Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

Bhagwan Yadav; Tea Pemovska; Agnieszka Szwajda; Evgeny Kulesskiy; Mika Kontro; Riikka Karjalainen; Muntasir Mamun Majumder; Disha Malani; Astrid Murumägi; Jonathan Knowles; Kimmo Porkka; Caroline Heckman; Olli Kallioniemi; Krister Wennerberg; Tero Aittokallio

doi:10.1038/srep05193

Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

Bhagwan Yadav, Tea Pemovska, Agnieszka Szwajda, Evgeny Kulesskiy, Mika Kontro, Riikka Karjalainen, Muntasir Mamun Majumder, Disha Malani, Astrid Murumägi, Jonathan Knowles, Kimmo Porkka, Caroline Heckman, Olli Kallioniemi, Krister Wennerberg, Tero Aittokallio

118 Citations (Scopus)

Abstract

We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. Mathematical model estimation and continuous interpolation makes the scoring approach robust against sources of technical variability and widely applicable to various experimental settings, both in cancer cell line models and primary patient-derived cells. Here, we demonstrate its improved performance over other response parameters especially in a leukemia patient case study, where differential DSS between patient and control cells enabled identification of both cancer-selective drugs and drug-sensitive patient sub-groups, as well as dynamic monitoring of the response patterns and oncogenic driver signals during cancer progression and relapse in individual patient cells ex vivo. An open-source and easily extendable implementation of the DSS calculation is made freely available to support its tailored application to translating drug sensitivity testing results into clinically actionable treatment options.

Original language	English
Journal	Scientific Reports
Volume	4
Pages (from-to)	5193
ISSN	2045-2322
DOIs	https://doi.org/10.1038/srep05193
Publication status	Published - 5 Jun 2014
Externally published	Yes

Keywords

Algorithms
Antineoplastic Agents/pharmacology
Case-Control Studies
Drug Resistance, Neoplasm/drug effects
Humans
Leukemia, Myeloid, Acute/drug therapy
Models, Theoretical
Neoplasm Recurrence, Local/drug therapy
Precision Medicine
Tumor Cells, Cultured

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Yadav, B., Pemovska, T., Szwajda, A., Kulesskiy, E., Kontro, M., Karjalainen, R., Majumder, M. M., Malani, D., Murumägi, A., Knowles, J., Porkka, K., Heckman, C., Kallioniemi, O., Wennerberg, K., & Aittokallio, T. (2014). Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports, 4, 5193. https://doi.org/10.1038/srep05193

Yadav, B, Pemovska, T, Szwajda, A, Kulesskiy, E, Kontro, M, Karjalainen, R, Majumder, MM, Malani, D, Murumägi, A, Knowles, J, Porkka, K, Heckman, C, Kallioniemi, O, Wennerberg, K & Aittokallio, T 2014, 'Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies', Scientific Reports, vol. 4, pp. 5193. https://doi.org/10.1038/srep05193

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title = "Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies",

abstract = "We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. Mathematical model estimation and continuous interpolation makes the scoring approach robust against sources of technical variability and widely applicable to various experimental settings, both in cancer cell line models and primary patient-derived cells. Here, we demonstrate its improved performance over other response parameters especially in a leukemia patient case study, where differential DSS between patient and control cells enabled identification of both cancer-selective drugs and drug-sensitive patient sub-groups, as well as dynamic monitoring of the response patterns and oncogenic driver signals during cancer progression and relapse in individual patient cells ex vivo. An open-source and easily extendable implementation of the DSS calculation is made freely available to support its tailored application to translating drug sensitivity testing results into clinically actionable treatment options. ",

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doi = "10.1038/srep05193",

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AU - Yadav, Bhagwan

AU - Pemovska, Tea

AU - Szwajda, Agnieszka

AU - Kulesskiy, Evgeny

AU - Kontro, Mika

AU - Karjalainen, Riikka

AU - Majumder, Muntasir Mamun

AU - Malani, Disha

AU - Murumägi, Astrid

AU - Knowles, Jonathan

AU - Porkka, Kimmo

AU - Heckman, Caroline

AU - Kallioniemi, Olli

AU - Wennerberg, Krister

AU - Aittokallio, Tero

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AB - We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. Mathematical model estimation and continuous interpolation makes the scoring approach robust against sources of technical variability and widely applicable to various experimental settings, both in cancer cell line models and primary patient-derived cells. Here, we demonstrate its improved performance over other response parameters especially in a leukemia patient case study, where differential DSS between patient and control cells enabled identification of both cancer-selective drugs and drug-sensitive patient sub-groups, as well as dynamic monitoring of the response patterns and oncogenic driver signals during cancer progression and relapse in individual patient cells ex vivo. An open-source and easily extendable implementation of the DSS calculation is made freely available to support its tailored application to translating drug sensitivity testing results into clinically actionable treatment options.

KW - Algorithms

KW - Antineoplastic Agents/pharmacology

KW - Case-Control Studies

KW - Drug Resistance, Neoplasm/drug effects

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KW - Leukemia, Myeloid, Acute/drug therapy

KW - Models, Theoretical

KW - Neoplasm Recurrence, Local/drug therapy

KW - Precision Medicine

KW - Tumor Cells, Cultured

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DO - 10.1038/srep05193

M3 - Journal article

C2 - 24898935

SN - 2045-2322

VL - 4

SP - 5193

JO - Scientific Reports

JF - Scientific Reports

ER -

Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

Abstract

Keywords

UN SDGs

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