Cited by 207Acute Myeloid Leukemia ResearchChronic Lymphocytic Leukemia ResearchProtein Degradation and Inhibitors

The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.

Functional Precision Medicine Provides Clinical Benefit in Advanced Aggressive Hematologic Cancers and Identifies Exceptional Responders

Open access

Kornauth, Christoph, Pemovska, Tea, Vladimer, Gregory I., Bayer, Günther, Bergmann, Michael, Eder, Sandra, Eichner, Ruth, Erl, Martin, Esterbauer, Harald, Exner, Ruth, Felsleitner-Hauer, Verena, Forte, Maurizio, Gaiger, Alexander, Geissler, Klaus, Greinix, Hildegard T., Gstöttner, Wolfgang, Hacker, Marcus, Hartmann, Bernd Lorenz, Hauswirth, Alexander W., Heinemann, Tim, Heintel, Daniel, Hoda, Mir Alireza, Hopfinger, Georg, Jaeger, Ulrich, Kazianka, Lukas, Kenner, Lukas, Kiesewetter, Barbara, Krall, Nikolaus, Krajnik, Gerhard, Kubicek, Stefan, Le, Trang, Lubowitzki, Simone, Mayerhoefer, Marius E., Menschel, Elisabeth, Merkel, Olaf, Miura, Katsuhiro, Müllauer, Leonhard, Neumeister, Peter, Noesslinger, Thomas, Ocko, Katharina, Öhler, Leopold, Panny, Michael, Pichler, Alexander, Porpaczy, Edit, Prager, Gerald W., Raderer, Markus, Ristl, Robin, Ruckser, Reinhard, Salamon, Julius, Schiefer, Ana-Iris, Schmolke, Ann-Sofie, Schwarzinger, Ilse, Selzer, Edgar, Sillaber, Christian, Skrabs, Cathrin, Sperr, Wolfgang R., Srndic, Ismet, Thalhammer, Renate, Valent, Peter, van der Kouwe, Emiel, Vanura, Katrina, Vogt, Stefan, Waldstein, Cora, Wolf, Dominik, Zielinski, Christoph C., Zojer, Niklas, Simonitsch-Klupp, Ingrid, Superti-Furga, Giulio, Snijder, Berend, Staber, Philipp B.·2022·Cancer Discovery

DOI 10.1158/2159-8290.cd-21-0538PMC PMC9762339

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Abstract Personalized medicine aims to match the right drug with the right patient by using specific features of the individual patient's tumor. However, current strategies of personalized therapy matching provide treatment opportunities for less than 10% of patients with cancer. A promising method may be drug profiling of patient biopsy specimens with single-cell resolution to directly quantify drug effects. We prospectively tested an image-based single-cell functional precision medicine (scFPM) approach to guide treatments in 143 patients with advanced aggressive hematologic cancers. Fifty-six patients (39%) were treated according to scFPM results. At a median follow-up of 23.9 months, 30 patients (54%) demonstrated a clinical benefit of more than 1.3-fold enhanced progression-free survival compared with their previous therapy. Twelve patients (40% of responders) experienced exceptional responses lasting three times longer than expected for their respective disease. We conclude that therapy matching by scFPM is clinically feasible and effective in advanced aggressive hematologic cancers. Significance: This is the first precision medicine trial using a functional assay to instruct n-of-one therapies in oncology. It illustrates that for patients lacking standard therapies, high-content assay-based scFPM can have a significant value in clinical therapy guidance based on functional dependencies of each patient's cancer. 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

Crossref · 10.1158/2159-8290.cd-21-0538

Cites

43 of 43 references verified0 citation errors · 0 not checkable

Statistics

Figure 1. EXALT procedure and primary outcome measure. A, Viable cells from lymph node (LN), BM, or PB of patients with late-stage hematologic cancer were subjected to image-based scFPM. Target cells are identified by staining with fluorescent antibodies. Reports, automatically generated by the analysis pipeline, are discussed in a dedicated tumor board with patients treated accordingly. B, Our primary outcome measure was PFS ratio, defined as PFS(scFPM treatment)/PFS(previous treatment). A ratio of 1.3 is considered individually beneficial. DAPI, 4′,6-diamidino-2-phenylindole.CC BY-NC-ND · PubMed Central

Figure 2. CONSORT diagram of study patients.CC BY-NC-ND · PubMed Central

Figure 3. scFPM-guided treatment enhances PFS ratio in patients with advanced hematologic cancers and provides a survival benefit. A, Bar plot showing the PFS for all included, scFPM-guided patients: blue bars denote PFS in days for scFPM-guided treatment, red bars indicate last previous treatment, and asterisks denote ongoing response for scFPM treatment at the censoring date. PFS ratio is the following ratio: PFS(scFPM treatment)/PFS(previous treatment). Patient characteristics are color coded and stratified (leukemia vs. lymphoma, exceptional response vs. nonexceptional response, ECOG >1 vs. ECOG ≤1). B, Kaplan–Meier plot comparing PFS on scFPM-guided treatment with previous treatment in exceptional responders (n = 12). C, Bar plot showing PFS for all patients with an ECOG ≤1 (n = 39). Asterisks denote ongoing response for scFPM treatment at censoring date. D, Kaplan–Meier plot comparing PFS on scFPM treatment between patients with ECOG ≤1 (n = 39) versus ECOG>1 (n = 17). E, Bar plot showing PFS for all patients with OR on previous treatment. Asterisks denote ongoing response for scFPM treatment at censoring date. F, Kaplan–Meier plot comparing PFS on scFPM treatment stratified according to OR on last treatment (CR/PR: n = 27, SD/PD: n = 29). G, Scatter plot comparing PFS on last treatment to current treatment, for scFPM-guided versus physician's choice patients (paired Wilcoxon test). H, Kaplan–Meier plot comparing overall survival stratified according to scFPM-guided patients (n = 56) versus physician's choice patients (n = 20).CC BY-NC-ND · PubMed Central

Figure 4. Post hoc analysis. A, Kaplan–Meier plot comparing scFPM-matched treatment with previous treatment. Dotted line denotes 1-year follow-up. B, Kaplan–Meier plot comparing non–scFPM-matched treatment with previous treatment. C, Paired scatter plot comparing nonmatched versus matched patients with regard to PFS ratio. Paired Wilcoxon test comparing PFS of previous treatment versus scFPM-matched/nonmatched treatment [H0: rank PFS(previous) = rank PFS(current)]. D, Kaplan–Meier plot of scFPM-matched treatment stratified according to ECOG <1 versus ECOG ≥1. E, Kaplan–Meier plot of scFPM-matched treatment stratified according to response on previous treatment. F, Kaplan–Meier plots comparing PFS for scFPM-matched patients stratified according to tumor cell content in the sample (high ≥50%, medium >10%, low ≥10%).CC BY-NC-ND · PubMed Central

