Biology subjects

Floisand, Y.

Publications and source records attributed to Floisand, Y..

3 recordsLinked to original sources

bioRxiv2022

Risk Stratification of Acute Myeloid Leukemia Using Ex Vivo Drug Sensitivity Profiling

Acute Myeloid Leukemia (AML) is a heterogeneous malignancy involving the clonal expansion of myeloid stem and progenitor cells in the bone marrow and peripheral blood. Most AML patients eligible for potentially curative treatment receive intensive chemotherapy. Risk stratification is used to optimize treatment intensity and transplant strategy, and is mainly based on cytogenetic screening for structural chromosomal alterations and targeted sequencing of a selection of common mutations. However, the forecasting accuracy of treatment response remains modest. Recently, ex vivo drug screening has gained traction for its potential in personalized treatment selection, as well as a tool for identifying and mapping patient groups based on relevant cancer dependencies. We systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can accurately assess risk of individual AML patients and may guide clinical decision-making.

bioinformatics↗

bioRxiv2022

Identifying predictors of survival in patients with leukemia using single-cell mass cytometry and machine learning

Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterisation of cellular heterogeneity in cancer and other diseases. However, computational identification of cell populations from CyTOF, and utilisation of single cell data for biomarker discoveries faces several technical limitations, and although some computational approaches are available, high-dimensional analyses of single cell data remains quite demanding. Here, we deploy a bioinformatics framework that tackles two fundamental problems in CyTOF analyses namely: a) automated annotation of cell populations guided by a reference dataset, and b) systematic utilisation of single cell data for more effective patient stratification. By applying this framework on several publicly available datasets, we demonstrate that the Scaffold approach achieves good tradeoff between sensitivity and specificity for automated cell type annotation. Additionally, a case study focusing on a cohort of 43 leukemia patients, reported salient interactions between signalling proteins that are sufficient to predict short-term survival at time of diagnosis using the XGBoost algorithm. Our work introduces an automated and versatile analysis framework for CyTOF data with many applications in future precision medicine projects. Datasets and codes are publicly available at: https://github.com/dkleftogi/singleCellClassification

cancer biology↗

bioRxiv2020

A cell competition-based drug screen identifies a novel compound that induces dual c-Myc degradation and p53 activation

BCR-Abl is a driver oncogene that causes chronic myeloid leukemia and a subset of acute lymphoid leukemias. Although tyrosine kinase inhibitors provide an effective treatment for these diseases, they generally do not kill leukemic stem cells. Leukemic stem cells are cancer-initiating cells that compete with normal hematopoietic stem cells for the bone marrow niche. Using BCR-Abl as a model oncogene, we performed a drug screen based on competition between isogenic untransformed cells and BCR-Abl-transformed cells, and identified several compounds that selectively target BCR-Abl-transformed cells. Systems-level analysis of one of these novel compounds, DJ34, revealed that it induced depletion of c-Myc and activation of p53. c-Myc depletion occurred in a wide range of tumor types, including leukemia, lymphoma, lung, glioblastoma and breast cancer. Further analyses revealed that DJ34 interferes with c-Myc synthesis at the level of transcription, and we provide data showing that DJ34 is a DNA intercalator and topoisomerase II inhibitor. Physiologically, DJ34 induced apoptosis, cell cycle arrest and cell differentiation, and primary leukemic stem cells were particularly sensitive to DJ34. Taken together, we have identified a novel compound that dually targets c-Myc and p53 in a wide variety of cancers, and with particularly strong activity against leukemic stem cells.

cancer biology↗