Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia
Open accessPublished version of a preprint
Abstract
Abstract We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine. Significance: Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB. See related commentary by Letai, p. 290. This article is highlighted in the In This Issue feature, p. 275
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
High-throughput ex vivo drug screening is a powerful tool to identify effective compounds and drug repurposing candidates.
“High-throughput ex vivo drug screening is a powerful tool to identify effective compounds and drug repurposing candidates,10,11”
From Integrated drug profiling and CRISPR screening identify BCR::ABL1-independent vulnerabilities in chronic myeloid leukemia (2024) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 50 · Cited by 193
Research works printed but not linked: 1
- [43]McMahon CM, Canaani J, Rea B, Sargent RL, Morrissette JJD, Lieberman DB. Mechanisms of acquired resistance to gilteritinib therapy in relapsed and refractory FLT3-mutated acute myeloid leukemia. Blood 2017; 130: 295.
- Authors
- Malani, D., Kumar, A., Brück, O., Kontro, M., Yadav, B., Hellesøy, M., Kuusanmäki, H., Dufva, O., Kankainen, M., Eldfors, S., Potdar, S., Saarela, J., Turunen, L., Parsons, A., Västrik, I., Kivinen, K., Saarela, J., Räty, R., Lehto, M., Wolf, M., Gjertsen, B. T., Mustjoki, S., Aittokallio, T., Wennerberg, K., Heckman, C. A., Kallioniemi, O., & Porkka, K.
- Affiliations
- University of Helsinki (FI)
- Haukeland University Hospital (NO)
- University of Copenhagen (DK)
- 5Department of Medicine, Hematology Section, Haukeland University Hospital, Bergen, Norway.
- Oslo University Hospital (NO)
- Corresponding author
- Kallioniemi, Olli
- Porkka, Kimmo
- ORCID
- Malani, Disha
- Kumar, Ashwini
- Brück, Oscar
- Kontro, Mika
- Yadav, Bhagwan
- Hellesøy, Monica
- Kuusanmäki, Heikki
- Dufva, Olli
- Kankainen, Matti
- Eldfors, Samuli
- Potdar, Swapnil
- Saarela, Jani
- Turunen, Laura
- Västrik, Imre
- Kivinen, Katja
- Saarela, Janna
- Räty, Riikka
- Lehto, Minna
- Wolf, Maija
- Mustjoki, Satu
- Aittokallio, Tero
- Wennerberg, Krister
- Heckman, Caroline A.
- Kallioniemi, Olli
- Porkka, Kimmo
- Venue
- Cancer Discovery
- Published
- 2021-11-17
- Volume
- 12
- Issue
- 2
- Pages
- 388-401
- ISSN
- 2159-8274 (ISSN-L)
- 2159-8290
- PMC
- PMC9762335
- OpenAlex
- W3214427435
- Cited by
- 193
- References
- 50
- Citations by year
- 2026: 36 · 2025: 43 · 2024: 46 · 2023: 40 · 2022: 26 · 2021: 2
- FWCI
- 18.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cancer Discovery (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc-nd)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc-nd)
- Työväentutkimus Vuosikirja (submittedVersion)
- Funders
- Novo Nordisk Foundation Center for Basic Metabolic Research (NNF17CC0027852)
- Academy of Finland (292611, 310507 and 313267, 1320185 and 334781, 259777, 278741, 271845 and 333050)
- Cancer Society of Finland (120089, 140114, 160080, 170112 and 190116)
- VINNOVA (2018-03338)
- Topics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Hematology › Chronic Myeloid Leukemia Treatments
- Keywords
- acute myeloid leukemia, functional precision medicine, multidisciplinary tumor board, relapsed/refractory AML, allogeneic hematopoietic stem cell transplantation, FLT3 inhibitor resistance, drug response biomarkers, multi-omics integration, personalized treatment
- MeSH terms
- Decision Support Techniques, Female, Finland, Humans, Male, Middle Aged, Patient Care Team, Remission Induction, Leukemia, Myeloid, Acute, Survival Analysis, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review