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel
Döhner, H., Estey, E. H., Grimwade, D. J., Amadori, S., Appelbaum, F. R., Büchner, T., Dombret, H., Ebert, B. L., Fenaux, P., Larson, R. A., et al. (2016) · Blood · doi:10.1182/blood-2016-08-733196 · Cited by 5,938 · cited 2×
“Response evaluation was based on RECIL criteria for lymphoma and ELN criteria for 468 leukemia (42,43).”
Method
Recurring mutations found by sequencing an acute myeloid leukemia genome
Mardis, E. R., Ding, L., Dooling, D. J., Larson, D. E., McLellan, M. D., Chen, K., Koboldt, D. C., Fulton, R. S., Delehaunty, K. D., McGrath, S. D., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0903840 · Cited by 2,219 · cited 1×
“The median time from sampling to scFPM report was 5 (1189 33) days, with longer times accounting for re-staining due to updated immunophenotyping 190 data, and to treatment 21 (4-77) days.”
Principles of cancer therapy: oncogene and non-oncogene addiction
Luo, J., Solimini, N. L., & Elledge, S. J. (2009) · Cell · doi:10.1016/j.cell.2009.02.024 · Cited by 1,765 · cited 2×
“The median time from sampling to scFPM report was 5 (1189 33) days, with longer times accounting for re-staining due to updated immunophenotyping 190 data, and to treatment 21 (4-77) days.”
Outcomes in refractory diffuse large B-cell lymphoma: results from the international SCHOLAR-1 study
Crump, M., Neelapu, S. S., Farooq, U., Neste, É. V. D., Kuruvilla, J. G., Westin, J. R., Link, B. K., Hay, A. E., Cerhan, J. R., Zhu, L., et al. (2017) · Blood · doi:10.1182/blood-2017-03-769620 · Cited by 1,755 · cited 1×
“For T-NHL, 9 months of PFS was considered exceptional, and for aggressive B-NHL and myeloid disease, 18 months was considered exceptional based on the definition by Wheeler and colleagues (26) and others (18, 43, 44).”
Background
Functional genomic landscape of acute myeloid leukaemia
Tyner, J. W., Tognon, C. E., Bottomly, D., Wilmot, B., Kurtz, S. E., Savage, S. L., Long, N., Schultz, A. R., Traer, E. A., Abel, M. L., et al. (2018) · Nature · doi:10.1038/s41586-018-0623-z · Cited by 1,359 · cited 1×
“The median time from sampling to scFPM report was 5 (1189 33) days, with longer times accounting for re-staining due to updated immunophenotyping 190 data, and to treatment 21 (4-77) days.”
Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial
Le Tourneau, C., Delord, J.-P., Gonçalves, A., Gavoille, C., Dubot, C., Isambert, N., Campone, M., Trédan, O., Massiani, M.-A., Mauborgne, C., et al. (2015) · The Lancet Oncology · doi:10.1016/s1470-2045(15)00188-6 · Cited by 1,137 · cited 2×
“therapeutic outcome impossible at our current state of knowledge (3).”
Background
BRAF Mutations in Hairy-Cell Leukemia
Tiacci, E., Trifonov, V. A., Schiavoni, G., Holmes, A. B., Kern, W., Martelli, M. P., Pucciarini, A., Bigerna, B., Pacini, R., Wells, V. A., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1014209 · Cited by 1,029 · cited 1×
“Genomic tumor characterization and treatment matching have improved the management of some patients with hematologic malignancies, such as BRAF in hairy cell leukemia (11, 12), IDH1/2 in acute myeloid leukemia ABSTRACT Personalized medicine aims to match the right drug with the right patient by using specific features…”
Background
Review of precision cancer medicine: Evolution of the treatment paradigm
Tsimberidou, A. M., Fountzilas, E., Nikanjam, M., & Kurzrock, R. (2020) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2020.102019 · Cited by 803 · cited 2×
“In contrast, a major 315 setback uniformly described in other personalized medicine trials is patient deterioration or 316 death during time of analysis (4,33).”
Background
High-Throughput Genomics and Clinical Outcome in Hard-to-Treat Advanced Cancers: Results of the MOSCATO 01 Trial
Massard, C., Michiels, S., Ferté, C., Deley, M. L., Lacroix, L., Hollebecque, A., Verlingue, L., Ileana, E. E., Rosellini, S., Ammari, S., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-16-1396 · Cited by 802 · cited 2×
“The median time from sampling to scFPM report was 5 (1189 33) days, with longer times accounting for re-staining due to updated immunophenotyping 190 data, and to treatment 21 (4-77) days.”
Background
Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study
Sicklick, J. K., Kato, S., Okamura, R., Schwaederle, M. C., Hahn, M. E., Williams, C. B., De, P. K., Krie, A. K., Piccioni, D. E., Miller, V. A., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0407-5 · Cited by 726 · cited 3×
“329 This strategy could raise the limited matching numbers, but has not yet been validated in a 330 prospective fashion (28).”
Background

Show all 43 references

Research works printed but not linked: 1

Swerdlow, 2017, WHO classification of tumours of haematopoietic and lymphoid tissues. Rev. 4th ed

