Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells

Korkut, Anil; Wang, Weiqing; Demir, Emek; Aksoy, Bülent Arman; Jing, Xin; Molinelli, Evan; Babur, Özgün; Bemis, Debra L.; Sumer, S. Onur; Solit, David B.; Pratilas, Christine A.; Sander, Chris

doi:10.7554/elife.04640

Cited by 112 publications

(135 citation statements)

References 66 publications

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“…MYC activation is linked to therapeutic resistance, for example in breast cancer cells treated with PI3K pathway inhibitors and in c-Met-addicted cancers treated with c-Met inhibitors (Ilic et al, 2011; Muellner et al, 2011; Shen et al, 2015). In melanoma, our finding that MYC is a nexus of convergent resistance is consistent with a recent report that used network modeling to nominate MYC as a synergistic target with BRAF, then verified this finding by demonstrating synergy between JQ1 and vemurafenib treatment in a cell line (Korkut et al, 2015). Our findings are also interesting in light of a report suggesting that the eukaryotic initiation factor 4F (eIF4F) complex may act as a point of convergence between the ERK and PI3K resistance pathways in melanoma, as MYC and eIF4F interact in a well-characterized synthetic lethal feedforward loop to support tumorigenesis (Boussemart et al, 2014; Lin et al, 2008; Lin et al, 2012).…”

Section: Discussionsupporting

confidence: 91%

Melanoma Therapeutic Strategies that Select against Resistance by Exploiting MYC-Driven Evolutionary Convergence

et al. 2017

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SUMMARY Diverse pathways drive resistance to BRAF/MEK inhibitors in BRAF-mutant melanoma, suggesting that durable control of resistance will be a challenge. By combining statistical modeling of genomic data from matched pre-treatment and post-relapse patient tumors with functional interrogation of over 20 in vitro and in vivo resistance models, we discovered that major pathways of resistance converge to activate the transcription factor, c-MYC (MYC). MYC expression and pathway gene signatures were suppressed following drug treatment, then rebounded during progression. Critically, MYC activation was necessary and sufficient for resistance, and suppression of MYC activity using genetic approaches or BET bromodomain inhibition was sufficient to resensitize cells and delay BRAFi resistance. Finally, MYC-driven, BRAFi-resistant cells are hypersensitive to the inhibition of MYC synthetic lethal partners, including SRC family and c-KIT tyrosine kinases as well as glucose, glutamine, and serine metabolic pathways. These insights enable the design of combination therapies that select against resistance evolution.

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Section: Discussionsupporting

confidence: 91%

Melanoma Therapeutic Strategies that Select against Resistance by Exploiting MYC-Driven Evolutionary Convergence

et al. 2017

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“…To develop a rigorous approach for identifying promising drug combinations the Sander's group has used a combination of experimental data and computational modeling to predict active combination therapy regimes [46]. This approach uses a relatively simple interaction based model to predict cell killing and arrest in response to inhibition of various nodes in a model corresponding to proteins or interactions.…”

Section: Towards a Model Based Unification Of Genomic And Dynamic Datmentioning

confidence: 99%

Integrating genomic information and signaling dynamics for efficient cancer therapy

Stewart-Ornstein

Lahav

2017

Current Opinion in Systems Biology

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The field of cancer systems biology has made great strides in understanding oncogenic pathway signaling and enumerating mutations involved in oncogenesis. However, application of these datasets to patient stratification, and to the design of personalized therapy, is in its infancy. We review BRAF and BRCA mutant targeted therapy, where patient stratification has had critical, albeit mixed success. We contrast the work on genomic targeted therapy with orthogonal studies on the dynamics of signaling pathways for designing optimal treatment schedules. We suggest that an integrated approach, combining genomic data and the dynamics of signaling pathways, is required for developing pathway specific computational models and for systematic deployment of targeted combination regimes. The field of cancer systems biology has made great strides in understanding oncogenic pathway signaling and enumerating mutations involved in oncogenesis. However, application of the existing approaches and datasets to patient stratification, and to the design of personalized therapy, is in its infancy. Here we discuss an integrated approach combining genomic data and the dynamics of signaling pathway to develop pathway specific computational models and the systematic deployment of targeted combination regimes. We review recent studies and existing datasets in the field of cancer systems biology and highlight potentially fruitful synergies between the different strands of this discipline.

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“…Combinations of small-molecule inhibitors or biologics that target signaling receptors can be analyzed through mechanistic models of the downstream signaling networks as, for example, logic circuits, causal networks, or differential equations describing the underlying biochemical reactions [79–83]. However, if we want to consider a combination of a small-molecule inhibitor that targets a kinase and a drug that affects metabolism or gene regulation, we would need integrated models of both molecular layers.…”

Section: Which Computational Approaches Can Identify These Multiscalementioning

confidence: 99%

Looking beyond the cancer cell for effective drug combinations

2016

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Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer’s pervasive resistance to treatment. Research efforts to discover effective combination treatments have focused on drugs targeting intracellular processes of the cancer cells and in particular on small molecules that target aberrant kinases. Accordingly, most of the computational methods used to study, predict, and develop drug combinations concentrate on these modes of action and signaling processes within the cancer cell. This focus on the cancer cell overlooks significant opportunities to tackle other components of tumor biology that may offer greater potential for improving patient survival. Many alternative strategies have been developed to combat cancer; for example, targeting different cancer cellular processes such as epigenetic control; modulating stromal cells that interact with the tumor; strengthening physical barriers that confine tumor growth; boosting the immune system to attack tumor cells; and even regulating the microbiome to support antitumor responses. We suggest that to fully exploit these treatment modalities using effective drug combinations it is necessary to develop multiscale computational approaches that take into account the full complexity underlying the biology of a tumor, its microenvironment, and a patient’s response to the drugs. In this Opinion article, we discuss preliminary work in this area and the needs—in terms of both computational and data requirements—that will truly empower such combinations.

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Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells

Cited by 112 publications

References 66 publications

Melanoma Therapeutic Strategies that Select against Resistance by Exploiting MYC-Driven Evolutionary Convergence

Melanoma Therapeutic Strategies that Select against Resistance by Exploiting MYC-Driven Evolutionary Convergence

Integrating genomic information and signaling dynamics for efficient cancer therapy

Looking beyond the cancer cell for effective drug combinations

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