TY - JOUR

T1 - Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer

AU - He, Liye

AU - Bulanova, Daria

AU - Oikkonen, Jaana

AU - Häkkinen, Antti

AU - Zhang, Kaiyang

AU - Zheng, Shuyu

AU - Wang, Wenyu

AU - Erkan, Erdogan Pekcan

AU - Carpén, Olli

AU - Joutsiniemi, Titta

AU - Hietanen, Sakari

AU - Hynninen, Johanna

AU - Huhtinen, Kaisa

AU - Hautaniemi, Sampsa

AU - Vähärautio, Anna

AU - Tang, Jing

AU - Wennerberg, Krister

AU - Aittokallio, Tero

N1 - Publisher Copyright: © The Author(s) 2021. Published by Oxford University Press.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - combination synergy

KW - drug combinations

KW - machine learning

KW - network visualization

KW - ovarian cancer

KW - precision oncology

KW - toxic effects

U2 - 10.1093/bib/bbab272

DO - 10.1093/bib/bbab272

M3 - Journal article

C2 - 34343245

AN - SCOPUS:85121952988

VL - 22

JO - Briefings in Bioinformatics

JF - Briefings in Bioinformatics

SN - 1467-5463

IS - 6

ER -