Sorted by citations

The present and future of the Cancer Dependency Map
Arafeh, R., Shibue, T., Dempster, J. M., Hahn, W. C., & Vazquez, F. (2025) · Nature Reviews Cancer · doi:10.1038/s41568-024-00763-x · Cited by 454
“These efforts, including the Beat AML (acute myeloid leukaemia) programme led by the Oregon Health and Science University 138,139 , are now demonstrating the promise of this approach in guiding therapeutic decision making 140–142 .”
Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations
Letai, A. G., Bhola, P. D., & Welm, A. L. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.12.004 · Cited by 282
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236
“A single-cell functional precision medicine (scFPM) platform successfully guided treatment selection and enhanced patient outcomes in a prospective clinical study (157).”
Background
Clinical application of advanced multi-omics tumor profiling: Shaping precision oncology of the future
Akhoundova, D., & Rubin, M. A. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.08.011 · Cited by 198
Drug screening at single-organoid resolution via bioprinting and interferometry
Tebon, P. J., Wang, B., Markowitz, A. L., Davarifar, A., Tsai, B. L., Krawczuk, P., Gonzalez, A. E., Sartini, S., Murray, G. F., Nguyen, H. T. L., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38832-8 · Cited by 163
“Adjusted p -values were 0.0253, 0.6087, >0.9999, 0.1499 for print pressures 10, 15, 20, 25 kPa, respectively. e H&E staining shows the development of multicellular organoids over time regardless of seeding method.”
Influential
Organoids for Functional Precision Medicine in Advanced Pancreatic Cancer
Boilève, A., Cartry, J., Goudarzi, N., Bedja, S., Mathieu, J. R. R., Bani, M. A., Nicolle, R., Mouawia, A., Bouyakoub, R., Nicotra, C., et al. (2024) · Gastroenterology · doi:10.1053/j.gastro.2024.05.032 · Cited by 103
Pharmacotypes across the genomic landscape of pediatric acute lymphoblastic leukemia and impact on treatment response
Lee, S. H. R., Yang, W., Gocho, Y., John, A. J., Rowland, L., Smart, B., Williams, H., Maxwell, D., Hunt, J. P., Yang, W., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02112-7 · Cited by 99
“Early data from the EXALT trial 34 pointed to the feasibility of this approach and similar studies are much needed for ALL.”
Computational drug repurposing: approaches, evaluation of in silico resources and case studies
Tanoli, Z., Fernández‐Torras, A., Özcan, U. O., Kushnir, A., Nader, K. M., Gadiya, Y., Fiorenza, L., Ianevski, A., Vähä-Koskela, M. J. V., Miihkinen, M., et al. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01164-x · Cited by 104
“There are multiple ongoing functional precision medicine studies, in both blood cancers and solid tumours 199,200 , and the first findings show promising results and clinical benefits, even in cases in which standard genomic profiling cannot provide any actionable information 201 .”
Precision cancer medicine: Concepts, current practice, and future developments
Edsjö, A., Holmquist, L., Geoerger, B., Nowak, F., Gomon, G., Alix‐Panabières, C., Staaf, J., Ploeger, C., Lassen, U., Tourneau, C. L., et al. (2023) · Journal of Internal Medicine · doi:10.1111/joim.13709 · Cited by 91
High-throughput identification of repurposable neuroactive drugs with potent anti-glioblastoma activity
Lee, S., Weiß, T., Bühler, M., Mena, J., Lottenbach, Z., Wegmann, R., Sun, M., Bihl, M., Augustynek, B., Baumann, S. P., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03224-y · Cited by 92
“…with the prior successful use of PCY to guide patient treat-ment for blood cancers and growing community efforts in functional precision oncology 32,33,35,55,56 , our results indicate the feasibility of using PCY as a drug discovery and personalized treatment selection platform for hard-to-treat…”
Self-citation
Feasibility of functional precision medicine for guiding treatment of relapsed or refractory pediatric cancers
de la Rocha, A. A., Berlow, N., Fader, M. E., Coats, E. R., Saghira, C., Espinal, P. S., Galano, J., Khatib, Z. A., Abdella, H., Maher, O. M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02848-4 · Cited by 75
“The secondary objectives included in the study reflect those that are now commonly reported in genomics and FPM studies 15,26–29 , including ORR between cohorts, PFS between cohorts and PFS2/PFS1 ratio metrics between the study regimen and the most recent previous regimen of the same patient above…”
BackgroundMethodResultInfluential
Drug sensitivity profiling of 3D tumor tissue cultures in the pediatric precision oncology program INFORM
Peterziel, H., Jamaladdin, N., ElHarouni, D., Gerloff, X. F., Herter, S., Fiesel, P., Berker, Y., Blattner-Johnson, M., Schramm, K., Jones, B. C., et al. (2022) · npj Precision Oncology · doi:10.1038/s41698-022-00335-y · Cited by 69
Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment
Kumar, K. S., Miskovic, V., Blasiak, A., Sundar, R., Pedrocchi, A., Pearson, A. T., Prelaj, A., & Ho, D. (2023) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_390084 · Cited by 68
Implementing patient derived organoids in functional precision medicine for patients with advanced colorectal cancer
Cartry, J., Bedja, S., Boilève, A., Mathieu, J. R. R., Gontran, E., Annereau, M., Job, B., Mouawia, A., Mathias, P., de Baère, T., et al. (2023) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-023-02853-4 · Cited by 71
“Recently, Malani et al . and Kor-nauth et al . showed excellent results in FPM-based strategies for patient with hematological malignancies [8, 9].”
Background
Ex vivo venetoclax sensitivity testing predicts treatment response in acute myeloid leukemia
Kuusanmäki, H. A., Kytölä, S., Vänttinen, I., Ruokoranta, T. M., Ranta, A., Huuhtanen, J., Suvela, M. H., Parsons, A., Holopainen, A., Partanen, A., et al. (2022) · Haematologica · doi:10.3324/haematol.2022.281692 · Cited by 55
Novel precision medicine approaches and treatment strategies in hematological malignancies
Rosenquist, R., Bernard, E., Erkers, T., Scott, D. W., Itzykson, R., Rousselot, P. H., Soulier, J., Hutchings, M., Östling, P., Cavelier, L. B., et al. (2023) · Journal of Internal Medicine · doi:10.1111/joim.13697 · Cited by 56
Relapsed pediatric acute myeloid leukaemia: state-of-the-art in 2023
Egan, G., & Tasian, S. K. (2023) · Haematologica · doi:10.3324/haematol.2022.281106 · Cited by 47
Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma
Kropivšek, K., Kachel, P., Götze, S., Wegmann, R., Festl, Y., Severin, Y., Hale, B. D., Mena, J., van Drogen, A., Dietliker, N., et al. (2023) · Nature Cancer · doi:10.1038/s43018-023-00544-9 · Cited by 50
Self-citation
Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine
Jose, A., Kulkarni, P., Thilakan, J., Munisamy, M., Malhotra, A. G., Singh, J., Kumar, A., Rangnekar, V. M., Arya, N., & Rao, M. (2024) · Molecular Cancer · doi:10.1186/s12943-023-01916-6 · Cited by 49
“About 40% of these patients had exceptional responses that lasted three times longer than expected for their condition [160].”
Background
Ex vivo drug response profiling for response and outcome prediction in hematologic malignancies: the prospective non-interventional SMARTrial
