Multiobjective Optimization Identifies Cancer-Selective Combination Therapies

Pulkkinen, Otto; Gautam, Prson; Mustonen, Ville; Aittokallio, Tero

doi:10.1101/2020.04.10.035584

Cited by 1 publication

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References 32 publications

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“…However, most preclinical screening efforts emphasize merely the combination synergy as key determinant of the drug combination performance [ 7 ], even though cancer cell selectivity is critical for the clinical success of combinatorial therapies [ 8 ]. This leads to the translational challenge that requires careful assessment of potential toxic effects along with synergistic efficacy, as there is a fundamental trade-off between treatment efficacy and tolerable toxicity [ 9 ]. To date, there has been a lack of computational approaches that could address these experimental and translational challenges: (i) identifying among the massive number of potential drug combinations those that simultaneously show both maximal therapeutic potential and cancer selectivity, and (ii) bridging the gap to the clinical practice to enable real-world applications in translational studies and to establish their potential utility in clinical decision-making process.…”

Section: Introductionmentioning

confidence: 99%

Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer

Bulanova

Oikkonen

et al. 2021

Briefings in Bioinformatics

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Each patient’s cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

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Section: Introductionmentioning

confidence: 99%