Predicting Anti-Cancer Drug Combination Responses with a Temporal Cell State Network Model

Sarmah, Deepraj; Meredith, Wesley O.; Weber, Ian K; Price, Madison R; Birtwistle, Marc R.

doi:10.1101/2022.10.05.511031

Cited by 3 publications

(5 citation statements)

References 107 publications

(127 reference statements)

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“…Drug pairs derived by combining a drug from each cluster in the drug similarity network were synergistic across multiple synergy metrics. (Sarmah et al, 2023) Developed a temporal cell state network of cancer cells across stages of the cell cycle.…”

Section: Modelmentioning

confidence: 99%

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Computational Advancements in Cancer Combination Therapy Prediction

Flanary¹,

Fisher²,

Wilk³

et al. 2023

Preprint

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As cancer remains resistant to several modes of treatment, novel therapeutics are still under active investigation to overcome treatment inefficacy in cancer. Given the high attrition rate of de novo drug discovery, drug screening, and drug repurposing have offered time- and cost-effective alternative strategies for the identification of potentially effective therapeutics. In contrast to large-scale drug screens, computational approaches for drug repurposing leverage the increasing amounts of biomedical data to predict candidate therapeutic agents prior to testing in biological models. Current studies in drug repurposing for cancer therapy prediction have increasingly focused on the prediction of combination therapies, as combination therapies have numerous advantages over monotherapies. These include increased effect from synergistic interactions, reduced toxicity from lowered drug doses, and a reduced risk of resistance due to multiple non-overlapping mechanisms of action. This review provides a summary of several classes of computational methods used for drug combination therapy prediction in cancer research, including networks, regression-based machine learning, classifier machine learning models, and deep learning approaches, with the goal of presenting current progress in the field, particularly to non-computational cancer biologists. We conclude by discussing the need for further advancements in technologies that incorporate disease mechanisms, drug characteristics, multi-omics data, and clinical considerations to generate effective patient-specific drug combinations, as holistic data integration will inevitably result in optimal targeted therapeutics for cancer.

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Section: Modelmentioning

confidence: 99%

“…Given recent revelations on the nature of cancer cell plasticity from single-cell RNA sequencing studies, a recent publication from Sarmah et al aimed to predict drug combination responses using a temporal cell state network model (Sarmah et al, 2023). The authors explored the possibility that the types of cancer cells within a tumor (i.e.…”

Section: Modelmentioning

confidence: 99%

Computational Advancements in Cancer Combination Therapy Prediction

Flanary¹,

Fisher²,

Wilk³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the task of identifying optimal drug combinations is complicated by the significant variability in tumor types and patient responses, along with the complexities of cancer biology, the high dimensionality of data, and the number of drug combinations far beyond what is possible for clinical testing (Kong et al, 2022; Narayan et al, 2020). These challenges make it difficult to predict which combinations will be most effective, necessitating advanced computational models and extensive experimental validation to navigate the vast landscape of potential drug combinations and tailor treatments to cohort patients’ needs (Sarmah et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Cancer treatment is an intricate field, with the ongoing quest to develop therapies that effectively target the disease while minimizing side effects. The application of synergistic drug combinations builds on the idea of lowering the concentration of both drugs to archieve the same effect with less side effects and represents an important advancement in cancer therapy(Duarte and Vale, 2022) This approach aims to overcome the limitations of single-agent treatments by improving efficacy and reducing the likelihood of drug resistance (Delou et al, 2019) However, the task of identifying optimal drug combinations is complicated by the significant variability in tumor types and patient responses, along with the complexities of cancer biology, the high dimensionality of data, and the number of drug combinations far beyond what is possible for clinical testing (Kong et al, 2022; Narayan et al, 2020) These challenges make it difficult to predict which combinations will be most effective, necessitating advanced computational models and extensive experimental validation to navigate the vast landscape of potential drug combinations and tailor treatments to cohort patients’ needs (Sarmah et al, 2023)…”

Section: Introductionmentioning

confidence: 99%

Advancing Personalized Cancer Therapy: Onko_DrugCombScreen - A Novel Shiny App for Precision Drug Combination Screening

Yang,

Wang,

Dönitz

et al. 2024

Preprint

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Identifying genotype-guided drug combinations in cancer therapy represents an unmet medical need and is important in enhancing efficacy and reducing toxicity. However, the exponential increase in combinatorial possibilities constrains the ability to identify and validate effective drug combinations. In this context, we have developedOnko_DrugCombScreen, an innovative tool aiming at advancing precision medicine based on identifying significant drug combination candidates in a target cancer cohort compared to a comparison cohort.Onko_DrugCombScreen, inspired by the Molecular Tumor Board (MTB) process, synergizes drug knowledge-base analysis with various statistical methodologies and data visualization techniques to pinpoint drug combination candidates. Validated through a TCGA-BRCA case study,Onko_DrugCombScreenhas demonstrated its proficiency in discerning established drug combinations in a specific cancer type and in revealing potential novel drug combinations. By enhancing the capability of drug combination discovery through drug knowledge bases,Onko_DrugCombScreenrepresents a significant advancement in personalized cancer treatment by identifying promising drug combinations, setting the stage for the development of more precise and potent combination treatments in cancer care.Availabilityhttps://rshiny.gwdg.de/apps/onko_drugcombscreenGit Repositoryhttps://gitlab.gwdg.de/MedBioinf/mtb/onko_drugcombscreen

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“…Given recent revelations on the nature of cancer cell plasticity from single-cell RNA sequencing studies, a recent publication from Sarmah et al aimed to predict drug combination responses using a temporal cell state network model [25]. The authors explored the possibility that the types of cancer cells within a tumor (i.e.…”

Section: Introductionmentioning

confidence: 99%

Computational Advancements in Cancer Combination Therapy Prediction

Flanary,

Fisher,

Wilk

et al. 2023

JCO Precision Oncology

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Given the high attrition rate of de novo drug discovery and limited efficacy of single-agent therapies in cancer treatment, combination therapy prediction through in silico drug repurposing has risen as a time- and cost-effective alternative for identifying novel and potentially efficacious therapies for cancer. The purpose of this review is to provide an introduction to computational methods for cancer combination therapy prediction and to summarize recent studies that implement each of these methods. A systematic search of the PubMed database was performed, focusing on studies published within the past 10 years. Our search included reviews and articles of ongoing and retrospective studies. We prioritized articles with findings that suggest considerations for improving combination therapy prediction methods over providing a meta-analysis of all currently available cancer combination therapy prediction methods. Computational methods used for drug combination therapy prediction in cancer research include networks, regression-based machine learning, classifier machine learning models, and deep learning approaches. Each method class has its own advantages and disadvantages, so careful consideration is needed to determine the most suitable class when designing a combination therapy prediction method. Future directions to improve current combination therapy prediction technology include incorporation of disease pathobiology, drug characteristics, patient multiomics data, and drug-drug interactions to determine maximally efficacious and tolerable drug regimens for cancer. As computational methods improve in their capability to integrate patient, drug, and disease data, more comprehensive models can be developed to more accurately predict safe and efficacious combination drug therapies for cancer and other complex diseases.

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Predicting Anti-Cancer Drug Combination Responses with a Temporal Cell State Network Model

Cited by 3 publications

References 107 publications

Computational Advancements in Cancer Combination Therapy Prediction

Computational Advancements in Cancer Combination Therapy Prediction

Advancing Personalized Cancer Therapy: Onko_DrugCombScreen - A Novel Shiny App for Precision Drug Combination Screening

Computational Advancements in Cancer Combination Therapy Prediction

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