Liebers, N., Bruch, P., Terzer, T., Hernández-Hernández, M. V., Paramasivam, N., Fitzgerald, D., Altmann, H., Roider, T., Kolb, C., Knoll, M., et al. (2023) · Nature Cancer · doi:10.1038/s43018-023-00645-5 · Cited by 46
“…Approach to Rational Treatment Trial of Blood Cancer (SMARTrial; ClinicalTrials.gov NCT03488641) complements recently published studies 11,12 by demonstrating the feasibility of ex vivo DRP as a clinical routine test and by directly relating ex vivo and in vivo drug effects for…”
Influential
Single-cell multiomics analysis reveals dynamic clonal evolution and targetable phenotypes in acute myeloid leukemia with complex karyotype
Leppä, A., Grimes, K., Jeong, H., Huang, F. Y., Andrades, Á., Waclawiczek, A., Boch, T., Jauch, A. S., Renders, S., Stelmach, P., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01999-x · Cited by 43
“Although we were not able to identify inhibitors with strong efficacy toward LSCs in all patients, our platform shows promise for discovering alternative treatments in CK-AML, which may be particularly relevant for personalized cancer therapy 42,43 .”
Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes
Severin, Y., Hale, B. D., Mena, J., Goslings, D. A. M., Frey, B. M., & Snijder, B. (2022) · Science Advances · doi:10.1126/sciadv.abn5631 · Cited by 45
Self-citation
An ex vivo platform to guide drug combination treatment in relapsed/refractory lymphoma
Goh, J., Mel, S. D., Hoppe, M. M., Rashid, M. B. M. A., Zhang, X. Y., Jaynes, P. W., Ng, E. K. Y., Rahmat, N. D. B., Jayalakshmi, Liu, C. X., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abn7824 · Cited by 39
Functional Drug Screening in the Era of Precision Medicine
Napoli, G. C., Figg, W. D., & Chau, C. H. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.912641 · Cited by 41
“3-fold improvements in PFS compared with prior therapies, and 40% of these patients had outstanding responses that lasted 3-fold longer than predicted for their disease (93).”
Result
Leading artificial intelligence–driven drug discovery platforms: 2025 landscape and global outlook
Dharmasivam, M., Kaya, B., Akinware, A., Azad, M. G., & Richardson, D. R. (2025) · Pharmacological Reviews · doi:10.1016/j.pharmr.2025.100102 · Cited by 45
Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia
Wegmann, R., Bonilla, X., Casanova, R., Chevrier, S., Coelho, R., Esposito, C., Ficek, J., Götze, S., Gut, G., Jacob, F., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53535-4 · Cited by 35
“Pharmacoscopy (PCY) was performed as previously described 13,20,27,72 .”
Influential
Application of precision medicine in clinical routine in haematology—Challenges and opportunities
Wästerlid, T., Cavelier, L. B., Haferlach, C., Konopleva, M. Y., Fröhling, S., Östling, P., Bullinger, L., Fioretos, T., & Smedby, K. E. E. (2022) · Journal of Internal Medicine · doi:10.1111/joim.13508 · Cited by 35
“Recently, the clinical feasibility of pharmacoscopy was shown in the ﬁrst prospective single-cell FPM (scFPM) trial called EXALT [128].”
Ex vivo venetoclax sensitivity predicts clinical response in acute myeloid leukemia in the prospective VenEx trial
Kytölä, S., Vänttinen, I., Ruokoranta, T. M., Partanen, A., Holopainen, A., Saad, J. J., Kuusisto, M. E. L., Koskela, S. M., Räty, R., Itälä‐Remes, M., et al. (2024) · Blood · doi:10.1182/blood.2024024968 · Cited by 32
Precision oncology using ex vivo technology: a step towards individualised cancer care?
Williams, S. T., Wells, G., Conroy, S., Gagg, H., Allen, R. J., Rominiyi, O., Helleday, T., Hullock, K., Pennington, C. E. W., Rantala, J., et al. (2022) · Expert Reviews in Molecular Medicine · doi:10.1017/erm.2022.32 · Cited by 35
“19 ).”
Background
Relapsed Acute Lymphoblastic Leukemia
Sidhu, J., Gogoi, M. P., Krishnan, S., & Saha, V. (2023) · The Indian Journal of Pediatrics · doi:10.1007/s12098-023-04635-4 · Cited by 31
“%) who received DRP-based therapy [44].”
Fatty acid synthase ( FASN ) signalome: A molecular guide for precision oncology
Menéndez, J. A., Cuyàs, E., Encinar, J. A., Steen, T. V., Verdura, S., Llop‐Hernández, À., López, J., Serrano‐Hervás, E., Osuna, S., Martín-Castillo, B., et al. (2023) · Molecular Oncology · doi:10.1002/1878-0261.13582 · Cited by 41
Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine
Heinemann, T., Kornauth, C., Severin, Y., Vladimer, G. I., Pemovska, T., Hadzijusufovic, E., Agis, H., Krauth, M.-T., Sperr, W. R., Valent, P., et al. (2022) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-21-0219 · Cited by 28
“However, genetically stratified precision medicine is currently estimated to benefit around 10% of all patients with cancer (4), indicating a clear need for additional methods to identify effective treatments.”
BackgroundMethodInfluentialSelf-citation
The drug efficacy testing in 3D cultures platform identifies effective drugs for ovarian cancer patients
Åkerlund, E., Gudoitytė, G., Moussaud-Lamodière, E. L., Lind, O., Bwanika, H. C., Lehti, K., Salehi, S., Carlson, J. W., Wallin, E., Fernebro, J., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00463-z · Cited by 28
Feasibility of multiomics tumor profiling for guiding treatment of melanoma
Miglino, N., Toussaint, N. C., Ring, A., Bonilla, X., Tusup, M., Gosztonyi, B., Mehra, T., Gut, G., Jacob, F., Chevrier, S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03715-6 · Cited by 31
SOX2 dosage sustains tumor-promoting inflammation to drive disease aggressiveness by modulating the FOSL2/IL6 axis
Njouendou, A. J., Szarvas, T., Tiofack, A. A. Z., Kenfack, R. N., Tonouo, P. D., Ananga, S. N., Bell, E. H. M. D., Simo, G., Hoheisel, J. D., Siveke, J. T., et al. (2023) · Molecular Cancer · doi:10.1186/s12943-023-01734-w · Cited by 25
Functional Precision Medicine: Putting Drugs on Patient Cancer Cells and Seeing What Happens
Letai, A. G. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1498 · Cited by 23
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
Murumägi, A., Ungureanu, D., Khan, S. A., Arjama, M., Välimäki, K. E., Ianevski, A., Ianevski, P., Bergström, R., Dini, A., Kanerva, A., et al. (2022) · British Journal of Cancer · doi:10.1038/s41416-022-02067-z · Cited by 24
Feasibility analysis of using patient-derived tumour organoids for treatment decision guidance in locally advanced head and neck squamous cell carcinoma
Fisch, A.-S., Pestana, A., Sachse, V., Doll, C., Hofmann, E., Heiland, M., Obermueller, T., Heidemann, J., Dommerich, S., Schoppe, D., et al. (2024) · European Journal of Cancer · doi:10.1016/j.ejca.2024.115100 · Cited by 22
Pharmacogenomic Profiling of Pediatric Acute Myeloid Leukemia to Identify Therapeutic Vulnerabilities and Inform Functional Precision Medicine
Wang, H., Chan, K. Y. Y., Cheng, C., Ng, M. H. L., Lee, P. Y., Cheng, F. W. T., Lam, G., Chow, T. W., Ha, S., Chiang, A., et al. (2022) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-22-0011 · Cited by 22
Standardized assays to monitor drug sensitivity in hematologic cancers
Durán, P. A., Hermansen, J. U., Giliberto, M., Yin, Y., Hanes, R., Gordon, S. L., Kuusanmäki, H. A., Brodersen, A. M., Andersen, A. N., Taskén, K., et al. (2023) · Cell Death Discovery · doi:10.1038/s41420-023-01722-5 · Cited by 22
Strategies for the biological synthesis of D-glucuronic acid and its derivatives
Hu, H., Li, J., Jiang, W., Jiang, Y., Wan, Y., Wang, Y., Xin, F., & Zhang, W. (2024) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-024-03900-8 · Cited by 22
