benchmarked against standard associative analysis methods. MethodsWe use our in silico simulation platform, ALaSCA, to apply both a number of machine learning (ML) and data imputation techniques, and perform causal inference and counterfactual simulation. ALaSCA uses standard ML and causal analysis libraries as well as custom code developed for data imputation and counterfactual simulation. Counterfactual simulation is a method for simulating potential or hypothetical model outcomes in the field of causal analysis (Glymour, Pearl and Jewell, 2016). We apply ALaSCA to T1D by using proteomic data from Liu et al. (2018), as the patients were selected based on the presence of T1D susceptible HLA (human leukocyte antigen)-DR/DQ alleles through genotyping at birth and followed prospectively. The genetic cause of T1D in this cohort is therefore known and the mechanism and proteins through which it causes T1D are well-characterized. This biological mechanism was converted into a directed acyclic graph (DAG) for the subsequent causal analyses. The dataset was used to benchmark the causal inference and counterfactual simulation capabilities of ALaSCA. Results and discussionAfter data imputation of the Liu, et al. (2018) dataset, causal inference and counterfactual simulation were completed. The causal inference output of the HLA, antioxidant, and non-causal proteins showed that the HLA proteins had the overall strongest causal effects on T1D, with antioxidant proteins having the overall second largest causal effects on T1D. The non-causal proteins showed negligibly small effects on T1D in comparison with the HLA and antioxidant proteins. With counterfactual simulation we were able to replicate evidence for and gain understanding into the protective effect that antioxidant proteins, specifically Superoxide dismutase 1 (SOD1), have in T1D, a trend which is seen in literature. We were also able to replicate an unusual case from literature where antioxidant proteins, specifically Catalase, do not have a protective effect on T1D. ConclusionBy analyzing the disease mechanism, with the inferred causal effects and counterfactual simulation, we identified the upstream HLA proteins, specifically the DR alpha chain and DR beta 4 chain proteins as causes of the protective effect of the antioxidant proteins on T1D. In contrast, through counterfactual simulation of the unusual case, in which the DR alpha chain and DR beta 4 chain proteins are not present in the model, we saw that the adverse effect which the antioxidant proteins have on T1D is due to the HLA protein, DQ beta 1 chain, and not the antioxidant proteins themselves. Future work would entail the application of the ALaSCA platform on various other diseases, and to integrate it into wet lab experimental design in a number of different biological study areas and topics.

bioinformatics↗