Signaling pathway evaluation of leading ATRi, PARPi and CDK7i cancer compounds targeting the DNA Damage Response using Causal Inference
Truter, N.; Bergh, W.; van den Heever, M.; Horn, S.; Shaw, K.; Leggate, D.; Wilkie, N.; Singh, R.
Show abstract
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.
Matching journals
The top 14 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Genomic profile in TGCT Mexican patients reveals a potential biomarker of sensitivity to platinum-based therapy 93%
- Drug-adapted cancer cell lines reveal drug-induced heterogeneity and enable the identification of biomarker candidates for the acquired resistance setting 91%
- A novel role for the tumor suppressor gene ITF2 in lung tumorigenesis and chemotherapy response 91%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- ATR and PKMYT1 inhibition re-sensitize a subset of TNBC patient-derived models to carboplatin inducing mitotic catastrophe 91%
- Inhibitor of the nuclear transport protein XPO1 enhances the anticancer efficacy of KRAS G12C inhibitors in preclinical models of KRAS G12C mutant cancers 90%
- Characterization of non-monotonic relationships between tumor mutational burden and clinical outcomes 89%
Similar papers in this journal
- A Network Pharmacology Approach to Elucidate the Anti-inflammatory Effects of Ellagic Acid 89%
- Pharmacophore modeling, 2D-QSAR, Molecular Docking and ADME studies for the discovery of inhibitors of PBP2a in MRSA 89%
- Complementation cloning identifies the essentials of mammalian Mastl kinase activation 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.