“In order to reduce the cost, the multi-enzyme cascade reaction has been developed in recent years (Kornauth et al. 2022).”
Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia
Qin, G., Dai, J., Chien, S. S., Martins, T. J., Loera, B., Nguyen, Q., Oakes, M., Tercan, B., Aguilar, B. H., Hagen, L., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-23-1674 · Cited by 21
“However, the current ex vivo drug sensitivity testing methods are now showing predictive value or correlations with response in clinical trials (17, 38, 39).”
Method
Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications
Samarkhazan, H. S. (2025) · Clinical and Experimental Medicine · doi:10.1007/s10238-025-01858-x · Cited by 23
“Similarly, proteogenomic studies have uncovered post-translational modifications, such as STAT3 phosphorylation, that mediate resistance to FLT3 inhibitors despite favorable mutation profiles [12].”
Functional precision cancer medicine: drug sensitivity screening enabled by cell culture models
Flobak, Å., Skånland, S. S., Hovig, E. J., Taskén, K., & Russnes, H. G. (2022) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.08.009 · Cited by 26
Long-term maintenance of patient-specific characteristics in tumoroids from six cancer indications
Paul, C. D., Yankaskas, C., Thakuri, P. S., Balhouse, B. N., Salen, S., Bullock, A., Beam, S., Chatman, A., Djikeng, S., Yang, X. J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-86979-9 · Cited by 19
“Finally, multiple reports have demonstrated that drug response in patient-derived tumoroid models, at least in some cases, correlates highly with matched donor response during clinical treatment 29,48–52 , 63– 68 .”
Impact of Age on Pharmacogenomics and Treatment Outcomes of B-Cell Acute Lymphoblastic Leukemia
Yoshimura, S., Li, Z., Gocho, Y., Yang, W., Crews, K. R., Lee, S. H. R., Roberts, K. G., Mullighan, C. G., Relling, M. V., Yu, J., et al. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.24.00500 · Cited by 19
“…therapy for adults with aggressive hematological malignancies demonstrated both the feasibility of this approach and its clinical bene ﬁ ts. 24,27 For pediatric ALL, our group and others recently reported that leukemia pharmacotypes are associated with treatment response and survival…”
A tumor microenvironment model of chronic lymphocytic leukemia enables drug sensitivity testing to guide precision medicine
Hermansen, J. U., Yin, Y., Urban, A., Myklebust, C. V., Karlsen, L., Melvold, K., Tveita, A. A., Taskén, K., Munthe, L. A., Tjønnfjord, G. E., et al. (2023) · Cell Death Discovery · doi:10.1038/s41420-023-01426-w · Cited by 18
“3-fold prolonged progression-free survival compared with the previous line of therapy) [5].”
BackgroundMethodInfluential
Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions
Gallo, C. (2023) · Bioengineering · doi:10.3390/bioengineering10050613 · Cited by 18
“A study [53] from the Medizinische Universität Wien (Medical University of Vienna) successfully tested the use of artificial intelligence in the fight against a tumor.”
Background
Proteogenetic drug response profiling elucidates targetable vulnerabilities of myelofibrosis
Wildschut, M. H. E., Mena, J., Doerdelmann, C., van Oostrum, M., Hale, B. D., Settelmeier, J., Festl, Y., Lysenko, V., Schürch, P. M., Ring, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42101-z · Cited by 18
“Utilizing pharmacoscopy, previously shown to be clinically predictive across a variety of hematological malignancies 17,18 , we developed a single-cell multiplexed and deep learning-based MF drug response pro ﬁ ling platform that captures patient heterogeneity and quanti ﬁ es drug ef ﬁ cacy on the…”
Self-citation
The future of precision cancer therapy might be to try everything
Dolgin, E. (2024) · Nature · doi:10.1038/d41586-024-00392-2 · Cited by 18
Mcl‐1 and Bcl‐xL levels predict responsiveness to dual MEK/Bcl‐2 inhibition in B‐cell malignancies
Melvold, K., Giliberto, M., Karlsen, L., Durán, P. A., Hanes, R., Holien, T., Enserink, J. M., Brown, J. R., Tjønnfjord, G. E., Taskén, K., et al. (2021) · Molecular Oncology · doi:10.1002/1878-0261.13153 · Cited by 19
Integration of Multi-omic Data in a Molecular Tumor Board Reveals EGFR-Associated ALK-Inhibitor Resistance in a Patient With Inflammatory Myofibroblastic Cancer
Hunt, A. L., Nutcharoen, A., Randall, J., Papazian, A., Deeken, J. F., Maxwell, G. L., Bateman, N. W., Petricoin, E. F., Benyounes, A., Conrads, T. P., et al. (2023) · The Oncologist · doi:10.1093/oncolo/oyad129 · Cited by 17
Robust scoring of selective drug responses for patient-tailored therapy selection
Chen, Y., He, L., Ianevski, A., Durán, P. A., Potdar, S., Saarela, J., Miettinen, J. J., Kytölä, S., Miettinen, S., Manninen, M. J., et al. (2023) · Nature Protocols · doi:10.1038/s41596-023-00903-x · Cited by 17
The challenge of making the right choice: patient avatars in the era of cancer immunotherapies
Kayser, C., Bräuer, A., Susanne, S., & Wandmacher, A. M. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1237565 · Cited by 17
“In this approach, the drug response of tumor cells within the cell bulk was determined by immunofluorescent microscopy and quantification of the surviving proportion of tumor cells in comparison to controls (40).”
MethodResult
A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Chen, Y., He, L., Ianevski, A., Nader, K. M., Ruokoranta, T. M., Linnavirta, N., Miettinen, J. J., Vähä-Koskela, M. J. V., Vänttinen, I., Kuusanmäki, H. A., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-24-3840 · Cited by 20
Integrated molecular and functional characterization of the intrinsic apoptotic machinery identifies therapeutic vulnerabilities in glioma
Fernandez, E. G., Mai, W. X., Song, K., Bayley, N. A., Kim, J., Zhu, H., Pioso, M. S., Young, P., Andrasz, C. L., Cadet, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54138-9 · Cited by 15
Discovery of predictive biomarkers for cancer therapy through computational approaches
Wang, X., Nguyen, J., Nader, K. M., Miihkinen, M., Wall, P., Singhal, A., Bedard, P. L., Ideker, T., Aittokallio, T. A., & Haibe‐Kains, B. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01109-8 · Cited by 24
Integrated drug profiling and CRISPR screening identify BCR::ABL1-independent vulnerabilities in chronic myeloid leukemia
Adnan Awad, S., Dufva, O., Klievink, J., Karjalainen, E., Ianevski, A., Pietarinen, P., Kim, D., Potdar, S., Wolf, M., Lotfi, K., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101521 · Cited by 14
Efficacy and feasibility of pharmacoscopy-guided treatment for acute myeloid leukemia patients who have exhausted all registered therapeutic options
Schmid, J. A., Festl, Y., Severin, Y., Bacher, U., Kronig, M.-N., Snijder, B., & Pabst, T. (2023) · Haematologica · doi:10.3324/haematol.2023.283224 · Cited by 14
Self-citation
Ex vivo Drug Sensitivity Imaging-based Platform for Primary Acute Lymphoblastic Leukemia Cells
Rowland, L., Smart, B., Brown, A., Dettorre, G. M., Gocho, Y., Hunt, J. P., Yang, W., Yoshimura, S., Reyes, N., Du, G., et al. (2023) · BIO-PROTOCOL · doi:10.21769/bioprotoc.4731 · Cited by 14
“, pharmacotyping) with genomic testing to elucidate the biological basis of variability in treatment response (Frismantas et al., 2017; Kornauth et al., 2022; Lee et al., 2023).”
