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Biology subjects

Frias, S.

Publications and source records attributed to Frias, S..

3 recordsLinked to original sources

Transcriptional contingency in the early hematopoiesis of inherited bone marrow failure syndromes

The inherited bone marrow failure syndromes (IBMFS) are a group of heterogeneous genetic diseases caused by germline pathogenic variants in genes involved in hematopoiesis and genomic integrity. Despite sharing common phenotypical manifestations that have aided opportune diagnosis, such as bone marrow failure, congenital malformations and an increased predisposition to develop cancer, little is known about the shared and distinctive transcriptional programs in their hematopoietic stem and progenitor cells compartment that underlie the clinical manifestations of each disease. In this work we have successfully integrated and analyzed the publicly available scRNAseq datasets of HSPCs from patients with Fanconi anemia (FA) and Telomere Biology disorders (TBD), as well as healthy controls. Once integrated, we performed quality control metrics, as well as batch effect correction using different methods. Through dimensionality reduction, cell clustering and cell type annotation we found distinctive gene expression profiles per syndrome and per cell type, as well as orchestrated gene expression profiles, including upregulation of inflammatory pathways and cell proliferation pathways. This work shows the integration of datasets from FA and TBD, two major IBMFS, and provides the basis for an accumulative, comparative, and integrated atlas of scRNAseq data from patients with IBMFS.

genomics↗

Prediction of myeloid malignant cells in Fanconi anemia using machine learning

Fanconi anemia (FA) is an inherited bone marrow failure syndrome with cancer predisposition. Most FA patients develop aplastic anemia during childhood and have an extremely high cumulative risk to develop cancer during their lifespan. Myeloid malignancy is one of the main tumor risks for patients with FA, including high-risk myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). Although bone marrow transplantation is the treatment of choice for FA patients that develop aplastic anemia, patients with a more stable bone marrow remain at a high risk of presenting MDS/AML and should be monitored for appearance of myeloid malignant clones. Markers for an as-early-as-possible identification of emerging myeloid malignant cells are needed for the monitoring of patients with FA, since quick medical action after detection of neoplastic transformation is needed. In this work we have leveraged publicly available single cell RNA seq (scRNAseq) datasets of patients with MDS and AML for training deep neural networks (DNN). We have generated two machine learning models aimed to identify myeloid malignant transcriptional profiles in scRNAseq datasets from the bone marrow of patients with FA, one for detection of MDS and a second one for AML. Both predictors displayed high sensitivity, specificity, and accuracy for detection of single cell resolution myeloid malignant transcriptional profiles. Multiple tools for analysis of single cell transcriptional data were implemented to characterize the predicted MDS and AML cells. Our analysis suggests that the predicted MDS and AML cells from FA patients are enriched in the lympho-myeloid-primed progenitor (LMPP) and the granulocyte-monocyte progenitor (GMP) populations. The predicted MDS and AML cells have gene expression and master transcriptional factor profiles that suggest malignant transformation and that differ from the rest of FA cells. Also cues of immune evasion were detected using single cell pathway analysis (SCPA) and cell-cell communication profiles. Next work will be aimed to find potential cell surface markers on the predicted MDS and AML cells as well as to assess our predictions in primary samples from FA patients.

cancer biology↗

Large-scale topological disruption of chromosome territories 9 and 22 is associated with nonresponse to treatment in CML

Chronic Myeloid Leukemia (CML) is a myeloproliferative neoplasm defined by the presence of t(9;22) translocation whose origin has been associated with the tridimensional genome organization. This rearrangement leads to the fusion of BCR and ABL1 genes giving rise to a chimeric protein with constitutive kinase activity. Imatinib, a tyrosine kinase inhibitor (TKI), is used as a first-line treatment for CML, though approximately 40% of CML patients do not respond. Here, using structured illumination microscopy (SIM) and 3D reconstruction, we studied the 3D organization patterns of the ABL1 and BCR genes, and their chromosome territories (CTs) CT9 and CT22, in CD34+ cells from CML patients that responded or not to Imatinib. We found that TKI resistance in CML is associated with high levels of structural disruption of CT9 and CT22 in CD34+ cells, increased CT volumes (especially for CT22), intermingling between CT9 and CT22, and open-chromatin epigenetic mark, in CT22. Altogether our results suggest that large-scale disruption of CT9 and CT22 correlates with the clinical response of CML patients, which could be translated in a potential prognostic marker of response to treatment in this disease and provide novel insights into the mechanisms underlying resistance to TKI in CML.

cancer biology↗