| 1. |
- Leo, Isabelle Rose, et al.
(författare)
-
Integrative multi-omics and drug response profiling of childhood acute lymphoblastic leukemia cell lines
- 2022
-
Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
-
Tidskriftsartikel (refereegranskat)abstract
- Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Although standard-of-care chemotherapeutics are sufficient for most ALL cases, there are subsets of patients with poor response who relapse in disease. The biology underlying differences between subtypes and their response to therapy has only partially been explained by genetic and transcriptomic profiling. Here, we perform comprehensive multi-omic analyses of 49 readily available childhood ALL cell lines, using proteomics, transcriptomics, and pharmacoproteomic characterization. We connect the molecular phenotypes with drug responses to 528 oncology drugs, identifying drug correlations as well as lineage-dependent correlations. We also identify the diacylglycerol-analog bryostatin-1 as a therapeutic candidate in the MEF2D-HNRNPUL1 fusion high-risk subtype, for which this drug activates pro-apoptotic ERK signaling associated with molecular mediators of pre-B cell negative selection. Our data is the foundation for the interactive online Functional Omics Resource of ALL (FORALL) with navigable proteomics, transcriptomics, and drug sensitivity profiles at https://proteomics.se/fora.
|
|
| 2. |
- Mou, Tian, et al.
(författare)
-
The transcriptome-wide landscape of molecular subtype-specific mRNA expression profiles in acute myeloid leukemia
- 2021
-
Ingår i: American Journal of Hematology. - : John Wiley & Sons. - 0361-8609 .- 1096-8652. ; 96:5, s. 580-588
-
Tidskriftsartikel (refereegranskat)abstract
- Molecular classification of acute myeloid leukemia (AML) aids prognostic stratification and clinical management. Our aim in this study is to identify transcriptome-wide mRNAs that are specific to each of the molecular subtypes of AML. We analyzed RNA-sequencing data of 955 AML samples from three cohorts, including the BeatAML project, the Cancer Genome Atlas, and a cohort of Swedish patients to provide a comprehensive transcriptome-wide view of subtype-specific mRNA expression. We identified 729 subtype-specific mRNAs, discovered in the BeatAML project and validated in the other two cohorts. Using unique proteomics data, we also validated the presence of subtype-specific mRNAs at the protein level, yielding a rich collection of potential protein-based biomarkers for the AML community. To enable the exploration of subtype-specific mRNA expression by the broader scientific community, we provide an interactive resource to the public.
|
|
| 3. |
- Struyf, Nona, et al.
(författare)
-
Delineating functional and molecular impact of ex vivo sample handling in precision medicine
- 2024
-
Ingår i: npj Precision Oncology. - : Springer Nature. - 2397-768X. ; 8:1
-
Tidskriftsartikel (refereegranskat)abstract
- Consistent handling of samples is crucial for achieving reproducible molecular and functional testing results in translational research. Here, we used 229 acute myeloid leukemia (AML) patient samples to assess the impact of sample handling on high-throughput functional drug testing, mass spectrometry-based proteomics, and flow cytometry. Our data revealed novel and previously described changes in cell phenotype and drug response dependent on sample biobanking. Specifically, myeloid cells with a CD117 (c-KIT) positive phenotype decreased after biobanking, potentially distorting cell population representations and affecting drugs targeting these cells. Additionally, highly granular AML cell numbers decreased after freezing. Secondly, protein expression levels, as well as sensitivity to drugs targeting cell proliferation, metabolism, tyrosine kinases (e.g., JAK, KIT, FLT3), and BH3 mimetics were notably affected by biobanking. Moreover, drug response profiles of paired fresh and frozen samples showed that freezing samples can lead to systematic errors in drug sensitivity scores. While a high correlation between fresh and frozen for the entire drug library was observed, freezing cells had a considerable impact at an individual level, which could influence outcomes in translational studies. Our study highlights conditions where standardization is needed to improve reproducibility, and where validation of data generated from biobanked cohorts may be particularly important.
|
|
| 4. |
- Trac, Quang Thinh, et al.
(författare)
-
Prediction model for drug response of acute myeloid leukemia patients
- 2023
-
Ingår i: npj Precision Oncology. - : Springer Nature. - 2397-768X. ; 7
-
Tidskriftsartikel (refereegranskat)abstract
- Despite some encouraging successes, predicting the therapy response of acute myeloid leukemia (AML) patients remains highly challenging due to tumor heterogeneity. Here we aim to develop and validate MDREAM, a robust ensemble-based prediction model for drug response in AML based on an integration of omics data, including mutations and gene expression, and large-scale drug testing. Briefly, MDREAM is first trained in the BeatAML cohort (n = 278), and then validated in the BeatAML (n = 183) and two external cohorts, including a Swedish AML cohort (n = 45) and a relapsed/refractory acute leukemia cohort (n = 12). The final prediction is based on 122 ensemble models, each corresponding to a drug. A confidence score metric is used to convey the uncertainty of predictions; among predictions with a confidence score >0.75, the validated proportion of good responders is 77%. The Spearman correlations between the predicted and the observed drug response are 0.68 (95% CI: [0.64, 0.68]) in the BeatAML validation set, -0.49 (95% CI: [-0.53, -0.44]) in the Swedish cohort and 0.59 (95% CI: [0.51, 0.67]) in the relapsed/refractory cohort. A web-based implementation of MDREAM is publicly available at https://www.meb.ki.se/shiny/truvu/MDREAM/.
|
|