N-of-1 medicine
Wang, P., Leong, Q. Y., Lau, N. Y., Ng, W. Y., Kwek, S. P., Tan, L. W. J., Song, S.-W., You, K., Chong, L. M., Zhuang, I., et al. (2024) · Singapore Medical Journal · doi:10.4103/singaporemedj.smj-2023-243 · Cited by 14
“[43] Recently, phenotypic-driven approaches based on correlations discovered via neural networks have also been developed.”
Background
Functional Testing to Characterize and Stratify PI3K Inhibitor Responses in Chronic Lymphocytic Leukemia
Yin, Y., Athanasiadis, P., Karlsen, L., Urban, A., Xu, H., Murali, I., Fernandes, S. M., Arribas, A. J., Hilli, A. K., Taskén, K., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-1221 · Cited by 14
Therapeutic biomarkers in acute myeloid leukemia: functional and genomic approaches
Bhatia, K. S., Sandhu, V., Wong, M. H., Iyer, P., & Bhatt, S. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1275251 · Cited by 13
Acquired Multidrug Resistance in AML Is Caused by Low Apoptotic Priming in Relapsed Myeloblasts
Olesinski, E. A., Bhatia, K. S., Wang, C., Pioso, M. S., Lin, X. X., Mamdouh, A. M., Ng, S. X., Sandhu, V., Jasdanwala, S. S., Yilma, B., et al. (2024) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-24-0001 · Cited by 14
Functional testing of relapsed chronic lymphocytic leukemia guides precision medicine and maps response and resistance mechanisms. An index case
Skånland, S. S., Inngjerdingen, M., Bendiksen, H., York, J. P., Spetalen, S., Munthe, L. A., & Tjønnfjord, G. E. (2022) · Haematologica · doi:10.3324/haematol.2021.280393 · Cited by 13
Microfluidic-based dynamic BH3 profiling predicts anticancer treatment efficacy
Manzano-Muñoz, A., Yeste, J., Ortega, M. A., Martín, F., López, A., Rosell, J., Castro, S., Serrano, C., Samitier, J., Ramón‐Azcón, J., et al. (2022) · npj Precision Oncology · doi:10.1038/s41698-022-00333-0 · Cited by 14
Delineating functional and molecular impact of ex vivo sample handling in precision medicine
Struyf, N., Österroos, A., Vesterlund, M., Arnroth, C., James, T., Sunandar, S., Mermelekas, G., Bohlin, A., Levedahl, K. H., Bengtzén, S., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00528-7 · Cited by 11
Molecular and functional landscape of malignant serous effusions for precision oncology
Wegmann, R., Bankel, L., Festl, Y., Lau, K., Lee, S., Arnold, F. M., Cappelletti, V., Fehr, A., Picotti, P., Dedes, K. J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52694-8 · Cited by 14
“This drug concentration was chosen based on prior experience in hemato-oncology 26,46,47 .”
InfluentialSelf-citation
High-throughput single-cell density measurements enable dynamic profiling of immune cell and drug response from patient samples
Wu, W., Ishamuddin, S. H., Quinn, T. W., Yerrum, S., Zhang, Y., Debaize, L., Kao, P.-L., Duquette, S. M., Murakami, M. A., Mohseni, M., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01408-6 · Cited by 19
“Functional precision medicine (FPM) approaches, which involve drug testing performed directly on patient tumour cells, have emerged in recent years to help select the optimal drug treatment at the time of diagnosis or relapse 40 .”
Comparing the value of mono- vs coculture for high-throughput compound screening in hematological malignancies
Herbst, S. A., Kim, V., Roider, T., Schitter, E. C., Bruch, P., Liebers, N., Kolb, C., Knoll, M., Lu, J., Dreger, P., et al. (2023) · Blood Advances · doi:10.1182/bloodadvances.2022009652 · Cited by 11
“11,12 The disadvantage that most of these studies face is that, deprived of microenvironmental stimuli, leukemia cells undergo spontaneous apoptosis ex vivo.”
Advances in peripheral T cell lymphomas: pathogenesis, genetic landscapes and emerging therapeutic targets
Pichler, A., Amador, C., Fujimoto, A., Takeuchi, K., de Jong, D., Iqbal, J., & Staber, P. B. (2024) · Histopathology · doi:10.1111/his.15376 · Cited by 11
“144 The promising results prompted current testing in the prospective randomised EXALT-2 study.”
Self-citation
Ex Vivo Drug Sensitivity Correlates with Clinical Response and Supports Personalized Therapy in Pediatric AML
Strachan, D. C., Gu, C. J., Kita, R., Anderson, E. K., Richardson, M. A., Yam, G. C., Pimm, G., Roselli, J., Schweickert, A., Terrell, M., et al. (2022) · Cancers · doi:10.3390/cancers14246240 · Cited by 11
Functional Precision Oncology: The Next Frontier to Improve Glioblastoma Outcome?
Panovska, D., & Smet, F. D. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23158637 · Cited by 11
“One of the greatest advantages of this approach was the short time between the testing and making treatment decisions [90].”
Background
Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling
Andersen, A. N., Brodersen, A. M., Durán, P. A., Piechaczyk, L., Tadele, D. S., Baken, L., Fredriksen, J., Stoksflod, M., Lenartova, A., Fløisand, Y., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100654 · Cited by 10
Pediatric AML: state of the Art and Future Directions
Iyer, P. (2025) · Pediatric Hematology and Oncology · doi:10.1080/08880018.2025.2453861 · Cited by 10
“105 These approaches have been tested in a functional precision tumor board that promises quick turnarounds and encouraging outcomes.”
Tumor-Associated Macrophages and Related Myelomonocytic Cells in the Tumor Microenvironment of Multiple Myeloma
Wang, S. S. Y., Chng, W. J., Liu, H., & Mel, S. D. (2022) · Cancers · doi:10.3390/cancers14225654 · Cited by 10
Immunophenotyping with (phospho)protein profiling and fluorescent cell barcoding for single-cell signaling analysis and biomarker discovery
Hermansen, J. U., Yin, Y., Rein, I. D., & Skånland, S. S. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00604-y · Cited by 9
“Functional biomarkers such as drug sensitivity screens, BH3 pro ﬁ les, and (phospho)protein pro ﬁ les have demonstrated pre-clinical and clinical value in predicting drug responses and guiding clinical decision-making 7,15,16,29 – 34 .”
Mismatch repair-proficient tumor footprints in the sands of immune desert: mechanistic constraints and precision platforms
Majumder, B., Nataraj, N. B., Maitreyi, L., & Datta, S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1414376 · Cited by 10
Next‐generation spatial transcriptomics: unleashing the power to gear up translational oncology
Wang, N., Hong, W., Wu, Y., Chen, Z., Bai, M., Wang, W., & Zhu, J. Y. (2024) · MedComm · doi:10.1002/mco2.765 · Cited by 10
BCL-2 dependence is a favorable predictive marker of response to therapy for chronic lymphocytic leukemia
Chong, S. J. F., Lu, J., Valentin, R., Lehmberg, T. Z., Eu, J. Q., Wang, J., Fen, Z., Kong, L. R., Fernandes, S. M., Zhang, J., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02260-7 · Cited by 9
“Subsequent efforts using ex vivo profiling of response to individual drugs or combinations revealed synergistic drug effects and genetic dependences in CLL [7] as well as guided treatment with improved clinical benefits in certain patients with aggressive hematologic malignancies [8].”
Patient-derived tumoroids from CIC::DUX4 rearranged sarcoma identify MCL1 as a therapeutic target
Breunis, W. B., Brack, E. K., Ehlers, A. C., Bechtold, I., Kisele, S., Wurth, J., Mous, L., Zabėlė, D., Steffen, F. D., Zahnow, F., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62629-6 · Cited by 10
