Biology subjects

Taj, F.

Publications and source records attributed to Taj, F..

3 recordsLinked to original sources

bioRxiv2026

Medulloblastoma Forms Symbiotic Metabolic Partnerships with Macrophages to Establish Leptomeningeal Metastases

Leptomeningeal metastases are the primary source of morbidity and mortality for pediatric medulloblastoma patients. Due to limited surgical sampling of metastases in patients, little is understood of the mechanisms of metastasis. Here, we identify biologically distinct quiescent small metastases (designated as micrometastases) and mitotically active larger metastases (macrometastases). Macrometastases are more metabolically active than micrometastases and contain higher levels of lipids, particularly cholesterol. Macrometastases secrete CXCL12, which attracts lipid-laden macrophages into the tumor. Lipid-laden macrophages upregulate the cholesterol transporter ABCG1, promoting the efflux of free cholesterol, which is then taken up by tumor cells via the HDL receptor SCARB1. CXCL12-driven macrophage recruitment and exogenous cholesterol are sufficient and necessary to drive progression of medulloblastoma leptomeningeal metastases in vivo. High fat diets drive metastatic progression in vivo. Dietary or pharmacological interventions targeting the CXCL12-SCARB1-cholesterol axis represent therapeutic strategies to either prevent or treat medulloblastoma leptomeningeal metastases.

cancer biology↗

bioRxiv2025

OmniPert: A Deep Learning Foundation Model for Predicting Responses to Genetic and Chemical Perturbations in Single Cancer Cells

In cancer, intra- and inter-patient heterogeneity presents a significant challenge for therapeutic management, as patients with apparently similar profiles often exhibit divergent responses to the same therapies. This heterogeneity is primarily attributed to genetic and molecular variations among individuals and their tumors. Understanding the impact of these differences on treatment outcomes is widely believed to be a key step for developing effective precision medicine strategies. However, the complexity of most biological pathways makes it difficult to predict the effect of genetic variation on cells and tissues, let alone predict a patients response to therapy. As a result, high-throughput genetic and chemical perturbation screens have emerged as valuable tools for precision medicine-related tasks, such as disease modeling, target discovery, cellular programming, and pathway reconstruction. This approach is fundamentally limited, however, because the number of possible combinations of cell types, cell states, perturbation targets, and perturbation types is huge and cannot be exhaustively tested experimentally. This calls for computational approaches that can simulate such experiments in silico, guiding in vitro experiments towards perturbations that are more likely to produce the desired effect. Here we describe OmniPert, a novel generative AI tool, which utilizes a deep learning, transformer-based architecture to model the effects of genetic and chemical perturbations on single-cell transcriptomes. Trained on millions of diverse cellular profiles, this approach allows for more granular analysis of cellular responses, thereby facilitating downstream applications in cell-specific gene-gene and gene-drug interaction networks, biomarker and drug target discovery, drug repurposing, and in silico perturbation reverse-engineering. In the context of oncology, OmniPert promises to facilitate the discovery of novel cell type- and state-specific targets, ultimately contributing to more effective and personalized cancer treatments.

bioinformatics↗

bioRxiv2023

Drug Response Prediction and Biomarker Discovery Using Multi-Modal Deep Learning

A major challenge in cancer care is that patients with similar demographics, tumor types, and medical histories can respond quite differently to the same drug regimens. This difference is largely explained by genetic and other molecular variabilities among the patients and their cancers. Efforts in the pharmacogenomics field are underway to understand better the relationship between the genome of the patients healthy and tumor cells and their response to therapy. To advance this goal, research groups and consortia have undertaken large-scale systematic screening of panels of drugs across multiple cancer cell lines that have been molecularly profiled by genomics, proteomics, and similar techniques. These large data drug screening sets have been applied to the problem of drug response prediction (DRP), the challenge of predicting the response of a previously untested drug/cell-line combination. Although deep learning algorithms outperform traditional methods, there are still many challenges in DRP that ultimately result in these models low generalizability and hampers their clinical application. In this paper, we describe a novel algorithm that addresses the major shortcomings of current DRP methods by combining multiple cell line characterization data, addressing drug response data skewness, and improving chemical compound representation. The result is an open-source, Python-based, command-line program available at https://github.com/LincolnSteinLab/MMDRP.

bioinformatics↗