Biology subjects

Ahmadi, Z.

Publications and source records attributed to Ahmadi, Z..

3 recordsLinked to original sources

bioRxiv2026

Detecting Extrachromosomal DNA from Routine Histopathology

Extrachromosomal DNA (ecDNA) is a major driver of oncogene amplification, tumour heterogeneity and poor clinical outcomes [1-3], yet its detection relies on specialised genomic assays that are not integrated into routine diagnostics. Here, we show that ecDNA status can be inferred directly from standard haematoxylin and eosin-stained whole-slide pathology images. We develop an end-to-end, weakly supervised deep learning framework that aggregates thousands of high-magnification patches per slide with slide-level augmentation and interpretable attention. Across twelve cancer types from The Cancer Genome Atlas, the approach identifies tumours with genomic amplifications and, critically, distinguishes ecDNA-amplified from chromosomally amplified or non-amplified tumours, with the strongest signal in glioblastoma. Attention maps localise regions enriched for nuclei with altered chromatin intensity and texture, and predicted ecDNA status recapitulates its adverse association with survival. These results indicate that ecDNA amplifications leave reproducible histomorphologic foot-prints detectable by routine pathology, enabling scalable screening to prioritise tumours for confirmatory molecular testing.

bioinformatics↗

bioRxiv2024

ecPath detects ecDNA in tumors from histopathology images

Circular extrachromosomal DNA (ecDNA) can drive tumor initiation, progression and resistance in some of the most aggressive cancers and is emerging as a promising anti-cancer target. However, detection currently requires costly whole-genome sequencing (WGS) or labor-intensive cytogenetic or FISH imaging, limiting its application in routine clinical diagnosis. To overcome this, we developed ecPath (ecDNA from histopathology), a computational method for predicting ecDNA status from routinely available hematoxylin and eosin (H&E) images. ecPath implements a deep-learning method we call transcriptomics-guided learning, which utilizes both transcriptomics and H&E images during the training phase to enable successful ecDNA prediction from H&E images alone, a task not achievable with models trained on H&E images only. It is trained on more than 6,000 tumor whole-slide images from the TCGA cohort with the best performance in predicting ecDNA status in brain and stomach tumors (average AUC=0.78). ecPath revealed that ecDNA-positive tumors are enriched with pleomorphic, larger and high-density nuclei. Testing in an independent cohort, ecPath predicted ecDNA status of 985 pediatric brain tumor patients with an AUC of 0.72. Finally, we applied ecPath to identify ecDNA-positive tumors in the TCGA cohort for which no WGS data were available. Like WGS-based ecDNA-positive labels, the predicted ecDNA-positive status also identify poor prognoses for low grade glioma patients. These results demonstrate that ecPath enables the detection of ecDNA from routinely available H&E imaging alone and help nominate aggressive tumors with ecDNA to study and target it.

pathology↗

bioRxiv2024

Antithrombotic Efficacy and Bleeding Risks of Vaccine-Induced Immune Thrombotic Thrombocytopenia Treatments

Current guidelines for treating vaccine-induced immune thrombotic thrombocytopenia (VITT) recommend non-heparin anticoagulants and intravenous immunoglobulin (IVIg). However, the efficacy of these treatments remains uncertain due to a lack of comparative clinical trials or animal studies. A recent study proposed danaparoid and heparin as potential VITT therapies due to their ability to disrupt VITT IgG-PF4 binding. Here, we examined the effects of various anticoagulants (including unfractionated (UF) heparin, danaparoid, bivalirudin, fondaparinux, and argatroban), IVIg, and the Fc{gamma}RIIa receptor-blocking antibody, IV.3, in relation to VITT pathophysiology. Our investigation focused on VITT IgG-PF4 binding, platelet activation, thrombocytopenia, and thrombosis. Danaparoid, at therapeutic doses, was the sole anticoagulant that reduced VITT IgG-PF4 binding, verified by purified anti-PF4 specific VITT IgG. Low-dose UF heparin (< 2U/mL) augmented VITT IgG binding to PF4 on platelets. While danaparoid and high-dose UF heparin (10 U/mL) inhibited platelet activation, none of the anticoagulants significantly affected thrombocytopenia in our VITT animal model, and all prolonged bleeding time. IVIg and all anticoagulants, except UF heparin, protected VITT mice from thrombosis. Direct Fc{gamma}RIIa receptor inhibition with IV.3 antibody proved the most effective approach for managing both thrombosis and thrombocytopenia in VITT. Our results underscore the necessity of animal model investigations to inform patient treatment strategies. This study provides compelling evidence for developing Fc{gamma}RIIa receptor blockers to treat VITT and other Fc{gamma}RIIa-related thrombotic inflammatory disorders. Key pointsO_LINon-heparin anticoagulants and IVIg reduce thrombosis in vivo by varying degrees whereas heparin exacerbates thrombosis. C_LIO_LIDirect blocking of Fc{gamma}RIIa receptor is the most effective strategy to treating both thrombosis and thrombocytopenia in VITT. C_LI

immunology↗