Biology subjects

Lusby, R.

Publications and source records attributed to Lusby, R..

3 recordsLinked to original sources

bioRxiv2024

Tracing Endometriosis: Coupling deeply phenotyped, single-cell based Endometrial Differences and AI for disease pathology and prediction

Endometriosis, affecting 1 in 9 women, presents treatment and diagnostic challenges. To address these issues, we generated the biggest single-cell atlas of endometrial tissue to date, comprising 466,371 cells from 35 endometriosis and 25 non-endometriosis patients without exogenous hormonal treatment. Detailed analysis reveals significant gene expression changes and altered receptor-ligand interactions present in the endometrium of endometriosis patients, including increased inflammation, adhesion, proliferation, cell survival, and angiogenesis in various cell types. These alterations may enhance endometriosis lesion formation and offer novel therapeutic targets. Using ScaiVision, we developed neural network models predicting endometriosis of varying disease severity (median AUC = 0.83), including an 11-gene signature-based model (median AUC = 0.83) for hypothesis-generation without external validation. In conclusion, our findings illuminate numerous pathway and ligand-receptor changes in the endometrium of endometriosis patients, offering insights into pathophysiology, targets for novel treatments, and diagnostic models for enhanced outcomes in endometriosis management.

molecular biology↗

bioRxiv2024

Integrating single-cell transcriptomics with Artificial Intelligence reveals pan-cancer biomarkers of brain metastasis

Brain metastasis (BrM) is a serious complication of advanced cancers and remains difficult to predict before clinical symptoms appear. To investigate shared transcriptional features of BrM across tumour types, we integrated single-cell RNA sequencing (scRNA-seq) data from malignant epithelial cells derived from six carcinoma types, including lung, breast, colorectal, renal, ovarian, and melanoma. We applied ScaiVision, a supervised representation learning method, to classify tumour samples based on BrM status. The models achieved high predictive accuracy (area under the ROC curve > 0.90) across all six cancer types. This analysis identified a consistent multi-cancer gene expression signature associated with BrM, defined at single-cell resolution. To evaluate the clinical relevance of this signature, we assessed its presence in tumour-educated platelets (TEPs) from blood samples of patients with and without BrM. The signature was detectable in platelet RNA and distinguished patients with BrM from those without, indicating that features of the BrM-associated expression program are reflected in blood-derived material. These findings demonstrate that a transcriptional signature of brain metastasis can be identified across multiple tumour types using scRNA-seq and neural network-based analysis. The detectability of this signature in TEPs supports its relevance in a non-invasive context and provides a basis for further investigation into its utility for BrM risk assessment.

cancer biology↗

bioRxiv2023

Deciphering the gene regulatory circuitry governing chemoresistance in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, due in part to extensive intratumoral heterogeneity, high rates of metastasis and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of chemotherapy resistance in TNBC patients remains poorly understood. Here, using single-cell transcriptome datasets of matched longitudinal TNBC chemoresponsive and chemoresistant patient cohorts, we discover cell subpopulations associated with chemoresistance and the signature genes defining these populations. Notably, we show that the expression of these chemoresistance genes is driven via a set of TNBC super-enhancers and transcription factor networks across TNBC subtypes. Furthermore, genetic screens reveal that a subset of these transcription factors is essential for the survival of TNBC cells and their loss increases sensitivity to chemotherapeutic agents. Overall, our study has revealed transcriptional regulatory networks underlying chemoresistance and suggests novel avenues to stratify and improve the treatment of patients with a high risk of developing resistance.

cancer biology↗