Robust scoring of selective drug responses for patient-tailored therapy selection.
Journal
Nature protocols
ISSN: 1750-2799
Titre abrégé: Nat Protoc
Pays: England
ID NLM: 101284307
Informations de publication
Date de publication:
23 Nov 2023
23 Nov 2023
Historique:
received:
24
03
2023
accepted:
10
08
2023
medline:
24
11
2023
pubmed:
24
11
2023
entrez:
23
11
2023
Statut:
aheadofprint
Résumé
Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.
Identifiants
pubmed: 37996540
doi: 10.1038/s41596-023-00903-x
pii: 10.1038/s41596-023-00903-x
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
ID : 101057442
Organisme : Academy of Finland (Suomen Akatemia)
ID : 310507
Organisme : Academy of Finland (Suomen Akatemia)
ID : 313267
Organisme : Academy of Finland (Suomen Akatemia)
ID : 313267
Organisme : Academy of Finland (Suomen Akatemia)
ID : 326238
Organisme : Academy of Finland (Suomen Akatemia)
ID : 340141
Organisme : Academy of Finland (Suomen Akatemia)
ID : 344698
Organisme : Academy of Finland (Suomen Akatemia)
ID : 345803
Organisme : Academy of Finland (Suomen Akatemia)
ID : 336666
Organisme : Academy of Finland (Suomen Akatemia)
ID : 326588
Organisme : Academy of Finland (Suomen Akatemia)
ID : 312413
Organisme : Academy of Finland (Suomen Akatemia)
ID : 353177
Organisme : Academy of Finland (Suomen Akatemia)
ID : 320185
Organisme : Academy of Finland (Suomen Akatemia)
ID : 334781
Organisme : Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
ID : 2020026
Organisme : Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
ID : 2017064
Organisme : Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
ID : 2018012
Organisme : Ministry of Health and Care Services | Helse Sør-Øst RHF (Southern and Eastern Norway Regional Health Authority)
ID : 2019096
Organisme : NordForsk
ID : 96782
Organisme : Kreftforeningen (Norwegian Cancer Society)
ID : 182524
Organisme : Kreftforeningen (Norwegian Cancer Society)
ID : 208012
Organisme : Norges Forskningsråd (Research Council of Norway)
ID : 262652
Informations de copyright
© 2023. Springer Nature Limited.
Références
Kornauth, C. et al. Functional precision medicine provides clinical benefit in advanced aggressive hematologic cancers and identifies exceptional responders. Cancer Discov. 12, 372–387 (2022).
doi: 10.1158/2159-8290.CD-21-0538
pubmed: 34635570
Malani, D. et al. Implementing a functional precision medicine tumor board for acute myeloid leukemia. Cancer Discov. 12, 388–401 (2022).
doi: 10.1158/2159-8290.CD-21-0410
pubmed: 34789538
Letai, A., Bhola, P. & Welm, A. L. Functional precision oncology: testing tumors with drugs to identify vulnerabilities and novel combinations. Cancer Cell 40, 26–35 (2022).
doi: 10.1016/j.ccell.2021.12.004
pubmed: 34951956
Tognon, C. E., Sears, R. C., Mills, G. B., Gray, J. W. & Tyner, J. W. Ex vivo analysis of primary tumor specimens for evaluation of cancer therapeutics. Annu. Rev. Cancer Biol. 5, 39–57 (2021).
doi: 10.1146/annurev-cancerbio-043020-125955
pubmed: 34222745
Flobak, Å., Skånland, S. S., Hovig, E., Taskén, K. & Russnes, H. G. Functional precision cancer medicine: drug sensitivity screening enabled by cell culture models. Trends Pharmacol. Sci. 43, 973–985 (2022).
doi: 10.1016/j.tips.2022.08.009
pubmed: 36163057
Pemovska, T. et al. Axitinib effectively inhibits BCR-ABL1(T315I) with a distinct binding conformation. Nature 519, 102–105 (2015).
doi: 10.1038/nature14119
pubmed: 25686603
Hatzis, C. et al. Enhancing reproducibility in cancer drug screening: how do we move forward? Cancer Res. 74, 4016–4023 (2014).
doi: 10.1158/0008-5472.CAN-14-0725
pubmed: 25015668
pmcid: 4119520
Haibe-Kains, B. et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013).
doi: 10.1038/nature12831
pubmed: 24284626
pmcid: 4237165
Yadav, B. et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci. Rep. 4, 5193 (2014).
doi: 10.1038/srep05193
pubmed: 24898935
pmcid: 4046135
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
doi: 10.1038/nature11003
pubmed: 22460905
pmcid: 3320027
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).
doi: 10.1038/nature11005
pubmed: 22460902
pmcid: 3349233
Mpindi, J. P. et al. Consistency in drug response profiling. Nature 540, E5–E6 (2016).
doi: 10.1038/nature20171
pubmed: 27905421
Pemovska, T. et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov. 3, 1416–1429 (2013).
doi: 10.1158/2159-8290.CD-13-0350
pubmed: 24056683
Yin, Y. et al. Functional testing to characterize and stratify PI3K inhibitor responses in chronic lymphocytic leukemia. Clin. Cancer Res. 28, 4444–4455 (2022).
doi: 10.1158/1078-0432.CCR-22-1221
pubmed: 35998013
pmcid: 9588626
Andersen, A. N. et al. Clinical forecasting using ex vivo drug sensitivity profiling of acute myeloid leukemia. Preprint at https://www.biorxiv.org/content/10.1101/2022.10.11.509866v2 (2023).
