Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. / Ianevski, Aleksandr; Lahtela, Jenni; Javarappa, Komal K.; Sergeev, Philipp; Ghimire, Bishwa R.; Gautam, Prson; Vähä-Koskela, Markus; Turunen, Laura; Linnavirta, Nora; Kuusanmäki, Heikki; Kontro, Mika; Porkka, Kimmo; Heckman, Caroline A.; Mattila, Pirkko; Wennerberg, Krister; Giri, Anil K.; Aittokallio, Tero.

In: Science Advances, Vol. 7, No. 8, eabe4038, 2021.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Ianevski, A, Lahtela, J, Javarappa, KK, Sergeev, P, Ghimire, BR, Gautam, P, Vähä-Koskela, M, Turunen, L, Linnavirta, N, Kuusanmäki, H, Kontro, M, Porkka, K, Heckman, CA, Mattila, P, Wennerberg, K, Giri, AK & Aittokallio, T 2021, 'Patient-tailored design for selective co-inhibition of leukemic cell subpopulations', Science Advances, vol. 7, no. 8, eabe4038. https://doi.org/10.1126/sciadv.abe4038

APA

Ianevski, A., Lahtela, J., Javarappa, K. K., Sergeev, P., Ghimire, B. R., Gautam, P., Vähä-Koskela, M., Turunen, L., Linnavirta, N., Kuusanmäki, H., Kontro, M., Porkka, K., Heckman, C. A., Mattila, P., Wennerberg, K., Giri, A. K., & Aittokallio, T. (2021). Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Science Advances, 7(8), [eabe4038]. https://doi.org/10.1126/sciadv.abe4038

Vancouver

Ianevski A, Lahtela J, Javarappa KK, Sergeev P, Ghimire BR, Gautam P et al. Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Science Advances. 2021;7(8). eabe4038. https://doi.org/10.1126/sciadv.abe4038

Author

Ianevski, Aleksandr ; Lahtela, Jenni ; Javarappa, Komal K. ; Sergeev, Philipp ; Ghimire, Bishwa R. ; Gautam, Prson ; Vähä-Koskela, Markus ; Turunen, Laura ; Linnavirta, Nora ; Kuusanmäki, Heikki ; Kontro, Mika ; Porkka, Kimmo ; Heckman, Caroline A. ; Mattila, Pirkko ; Wennerberg, Krister ; Giri, Anil K. ; Aittokallio, Tero. / Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. In: Science Advances. 2021 ; Vol. 7, No. 8.

Bibtex

@article{c2ffa8210d054794a0d3fd832be25f31,
title = "Patient-tailored design for selective co-inhibition of leukemic cell subpopulations",
abstract = "The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.",
author = "Aleksandr Ianevski and Jenni Lahtela and Javarappa, {Komal K.} and Philipp Sergeev and Ghimire, {Bishwa R.} and Prson Gautam and Markus V{\"a}h{\"a}-Koskela and Laura Turunen and Nora Linnavirta and Heikki Kuusanm{\"a}ki and Mika Kontro and Kimmo Porkka and Heckman, {Caroline A.} and Pirkko Mattila and Krister Wennerberg and Giri, {Anil K.} and Tero Aittokallio",
year = "2021",
doi = "10.1126/sciadv.abe4038",
language = "English",
volume = "7",
journal = "Science advances",
issn = "2375-2548",
publisher = "American Association for the Advancement of Science",
number = "8",

}

RIS

TY - JOUR

T1 - Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

AU - Ianevski, Aleksandr

AU - Lahtela, Jenni

AU - Javarappa, Komal K.

AU - Sergeev, Philipp

AU - Ghimire, Bishwa R.

AU - Gautam, Prson

AU - Vähä-Koskela, Markus

AU - Turunen, Laura

AU - Linnavirta, Nora

AU - Kuusanmäki, Heikki

AU - Kontro, Mika

AU - Porkka, Kimmo

AU - Heckman, Caroline A.

AU - Mattila, Pirkko

AU - Wennerberg, Krister

AU - Giri, Anil K.

AU - Aittokallio, Tero

PY - 2021

Y1 - 2021

N2 - The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.

AB - The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.

U2 - 10.1126/sciadv.abe4038

DO - 10.1126/sciadv.abe4038

M3 - Journal article

C2 - 33608276

AN - SCOPUS:85101307422

VL - 7

JO - Science advances

JF - Science advances

SN - 2375-2548

IS - 8

M1 - eabe4038

ER -

ID: 257744067