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Hafezi-Bakhtiari, S.

Publications and source records attributed to Hafezi-Bakhtiari, S..

2 recordsLinked to original sources

A Pan-Cancer Single-Cell Compendium of Intratumoural Heterogeneity.

Intratumoural heterogeneity remains one of the most formidable challenges in oncology, driving treatment resistance, disease progression, and poor patient outcomes. To uncover the cellular programs that define tumour composition and diversity, we interrogated over 3.6 million single cells derived from more than 1,000 primary and metastatic tumours spanning 14 diverse cancer types. Through the identification and quantification of coordinated gene expression programs, we delineated heterogeneous cancer cell states, exhibiting unique and clinically significant associations with patient survival. Integration with high-resolution spatial transcriptomics identified prognostic cancer cell states marked by aggressive transcriptional programs, co-localizing with invasive histological features and significantly enriched in metastatic settings. Functional drug perturbation screening uncovered drug vulnerabilities specific to distinct cancer cell states, supporting both direct targeting strategies and therapeutic reprogramming toward less aggressive cellular phenotypes. Collectively, this study offers novel insights into the relationship between intratumoural transcriptional heterogeneity, spatial organization, clinical outcomes, and therapeutic vulnerabilities.

cancer biology↗

Meta-analysis of 1,200 transcriptomic profiles identifies a prognostic model for pancreatic ductal adenocarcinoma

BackgroundWith a dismal 8% median 5-year overall survival (OS), pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Only 10-20% of patients are eligible for surgery, and over 50% of these will die within a year of surgery. Identify molecular predictors of early death would enable the selection of PDAC patients at high risk.\n\nMethodsWe developed the Pancreatic Cancer Overall Survival Predictor (PCOSP), a prognostic model built from a unique set of 89 PDAC tumors where gene expression was profiled using both microarray and sequencing platforms. We used a meta-analysis framework based on the binary gene pair method to create gene expression barcodes robust to biases arising from heterogeneous profiling platforms and batch effects. Leveraging the largest compendium of PDAC transcriptomic datasets to date, we show that PCOSP is a robust single-sample predictor of early death ([≤]1 yr) after surgery in a subset of 823 samples with available transcriptomics and survival data.\n\nResultsThe PCOSP model was strongly and significantly prognostic with a meta-estimate of the area under the receiver operating curve (AUROC) of 0.70 (P=1.9e-18) and hazard ratio (HR) of 1.95(1.6-2.3) (P=2.6e-16) for binary and survival predictions, respectively. The prognostic value of PCOSP was independent of clinicopathological parameters and molecular subtypes. Over-representation analysis of the PCOSP 2619 gene-pairs (1070 unique genes) unveiled pathways associated with Hedgehog signalling, epithelial mesenchymal transition (EMT) and extracellular matrix (ECM) signalling.\n\nConclusionPCOSP could improve treatment decision by identifying patients who will not benefit from standard surgery/chemotherapy and may benefit from alternate approaches.\n\nAbbreviations

genomics↗