“Therapy guidance based on DRP data has been shown to improve outcomes in heavily pretreated patients with hematologic malignancies 17 .”
A pipeline for malignancy and therapy agnostic assessment of cancer drug response using cell mass measurements
Kimmerling, R. J., Stevens, M. M., Olçum, S., Minnah, A., Vacha, M., LaBella, R., Ferri, M., Wasserman, S. C., Fujii, J., Shaheen, Z., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-04270-3 · Cited by 8
Microphysiological Drug‐Testing Platform for Identifying Responses to Prodrug Treatment in Primary Leukemia
Gökçe, F., Kaestli, A. J., Lohasz, C., de Geus, M., Kaltenbach, H., Renggli, K., Bornhauser, B. C., Hierlemann, A. R., & Modena, M. M. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202202506 · Cited by 8
Macrophage sensitivity to bexmarilimab-induced reprogramming is shaped by the tumor microenvironment
Rannikko, J. H., Turpin, R., Boström, P., Virtakoivu, R., Harth, C., Takeda, A., Tamminen, A., Koskivuo, I., & Hollmén, M. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-011292 · Cited by 9
Functional prediction of response to therapy prior to therapeutic intervention is associated with improved survival in patients with high-grade glioma
Ledford, A., Rodriguez, A., Lipinski, L., Abad, A. P., Fenstermaker, R. A., Edenfield, J., Kanos, C. C., Redjal, N., Mansouri, A., Zacharia, B. E., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-68801-0 · Cited by 8
Functional precision medicine: the future of cancer care
de la Rocha, A. A., Berlow, N., & Azzam, D. J. (2024) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2024.10.015 · Cited by 8
Protein Profiles Predict Treatment Responses to the PI3K Inhibitor Umbralisib in Patients with Chronic Lymphocytic Leukemia
Yin, Y., Xu, H., He, L., Brown, J. R., Mato, A. R., Aittokallio, T. A., & Skånland, S. S. (2025) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-2911 · Cited by 7
Functional combinatorial precision medicine for predicting and optimizing soft tissue sarcoma treatments
Chan, S. P. Y., Rashid, M. B. M. A., Lim, J. J., Goh, J. J. N., Wong, W. Y., Hooi, L., Ismail, N. N., Luo, B., Chen, B. J., Noor, N. F. B. M., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00851-7 · Cited by 7
“Functional precision oncology (FPO) has promising potential to expand treatment options when standard or salvage options are exhausted 20 – 24 .”
Precision oncology to overcome resistance in R/R AML in children and adults requires combinations of cytotoxic, targeted, and immunological treatments
Jädersten, M., Lilienthal, I., Nilsson, C. C., Fredrikson, L., Pronk, C. J., Juul-Dam, K. L., Kontro, M., & Herold, N. (2025) · Journal of Internal Medicine · doi:10.1111/joim.70004 · Cited by 7
“The EXALT trial proﬁled 143 patients with advanced haematologic malignancies using a microscopy-based single-cell readout, with 56 participants treated [93].”
Leveraging Single-Cell Technologies to Advance Understanding of Myocardial Disease
Gardner, R. S., Tucker, N. R., & Amancherla, K. V. (2026) · Circulation Research · doi:10.1161/circresaha.125.326002 · Cited by 9
Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma
Panovska, D., Nazari, P., Cole, B., Creemers, P.-J., Derweduwe, M., Solie, L., Gassen, S. V., Claeys, A., Verbeke, T., Cohen, E. F., et al. (2023) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-023-04772-1 · Cited by 7
“Inspired by the recent success of a functional diagnostics approach introduced in advanced hematological malignances [15,16], we developed a brain-tumor-focused functional readout to interrogate therapeutic efficacy at single-cell resolution, called the PROSPERO assay.”
Background
Decoding Acute Myeloid Leukemia: A Clinician’s Guide to Functional Profiling
Iyer, P., Jasdanwala, S. S., Wang, Y., Bhatia, K. S., & Bhatt, S. (2024) · Diagnostics · doi:10.3390/diagnostics14222560 · Cited by 8
Limited efficacy for ibrutinib and venetoclax in T-prolymphocytic leukemia: results from a phase 2 international study
Herling, M., Dearden, C. E., Zaja, F. F., El‐Sharkawi, D., Ding, W., Bellido, M., Khot, A. S., Tick, L. W., Jacobsen, E. D., Eyre, T. A., et al. (2024) · Blood Advances · doi:10.1182/bloodadvances.2023012248 · Cited by 7
Self-citation
Comparing functional and genomic‐based precision medicine in blood cancer patients
Kazianka, L., Pichler, A., Agreiter, C., Rohrbeck, J., Kornauth, C., Porpaczy, E. A., Sillaber, C., Sperr, W. R., Gleixner, K. V., Hauswirth, A. W., et al. (2025) · HemaSphere · doi:10.1002/hem3.70129 · Cited by 6
Self-citation
Celebrating Ulrik Ringborg: Multi-Omics-Based Patient Stratification for Precision Cancer Treatment
Teleanu, M., Schneider, A., Ball, C. R., Leber, M. F., Stange, C., Krieghoff‐Henning, E., Beck, K., Heilig, C. E., Kreutzfeldt, S., Küster, B., et al. (2025) · Biomolecules · doi:10.3390/biom15050693 · Cited by 6
“…on these results, the EXALT2 trial will prospectively compare functional testing, genomic profiling, and standard-of-care approaches in patients with relapsed or refractory hematologic malignancies and will incorporate an interdisciplinary tumor board to discuss treatment recommendations [65].”
Ex vivo drug screening on patient-derived tumor material to advance functional precision in oncology: an overview on current approaches and unresolved challenges
Spiró, Z., Amin, E.-H., Mair, M. J., Pieber, T. R., Prietl, B., Spiegl‐Kreinecker, S., Stanzer, S., Wöhrer, A., Preusser, M., & Berghoff, A. S. (2025) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2025.103072 · Cited by 7
Intratumor heterogeneity and clonal evolution in sarcomas: Molecular, cellular, and functional drivers of therapy resistance
Grünewald, T. G. P., Oehme, I., Wagner, J., & Fröhling, S. (2026) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2026.02.001 · Cited by 6
Mutational analysis and protein profiling predict drug sensitivity in multiple myeloma cell lines
Giliberto, M., Santana, L. M., Holien, T., Misund, K., Nakken, S., Vodák, D., Hovig, E. J., Meza‐Zepeda, L. A., Coward, E., Waage, A., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1040730 · Cited by 6
Measuring single-cell density with high throughput enables dynamic profiling of immune cell and drug response from patient samplesPreprint
Wu, W., Ishamuddin, S. H., Quinn, T. W., Yerrum, S., Zhang, Y., Debaize, L. L., Kao, P.-L., Duquette, S. M., Murakami, M. A., Mohseni, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.25.591092 · Cited by 6
“Functional precision medicine (FPM) approaches, which involve drug testing performed directly on patient tumor cells, have emerged in recent years to help select the optimal drug treatment at the time of diagnosis or relapse 42 .”
Image-based drug screening combined with molecular profiling identifies signatures and drivers of therapy resistance in pediatric AML
Haladik, B., Maurer‐Granofszky, M., Zoescher, P., Heredia, R. J., Frohne, A., Segarra‐Roca, A., Casey, C., Kartnig, F., Giuliani, S., Rashkova, C., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102304 · Cited by 5
“Building upon our previously established approach with demonstrated clinical efficacy 26,27 —albeit with a relatively short incubation time—we here established an image-based screening platform that enables fast turnover times for a vast array of drugs with clinical relevance, such as…”
InfluentialSelf-citation

