Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The cellular hierarchy of acute myeloid leukemia informs personalized treatment

ABSTRACT: Acute myeloid leukemia (AML) is characterized by malignant myeloid precursors that span a cellular hierarchy from dedifferentiated leukemic stem cells to mature blasts. While the diagnostic and prognostic importance of AML blast maturation is increasingly recognized, personalized therapies are currently not tailored to a patient’s individual makeup of this cellular hierarchy. In this study, we use multiplexed image-based ex vivo drug screening (pharmacoscopy) to systematically quantify the drug sensitivity across the cellular hierarchy of AML patients. We analyzed 174 prospective and longitudinal patient samples from 44 newly diagnosed AML patients, which indicated that differences in the AML hierarchy significantly identified poor responses to first-line therapy, outperforming European LeukemiaNet (ELN) criteria. Critically, drug response profiling across the AML hierarchy of each patient improved the accuracy of predicting patient response to first-line therapy (AUC 0.91), and revealed alternative individualized treatment options targeting the complete AML hierarchy of non-responding patients. We confirmed these findings in an independent cohort of 26 relapsed/refractory AML patients, for whom pan-hierarchy response profiling improved response predictions post hoc. Overall, our results quantify the clinical importance of therapeutically targeting the complete cellular hierarchy of newly diagnosed AML, and identify multiplexed image-based ex vivo drug screening to enable quantification and targeting of the AML maturation hierarchy for improved personalized treatment.

ORGANISM(S): Homo sapiens

PROVIDER: GSE272681 | GEO | 2024/07/20

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Deconvolution of Cell Type-Specific Drug Responses in Human Tumor Tissue with Single-Cell RNA-seq

Project description:Precision oncology requires the timely selection of effective drugs or drug combinations for individual patients. The ideal platform would enable rapid screening of cell type-specific drug sensitivities directly in patient tumor tissue and reveal strategies to overcome intratumoral heterogeneity. Here we combine multiplexed drug perturbation in acute slice culture from freshly resected tumors with single-cell RNA sequencing (scRNA-seq) to profile transcriptome-wide drug responses in individual patients. We applied this approach to glioblastoma (GBM) and demonstrated that acute slice cultures recapitulate the cellular and molecular features of the originating tumor tissue. Detailed investigation of etoposide, a topoisomerase poison, and the histone deacetylase (HDAC) inhibitor Panobinostat in acute slice cultures revealed cell type-specific responses across multiple patients, including unexpected effects on the immune microenvironment. We anticipate that this approach will facilitate rapid, personalized drug screening to identify effective therapies for solid tumors.

2020-04-18 | GSE148842 | GEO

Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction

Project description:Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, patient-specific genomic and transcriptomic data with a prior-knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.

2024-03-22 | GSE247483 | GEO

Image-based profiling identifies compound induced phenotypes in patient-derived organoids

Project description:Patient derived organoids (PDOs) closely resemble individual tumor biology. They are thus promising models for drug discovery and precision medicine. Here, we describe high-throughput imaging and automated image analysis of PDOs. We generated PDOs from colorectal cancer patients. Subsequently, we treated them with >500 substances to capture almost 6 million images by confocal microscopy. We developed a software framework to analyze how perturbations alter the organization of multicellular PDOs. Therewith, we observed a rich spectrum of reoccurring phenotypes. Targeting cellular processes, including signaling by MEK, GSK3 or CDKs, led to distinct architectural changes. Also, we detected compound-induced phenotypes only present in subsets of PDOs with specific molecular alterations. Finally, PDO response to anticancer drugs matched the clinical course of corresponding patients. The presented high-throughput imaging workflow and data allow compound profiling with complex multicellular organoid models for drug discovery and personalized medicine. We used microarrays to detail the global programme of gene expression underlying different lines of patient-derived colorectal cancer organoids.

2019-03-03 | GSE117548 | GEO

RNA sequencing of patient-derived AML xenografts treated with Fedratinib

Project description:The treatment landscape of AML is evolving with promising therapies entering clinical translation, yet patient responses remain heterogeneous and biomarkers for tailoring treatment are lacking. To understand how disease heterogeneity links with therapy response, we determined the leukemia cell hierarchy make-up from bulk transcriptomes of over 1000 patients through deconvolution using single-cell reference profiles of leukemia stem, progenitor, and mature cell types. Leukemia hierarchy composition was associated with functional, genomic, and clinical properties and converged into four overall classes, spanning Primitive, Mature, GMP, and Intermediate. Critically, variation in hierarchy composition along the Primitive vs GMP or Primitive vs Mature axes were associated with response to chemotherapy or drug sensitivity profiles of targeted therapies, respectively. A 7-gene biomarker derived from the Primitive vs Mature axis was predictive of patient response to 105 investigational drugs. Thus, hierarchy composition constitutes a novel framework for understanding disease biology and advancing precision medicine in AML.

2022-05-03 | GSE199456 | GEO

RNA sequencing of primary AML samples prior to in vivo treatment with Fedratinib

2022-05-03 | GSE199455 | GEO

RNA sequencing of patient-derived AML xenografts treated with Selinexor (KPT-330)

2022-05-03 | GSE199454 | GEO

Bulk RNA-seq profiles of primary AML samples corresponding to existing scRNA-seq data from GSE116256

2022-05-03 | GSE199453 | GEO

RNA sequencing of functionally validated LSC fractions in AML

2022-05-03 | GSE199452 | GEO

RNA sequencing of paired AML samples at diagnosis and relapse

2022-05-03 | GSE199451 | GEO

Uncovering the hidden threat: single-organoid analysis reveals clinically relevant treatment-resistant and invasive subclones in pancreatic cancer

Project description:Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal diseases, characterized by a treatment-resistant and invasive nature. In line with these inherent aggressive characteristics, only a subset of patients shows a clinical response to the standard of care therapies, thereby highlighting the need for a more personalized treatment approach. In this study, we comprehensively unraveled the intra-patient response heterogeneity and intrinsic aggressive nature of PDAC on bulk and single-organoid resolution. We leveraged a fully characterized PDAC organoid panel (N=8) and matched our artificial intelligence-driven, live-cell organoid image analysis with retrospective clinical patient response. In line with the clinical outcomes, we identified patient-specific sensitivities to the standard of care therapies (gemcitabine-paclitaxel and FOLFIRINOX) using a growth rate-based and normalized drug response metric. Moreover, the single-organoid analysis was able to detect resistant as well as invasive PDAC organoid clones, which was orchestrates on a patient, therapy, drug, concentration and time-specific level. Furthermore, our in vitro organoid analysis indicated a strong correlation with the matched patient progression-free survival (PFS) compared to the current, conventional drug response readouts. This work not only provides valuable insights on the response complexity in PDAC, but it also highlights the potential applications (extendable to other tumor types) and clinical translatability of our approach in drug discovery and the emerging era of personalized medicine.

2023-07-31 | GSE235548 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data