Biology subjects

Nagabushan, S.

Publications and source records attributed to Nagabushan, S..

3 recordsLinked to original sources

bioRxiv2025

Single-cell RNA-sequencing of cerebral spinal fluid identifies circulating tumour cells in children with brain cancer

Paediatric central nervous system (CNS) tumours are the leading cause of cancer-related death in children, yet disease monitoring remains challenging. Conventional approaches, including imaging and cytology, lack sensitivity, delaying intervention. Liquid biopsy offers a minimally invasive alternative, but the utility of circulating tumour cells (CTCs) in paediatric CNS tumours as biomarkers is poorly defined. We developed a CTC detection and characterisation workflow from cerebrospinal fluid (CSF) utilising single-cell RNA-sequencing (scRNA-seq) and applied this to ten CNS tumour subtypes in 16 patients. CTCs were identified in all cases, with higher burdens in pineoblastoma, medulloblastoma and atypical teratoid rhabdoid tumours. Longitudinal profiling revealed CTC dynamics correlated with clinical disease course and anticipated relapse. Critically, scRNA-seq uncovered a sub-clonal canonical driver alteration at diagnosis that only became detectable by bulk RNA-seq at progression, underscoring its potential to resolve clonal dynamics. This workflow enables real-time molecular profiling, offering a transformative strategy for disease monitoring and personalised therapy in paediatric brain tumours.

cancer biology↗

bioRxiv2025

Expanding the utility of transcriptome analysis for mutation detection in high-risk childhood precision oncology

In precision oncology, whole transcriptome sequencing (RNA-seq) excels at identifying oncogenic fusions. Here, using a cohort of 477 high-risk paediatric tumours, we demonstrate that RNA-seq can identify all mutation classes found previously using whole genome sequencing (WGS) and provides additional functional insights into their pathogenicity. By incorporating reference-guided fusion, and reference-free structural variant (SV) detection algorithms with RNA abundance assessment, RNA-seq identified 96% of SVs and resolved 33 complex SVs that WGS failed to identify. Furthermore, RNA-seq identified 92% of all single nucleotide variants and small insertions and deletions. Importantly RNA-seq informed the pathogenicity assessment in 22% of variants through identification of allele specific expression or the functional consequence of splice-altering variants. The utility of RNA-seq extends beyond fusion identification to the interpretation of mutation pathogenicity and the discovery of important mutations that would otherwise go undetected. We propose that RNA-seq is an indispensable companion to WGS in precision medicine.

genomics↗

bioRxiv2024

Federated deep learning enables cancer subtyping by proteomics

Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a Federated Deep Learning (FDL) approach (ProCanFDL), training local models on simulated sites containing data from a pan-cancer cohort (n=1,260) and 29 cohorts held behind private firewalls (n=6,265), representing 19,930 replicate data-independent acquisition mass spectrometry (DIA-MS) runs. Local parameter updates were aggregated to build the global model, achieving a 43% performance gain on the hold-out test set (n=625) in 14 cancer subtyping tasks compared to local models, and matching centralized model performance. The approachs generalizability was demonstrated by retraining the global model with data from two external DIA-MS cohorts (n=55) and eight acquired by tandem mass tag (TMT) proteomics (n=832). ProCanFDL presents a solution for internationally collaborative machine learning initiatives using proteomic data, e.g., for discovering predictive biomarkers or treatment targets, while maintaining data privacy. Statement of SignificanceA federated deep learning approach applied to human proteomic data, acquired using two distinct proteomic technologies from 40 tumor cohorts from eight countries, enabled accurate cancer histopathological subtyping while preserving data privacy. This approach will enable privacy-compliant development of large-scale proteomic AI models, including foundation models, across institutions globally.

cancer biology↗