Publications

Innovation

ASH 24 Posters

GTC presented abstracts at ASH 2024 GTC’s posters from 2024 Convention of American Society of Hematology are now available to be downloaded. Please reach out

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ASCO 24 posters
Innovation

ASCO 24 Posters

GTC presented abstracts at ASCO 2024 GTC’s posters from 2024 Convention of American Society of Clinical Oncology are now available to be downloaded. Please reach

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Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation
Publications

Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation

Acute graft-versus-host disease (aGvHD) remains a major cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT), occurring to some degree in over 50% of patients and being a direct cause of death in about 20% of patients. This complication occurs even despite a better understanding of donor selection and GvHD prophylaxis regimens. aGvHD is a complex event in which multiple contributing factors are involved. We performed RNA transcriptome analysis of 1408 genes in bone marrow samples obtained before and after transplantation using machine learning to predict the risk of aGvHD and post-transplant survival for a cohort of patients undergoing HSCT. Differential gene expression identified several signaling pathways in the bone marrow microenvironment that may be major regulators of the complex biology of GvHD, and identified targets of intervention to ameliorate the risk of aGvHD and improve patient survival.

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Combining cell-free RNA with cell-free DNA in liquid biopsy forhematologic and solid tumors
Publications

Combining cell-free RNA with cell-free DNA in liquid biopsy for hematologic and solid tumors

Introducing a novel liquid biopsy approach combining cfRNA and cfDNA sequencing. Our findings demonstrate superior mutation detection with cfRNA, while cfDNA excels in identifying chromosomal aberrations. Elevated cfRNA biomarkers correlate with tumor types, aiding diagnosis. Machine learning predicts cancer types accurately. Host immune response analysis through cfRNA ratios reveals distinct patterns in cancer patients. This integrated approach holds promise for predicting genomic abnormalities and cancer diagnosis

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