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

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...

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Autores principales: He, Liye, Bulanova, Daria, Oikkonen, Jaana, Häkkinen, Antti, Zhang, Kaiyang, Zheng, Shuyu, Wang, Wenyu, Erkan, Erdogan Pekcan, Carpén, Olli, Joutsiniemi, Titta, Hietanen, Sakari, Hynninen, Johanna, Huhtinen, Kaisa, Hautaniemi, Sampsa, Vähärautio, Anna, Tang, Jing, Wennerberg, Krister, Aittokallio, Tero
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8574973/
https://www.ncbi.nlm.nih.gov/pubmed/34343245
http://dx.doi.org/10.1093/bib/bbab272
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author He, Liye
Bulanova, Daria
Oikkonen, Jaana
Häkkinen, Antti
Zhang, Kaiyang
Zheng, Shuyu
Wang, Wenyu
Erkan, Erdogan Pekcan
Carpén, Olli
Joutsiniemi, Titta
Hietanen, Sakari
Hynninen, Johanna
Huhtinen, Kaisa
Hautaniemi, Sampsa
Vähärautio, Anna
Tang, Jing
Wennerberg, Krister
Aittokallio, Tero
author_facet He, Liye
Bulanova, Daria
Oikkonen, Jaana
Häkkinen, Antti
Zhang, Kaiyang
Zheng, Shuyu
Wang, Wenyu
Erkan, Erdogan Pekcan
Carpén, Olli
Joutsiniemi, Titta
Hietanen, Sakari
Hynninen, Johanna
Huhtinen, Kaisa
Hautaniemi, Sampsa
Vähärautio, Anna
Tang, Jing
Wennerberg, Krister
Aittokallio, Tero
author_sort He, Liye
collection PubMed
description 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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spelling pubmed-85749732021-11-09 Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer He, Liye Bulanova, Daria Oikkonen, Jaana Häkkinen, Antti Zhang, Kaiyang Zheng, Shuyu Wang, Wenyu Erkan, Erdogan Pekcan Carpén, Olli Joutsiniemi, Titta Hietanen, Sakari Hynninen, Johanna Huhtinen, Kaisa Hautaniemi, Sampsa Vähärautio, Anna Tang, Jing Wennerberg, Krister Aittokallio, Tero Brief Bioinform Case Study 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. Oxford University Press 2021-08-03 /pmc/articles/PMC8574973/ /pubmed/34343245 http://dx.doi.org/10.1093/bib/bbab272 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Case Study
He, Liye
Bulanova, Daria
Oikkonen, Jaana
Häkkinen, Antti
Zhang, Kaiyang
Zheng, Shuyu
Wang, Wenyu
Erkan, Erdogan Pekcan
Carpén, Olli
Joutsiniemi, Titta
Hietanen, Sakari
Hynninen, Johanna
Huhtinen, Kaisa
Hautaniemi, Sampsa
Vähärautio, Anna
Tang, Jing
Wennerberg, Krister
Aittokallio, Tero
Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer
title Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer
title_full Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer
title_fullStr Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer
title_full_unstemmed Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer
title_short Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer
title_sort network-guided identification of cancer-selective combinatorial therapies in ovarian cancer
topic Case Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8574973/
https://www.ncbi.nlm.nih.gov/pubmed/34343245
http://dx.doi.org/10.1093/bib/bbab272
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