Authors: Kornauth, C., Pemovska, T., Vladimer, G. I., Bayer, G., Bergmann, M., Eder, S., Eichner, R., Erl, M., Esterbauer, H., Exner, R., Felsleitner-Hauer, V., Forte, M., Gaiger, A., Geissler, K., Greinix, H. T., Gstöttner, W., Hacker, M., Hartmann, B. L., Hauswirth, A. W., Heinemann, T., Heintel, D., Hoda, M. A., Hopfinger, G., Jaeger, U., Kazianka, L., Kenner, L., Kiesewetter, B., Krall, N., Krajnik, G., Kubicek, S., Le, T., Lubowitzki, S., Mayerhoefer, M. E., Menschel, E., Merkel, O., Miura, K., Müllauer, L., Neumeister, P., Noesslinger, T., Ocko, K., Öhler, L., Panny, M., Pichler, A., Porpaczy, E., Prager, G. W., Raderer, M., Ristl, R., Ruckser, R., Salamon, J., Schiefer, A.-I., Schmolke, A.-S., Schwarzinger, I., Selzer, E., Sillaber, C., Skrabs, C., Sperr, W. R., Srndic, I., Thalhammer, R., Valent, P., van der Kouwe, E., Vanura, K., Vogt, S., Waldstein, C., Wolf, D., Zielinski, C. C., Zojer, N., Simonitsch-Klupp, I., Superti-Furga, G., Snijder, B., & Staber, P. B.
Affiliations: Vienna General Hospital (AT)
Austrian Academy of Sciences (AT)
Medical University of Vienna (AT)
Klinikum Klagenfurt (AT)
8Abteilung für Innere Medizin, Krankenhaus der Barmherzigen Brüder Salzburg, Salzburg, Austria.
Universitätsklinikum Wiener Neustadt (AT)
Sigmund Freud Privatuniversität Wien (AT)
Medical University of Graz (AT)
Landeskrankenhaus Feldkirch (AT)
ETH Zurich (CH)
17Division of Medicine I, Klinik Ottakring, Vienna, Austria.
19Third Medical Department, Centre for Oncology and Haematology, Klinik Favoriten, Vienna, Austria.
Universitätsklinikum St. Pölten (AT)
Memorial Sloan Kettering Cancer Center (US)
Hanusch Hospital (AT)
Nihon University (JP)
St. Josef-Krankenhaus Wien (AT)
29Second Medical Department, Klinik Donaustadt, Vienna, Austria.
30Department of Medicine, Landesklinikum Waidhofen a.d. Ybbs, Waidhofen-Ybbs, Austria.
Ludwig Boltzmann Institute for Hematology and Oncology (AT)
1Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria.
Innsbruck Medical University (AT)
5Department of Pathology, Medical University of Vienna, Vienna, Austria.
3CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Corresponding author: Staber, Philipp B.
ORCID: Kornauth, Christoph
Pemovska, Tea
Vladimer, Gregory I.
Bergmann, Michael
Eichner, Ruth
Esterbauer, Harald
Exner, Ruth
Gaiger, Alexander
Geissler, Klaus
Hacker, Marcus
Hartmann, Bernd Lorenz
Heinemann, Tim
Hoda, Mir Alireza
Hopfinger, Georg
Kazianka, Lukas
Kenner, Lukas
Kiesewetter, Barbara
Krall, Nikolaus
Kubicek, Stefan
Le, Trang
Lubowitzki, Simone
Mayerhoefer, Marius E.
Merkel, Olaf
Miura, Katsuhiro
Müllauer, Leonhard
Neumeister, Peter
Panny, Michael
Pichler, Alexander
Porpaczy, Edit
Prager, Gerald W.
Ristl, Robin
Schiefer, Ana-Iris
Schwarzinger, Ilse
Selzer, Edgar
Sperr, Wolfgang R.
Valent, Peter
van der Kouwe, Emiel
Vanura, Katrina
Waldstein, Cora
Wolf, Dominik
Zielinski, Christoph C.
Snijder, Berend
Staber, Philipp B.
Venue: Cancer Discovery
Publisher: American Association for Cancer Research (AACR)
Published: 2021-10-11
Volume: 12
Issue: 2
Pages: 372-387
ISSN: 2159-8274 (ISSN-L)
2159-8290
DOI: 10.1158/2159-8290.cd-21-0538
PMC: PMC9762339
OpenAlex: W3205761999
Cited by: 206
References: 43
Citations by year: 2026: 36 · 2025: 47 · 2024: 45 · 2023: 46 · 2022: 29 · 2021: 3
FWCI: 20.21
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor, 123 d)
Locations: Cancer Discovery (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Funders: Vienna Science and Technology Fund (LS16-034)
Austrian Science Fund (F4704-B20, F4711-B20, P27132-B20)
European Molecular Biology Organization (1543-2012, 733-2016)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (PP00P3_163961, PP00P3_194809, CRSII5_193832)
European Research Council
Topics: Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
Health Sciences › Medicine › Hematology › Chronic Lymphocytic Leukemia Research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords: functional precision medicine, hematologic malignancies, high-content imaging, drug response prediction, progression-free survival, exceptional responders
MeSH terms: Progression-Free Survival, Adult, Aged, Aged, 80 and over, Austria, Female, Humans, Male, Middle Aged, Cohort Studies, Hematologic Neoplasms, Young Adult, Precision Medicine, Molecular Targeted Therapy

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

Functional Precision Medicine Provides Clinical Benefit in Advanced Aggressive Hematologic Cancers and Identifies Exceptional Responders

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks