A graph model of combination therapies

Sang, Mengmeng; Dong, Ang; Wu, Shiwei; Li, Feng; Jing, Wang; Griffin, Christopher; Wu, Rongling

doi:10.1016/j.drudis.2022.02.001

Cited by 5 publications

(4 citation statements)

References 57 publications

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“…For example, perhaps the adaptive PDR mutations in cluster 2 cause misfolding of the PDR protein, resulting in lower fitness in RAD because this drug inhibits a chaperone that helps proteins to fold. In this case, it might be more correct to say that each of our six clusters affects fitness through a different, but potentially overlapping, suite of mechanisms (Wang et al 2023). Previous work demonstrating that mutations commonly affect multiple traits supports this broader view of the mechanistic differences between clusters (Paaby and Rockman 2013; Boyle et al 2017; Geiler-Samerotte et al 2020; Kinsler et al 2020).…”

Section: Resultsmentioning

confidence: 99%

“…This, as well as limits on our power to observe mutants with strong tradeoffs, suggest there may be additional mechanisms of FLU resistance beyond what we sampled (see (Cowen and Lindquist 2005)). Still, nuanced strategies to thwart resistance in cases where there are multiple types of resistant mutant are emerging (Maltas and Wood 2019; Gjini and Wood 2021; Wang et al 2023). For example, one idea is to apply a drug regimen that enriches for mutants that suffer strong tradeoffs before exploiting those tradeoffs (Iram et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…These observations suggest multidrug strategies that assume resistant mutants suffer consistent or common tradeoffs will often fail. On the other hand, nuanced strategies to forestall resistance that allow for multiple mutant types are emerging (Maltas and Wood 2019;Gjini and Wood 2021;Wang et al 2023). For example, one idea is to apply a drug regimen that enriches for mutants that suffer a particular tradeoff before exploiting that tradeoff (Iram et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Schmidlin,

Apodaca,

Newell

et al. 2023

Preprint

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There is growing interest in designing multidrug therapies that leverage tradeoffs to combat drug resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into six classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different molecular mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, and some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments that thwart resistance. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a relatively smaller number of groups, our findings suggest that resistance evolves through a relatively small number of mechanisms such that multidrug strategies that hinder resistance may be possible. Finally, by grouping mutants that likely affect fitness through similar underlying mechanisms, our findings also guide efforts to map the phenotypic impacts of mutation and predict the impacts of some mutations from others.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Schmidlin,

Apodaca,

Newell

et al. 2023

Preprint

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show abstract

“…The strong positive correlation of these drugs with m5C regulators indicates that many of these that are currently used for other diseases and conditions can be explored as a therapeutic option for AML. Furthermore, the analysis of antagonistic and coordinated effects between drugs will provide references for precise medical treatment of LAML [ 72 ].…”

Section: Discussionmentioning

confidence: 99%

5-methylcytosine RNA modification regulators-based patterns and features of immune microenvironment in acute myeloid leukemia

Ding,

Bajpai,

et al. 2024

Aging

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Acute myeloid leukemia (AML) is a highly heterogeneous malignant disease of the blood cell. The current therapies for AML are unsatisfactory and the molecular mechanisms underlying AML are unclear. 5-methylcytosine (m5C) is an important posttranscriptional modification of mRNA, and is involved in the regulation of mRNA stability, translation, and other aspects of RNA metabolism. However, based on our knowledge of published literature, the role of the m5C regulators has not been explored in AML till date. In this study, we clarified the expression and gene variants of m5C regulators in AML and found that most m5C regulators were differentially expressed and correlated with disease prognosis. We also found that the methylation status of certain m5C regulators (e.g., DNMT3A , DNMT3B ) affects the survival of AML patients. Two m5C modification subtypes, and high- and low-risk subgroups identified based on the expression of m5C regulators showed significant differences in the prognosis as well as immune cell infiltration. In addition, most of the m5C regulators were found to be correlated with miRNA expression in AML, as well as IC50 values of many drugs. The miRNA and GSVA analysis were used to identify the different miRNAs and KEGG or hallmark pathways between high- and low-risk subgroups. We also built a prognostic model based on m5C regulators, which was validated by two GSE databases. To verify the reliability of our analysis and conclusions, qPCR was used to identify the expressions of m5C regulators between normal and AML. In summary, we comprehensively explored the molecular characteristics of m5C regulators and built a prognostic model in AML. We proposed new mechanistic insights into the role of m5C in multiple databases and clinical data, which may pave novel ways for the development of therapeutic strategies.

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Biochemical reaction network topology defines dose-dependent Drug–Drug interactions

Babaei

Evers

Shokri

et al. 2023

Computers in Biology and Medicine

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A graph model of combination therapies

Cited by 5 publications

References 57 publications

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

5-methylcytosine RNA modification regulators-based patterns and features of immune microenvironment in acute myeloid leukemia

Biochemical reaction network topology defines dose-dependent Drug–Drug interactions

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