A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE108004 | GEO | 2017/12/13
SECONDARY ACCESSION(S): PRJNA422201
REPOSITORIES: GEO
Similar Datasets
Project description:We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.
2017-11-30 | GSE107465 | GEO
Project description:Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene's potential to drive cancer. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone, and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.
| S-EPMC5752671 | biostudies-literature
Project description:We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.
2017-12-13 | GSE108003 | GEO
Project description:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
| PRJNA422201 | ENA
Project description:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [array]
| PRJNA420193 | ENA
Project description:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [RNA-Seq]
| PRJNA422203 | ENA
Project description:Acute myeloid leukemia (AML) is a highly heterogeneous cancer of the hematopoietic system with no cure for most patients. In addition to chemotherapy, treatment options for AML include recently approved therapies that target proteins with roles in AML pathobiology, such as FLT3, BLC2, and IDH1/2. However, due to disease complexity, these therapies produce very diverse responses, and survival rates are still low. Thus, despite considerable advances, there remains a need for therapies that target different aspects of leukemic biology and for associated biomarkers that define patient populations likely to respond to each available therapy. To meet this need, drugs that target different AML vulnerabilities are currently in advanced stages of clinical development. Here, we review proteomics and phosphoproteomics studies that aimed to provide insights into AML biology and clinical disease heterogeneity not attainable with genomic approaches. To place the discussion in context, we first provide an overview of genetic and clinical aspects of the disease, followed by a summary of proteins targeted by compounds that have been approved or are under clinical trials for AML treatment and, if available, the biomarkers that predict responses. We then discuss proteomics and phosphoproteomics studies that provided insights into AML pathogenesis, from which potential biomarkers and drug targets were identified, and studies that aimed to rationalize the use of synergistic drug combinations. When considered as a whole, the evidence summarized here suggests that proteomics and phosphoproteomics approaches can play a crucial role in the development and implementation of precision medicine for AML patients.
| S-EPMC10152134 | biostudies-literature
Project description: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.
| S-EPMC9762335 | biostudies-literature
Project description:Machine Learning research applied to the medical field is increasing. However, few of the proposed approaches are actually deployed in clinical settings. One reason is that current methods may not be able to generalize on new unseen instances which differ from the training population, thus providing unreliable classifications. Approaches to measure classification reliability could be useful to assess whether to trust prediction on new cases. Here, we propose a new reliability measure based on the similarity of a new instance to the training set. In particular, we evaluate whether this example would be selected as informative by an instance selection method, in comparison with the available training set. We show that this method distinguishes reliable examples, for which we can trust the classifier's prediction, from unreliable ones, both on simulated data and in a real-case scenario, to distinguish tumor and normal cells in Acute Myeloid Leukemia patients.
| S-EPMC8075526 | biostudies-literature
Project description:The epichaperome is a new cancer target composed of hyperconnected networks of chaperome members that facilitate cell survival. Cancers with an altered chaperone configuration may be susceptible to epichaperome inhibitors. We developed a flow cytometry-based assay for evaluation and monitoring of epichaperome abundance at the single cell level, with the goal of prospectively identifying patients likely to respond to epichaperome inhibitors, to measure target engagement, and dependency during treatment. As proof of principle, we describe a patient with an unclassified myeloproliferative neoplasm harboring a novel PML-SYK fusion, who progressed to acute myeloid leukemia despite chemotherapy and allogeneic stem cell transplant. The leukemia was identified as having high epichaperome abundance. We obtained compassionate access to an investigational epichaperome inhibitor, PU-H71. After 16 doses, the patient achieved durable complete remission. These encouraging results suggest that further investigation of epichaperome inhibitors in patients with abundant baseline epichaperome levels is warranted.
| S-EPMC8155064 | biostudies-literature