Bottomly, D. et al. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell 40, 850–864.e9 (2022).
doi: 10.1016/j.ccell.2022.07.002
pubmed: 35868306
pmcid: 9378589
Potdar, S. et al. Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data. Nucleic Acids Res. 51, W57–W61 (2023).
doi: 10.1093/nar/gkad390
pubmed: 37178002
pmcid: 10320147
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).
doi: 10.1093/bioinformatics/bts034
pubmed: 22257669
pmcid: 3307112
Yamada, S. et al. Clinical relevance of in vitro chemoresistance in childhood acute myeloid leukemia. Leukemia 15, 1892–1897 (2001).
doi: 10.1038/sj.leu.2402305
pubmed: 11753610
Volm, M. & Efferth, T. Prediction of cancer drug resistance and implications for personalized medicine. Front. Oncol. 5, 282 (2015).
doi: 10.3389/fonc.2015.00282
pubmed: 26734568
pmcid: 4681783
Gupta, A., Gautam, P., Wennerberg, K. & Aittokallio, T. A normalized drug response metric improves accuracy and consistency of anticancer drug sensitivity quantification in cell-based screening. Commun. Biol. 3, 42 (2020).
doi: 10.1038/s42003-020-0765-z
pubmed: 31974521
pmcid: 6978361
Hafner, M., Niepel, M., Chung, M. & Sorger, P. K. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat. Methods 13, 521–527 (2016).
doi: 10.1038/nmeth.3853
pubmed: 27135972
pmcid: 4887336
Murumagi, A. et al. Drug response profiles in patient-derived cancer cells across histological subtypes of ovarian cancer: real-time therapy tailoring for a patient with low-grade serous carcinoma. Br. J. Cancer 128, 678–690 (2023).
doi: 10.1038/s41416-022-02067-z
pubmed: 36476658
Heinemann, T. et al. Deep morphology learning enhances ex vivo drug profiling-based precision medicine. Blood Cancer Discov. 3, 502–515 (2022).
doi: 10.1158/2643-3230.BCD-21-0219
pubmed: 36125297
pmcid: 9894727
Kropivsek, K. et al. Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma. Nat. Cancer 4, 734–753 (2023).
doi: 10.1038/s43018-023-00544-9
pubmed: 37081258
pmcid: 10212768
Kuusanmäki, H. et al. Phenotype-based drug screening reveals association between venetoclax response and differentiation stage in acute myeloid leukemia. Haematologica 105, 708–720 (2020).
doi: 10.3324/haematol.2018.214882
pubmed: 31296572
pmcid: 7049363
Ianevski, A. et al. Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Sci. Adv. 7, eabe4038 (2021).
doi: 10.1126/sciadv.abe4038
pubmed: 33608276
pmcid: 7895436
Goh, J. et al. An ex vivo platform to guide drug combination treatment in relapsed/refractory lymphoma. Sci. Transl. Med. 14, eabn7824 (2022).
doi: 10.1126/scitranslmed.abn7824
pubmed: 36260690
He, L. et al. Patient-customized drug combination prediction and testing for T-cell prolymphocytic leukemia patients. Cancer Res. 78, 2407–2418 (2018).
doi: 10.1158/0008-5472.CAN-17-3644
pubmed: 29483097
He, L. et al. Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer. Brief. Bioinform. 22, bbab272 (2021).
doi: 10.1093/bib/bbab272
pubmed: 34343245
pmcid: 8574973
Hanes, R. et al. screenwerk: a modular tool for the design and analysis of drug combination screens. Bioinformatics 39, btac840 (2023).
doi: 10.1093/bioinformatics/btac840
pubmed: 36573326
Ritz, C., Baty, F., Streibig, J. C. & Gerhard, D. Dose-response analysis using R. PLoS One 10, e0146021 (2015).
doi: 10.1371/journal.pone.0146021
pubmed: 26717316
pmcid: 4696819
Tipping, M. E. & Bishop, C. M. Probabilistic principal component analysis. J. R. Stat. Soc. Ser. B Stat. Methodol. 61, 611–622 (1999).
doi: 10.1111/1467-9868.00196
Lee, S. H. R. et al. Pharmacotypes across the genomic landscape of pediatric acute lymphoblastic leukemia and impact on treatment response. Nat. Med. 29, 170–179 (2023).
doi: 10.1038/s41591-022-02112-7
pubmed: 36604538
pmcid: 9873558
Kuusanmäki, H. et al. Ex vivo venetoclax sensitivity testing predicts treatment response in acute myeloid leukemia. Haematologica 108, 1768–1781 (2023).
doi: 10.3324/haematol.2022.281692
pubmed: 36519325
Majumder, M. M. et al. Identification of precision treatment strategies for relapsed/refractory multiple myeloma by functional drug sensitivity testing. Oncotarget 8, 56338–56350 (2017).
doi: 10.18632/oncotarget.17630
pubmed: 28915594
pmcid: 5593565
Pearson, K. LIII. On lines and planes of closest fit to systems of points in space. The Lond., Edinb. Dublin Philos. Mag. J. Sci. 2, 559–572 (1901).
doi: 10.1080/14786440109462720