DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity

Anchang, Benedict; Davis, Kara L.; Fienberg, Harris G.; Williamson, Brian D.; Bendall, Sean C.; Karacosta, Loukia Georgiou; Tibshirani, Robert; Nolan, Garry P.; Plevritis, Sylvia K.

doi:10.1073/pnas.1711365115

Cited by 41 publications

(26 citation statements)

References 38 publications

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“…This technique provides detailed expression data for multiple signaling partners and markers in a single experiment, enabling profiling of drug-resistant cell lines in a higher throughput, multiplexed manner. Other groups have utilized CyTOF to study tumor samples to better understand drug responses (32,33), the roles of stem cell populations (34), and in the study of combination therapy (35). CyTOF has also been used to study drug resistance in acute myeloid leukemia (36) and glioblastoma (37).…”

Section: Introductionmentioning

confidence: 99%

The FGFR1 V561M Gatekeeper Mutation Drives AZD4547 Resistance through STAT3 Activation and EMT

Ryan

Sohl

Luo

et al. 2019

Molecular Cancer Research

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FGFR1 has been implicated in numerous cancer types including squamous cell lung cancer, a subset of non-small cell lung cancer with a dismal 5-year survival rate. Smallmolecule inhibitors targeting FGFR1 are currently in clinical trials, with AZD4547 being one of the furthest along; however, the development of drug resistance is a major challenge for targeted therapies. A prevalent mechanism of drug resistance in kinases occurs through mutation of the gatekeeper residue, V561M in FGFR1; however, mechanisms underlying V561M resistance to AZD4547 are not fully understood. Here, the cellular consequences of the V561M gatekeeper mutation were characterized, and it was found that although AZD4547 maintains nanomolar affinity for V561M FGFR1, based on in vitro binding assays, cells expressing V561M demonstrate dramatic resistance to AZD4547 driven by increased STAT3 activation downstream of V561M FGFR1. The data reveal that the V561M mutation biases cells toward a more mesenchymal phenotype, including increased levels of proliferation, migration, invasion, and anchorage-independent growth, which was confirmed using CyTOF, a novel single-cell analysis tool. Using shRNA knockdown, loss of STAT3 restored sensitivity of cancer cells expressing V561M FGFR1 to AZD4547. Thus, the data demonstrate that combination therapies including FGFR and STAT3 may overcome V561M FGFR1-driven drug resistance in the clinic.Implications: The V561M FGFR1 gatekeeper mutation leads to devastating drug resistance through activation of STAT3 and the epithelial-mesenchymal transition; this study demonstrates that FGFR1 inhibitor sensitivity can be restored upon STAT3 knockdown.

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Section: Introductionmentioning

confidence: 99%

The FGFR1 V561M Gatekeeper Mutation Drives AZD4547 Resistance through STAT3 Activation and EMT

Ryan

Sohl

Luo

et al. 2019

Molecular Cancer Research

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“…Even though scRNA-seq enables efficient mapping of primary AML cell populations at baseline 23 , when used alone, it lacks the possibility to profile functional responses of drug treatments. Another recent study utilized nested effects modeling combined with Mass Cytometry Time-of-Flight (CyTOF) to simultaneously profile single agent responses and to catalogue signaling heterogeneity at the single-cell level, with the aim to suggest drug combinations that lead to maximal desired intracellular effects 50 . However, this method requires CyTOF profiling before and after the drug treatment, hence limiting its predictive clinical value.…”

Section: Discussionmentioning

confidence: 99%

Patient-tailored design of AML cell subpopulation-selective drug combinations

Ianevski

Lahtela

Javarappa

et al. 2020

Preprint

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The extensive primary and secondary drug resistance in acute myeloid leukemia (AML) requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to optimally target disease-driving AML cell subpopulations. However, the large number of AML-relevant drug combinations makes the testing impossible in scarce primary patient cells. This combinatorial problem is further exacerbated by the translational challenge of how to design such personalized and selective drug combinations that do not only show synergistic effect in overall AML cell killing but also result in minimal toxic side effects on non-malignant cells. To solve these challenges, we implemented a systematic computational-experimental approach for identifying potential drug combinations that have a desired synergy-efficacy-toxicity balance. Our mechanism-agnostic approach combines single-cell RNA-sequencing (scRNA-seq) with ex vivo single-agent viability testing in primary patient cells. The data integration and predictive modelling are carried out at a single-cell resolution by means of a machine learning model that makes use of compound-target interaction networks to narrow down the massive search space of potentially effective drug combinations. When applied to two diagnostic and two refractory AML patient cases, each having a different genetic background, our integrated approach predicted a number of patient-specific combinations that were shown to result not only in synergistic cancer cell inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Overall, 53% of the 59 predicted combinations were experimentally confirmed to show synergy, and 83% were non-antagonistic, as validated with viability assays, which is a significant improvement over the success rate of randomly guessing a synergistic drug combination (5%). Importantly, 67% of the predicted combinations showed low toxicity to non-malignant cells, as validated with flow-based population assays, suggesting their selective killing of AML cell populations. Our data-driven approach provides an unbiased means for systematic prioritization of patient-specific drug combinations that selectively inhibit AML cells and avoid co-inhibition of non-malignant cells, thereby increasing their likelihood for clinical translation. The approach uses only a limited number of patient primary cells, and it is widely applicable to hematological cancers that are accessible for scRNA-seq profiling and ex vivo compound testing.

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“…178 The understanding of drugs and its relationships with the immune system confers information for treating diseases, minimizing unwanted side effects, facilitates individual-tailored medicine, and optimizes combination therapies. 179 In addition, we expect CyTOF to enter clinical laboratories and gradually play a dominant role in immune compartment profiling. By virtue of its capacity of comprehensive and systematic immune characterization, CyTOF would play an indispensable role in routine evaluation of global health.…”

Section: Perspectivementioning

confidence: 99%

Progress and applications of mass cytometry in sketching immune landscapes

Zhang

Warden

2020

Clinical & Translational Med

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Recently emerged mass cytometry (cytometry by time-of-flight [CyTOF]) technology permits the identification and quantification of inherently diverse cellular systems, and the simultaneous measurement of functional attributes at the single-cell resolution. By virtue of its multiplex ability with limited need for compensation, CyTOF has led a critical role in immunological research fields. Here, we present an overview of CyTOF, including the introduction of CyTOF principle and advantages that make it a standalone tool in deciphering immune mysteries. We then discuss the functional assays, introduce the bioinformatics to interpret the data yield via CyTOF, and depict the emerging clinical and research applications of CyTOF technology in sketching immune landscape in a wide variety of diseases.

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DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity

Cited by 41 publications

References 38 publications

The FGFR1 V561M Gatekeeper Mutation Drives AZD4547 Resistance through STAT3 Activation and EMT

The FGFR1 V561M Gatekeeper Mutation Drives AZD4547 Resistance through STAT3 Activation and EMT

Patient-tailored design of AML cell subpopulation-selective drug combinations

Progress and applications of mass cytometry in sketching immune landscapes

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