Biology subjects

Hebbard, L.

Publications and source records attributed to Hebbard, L..

3 recordsLinked to original sources

bioRxiv2026

The Anti-Cancer Effects of Selected Indigenous Medicinal Plants of the Arid Bioregion

Ethnopharmacological relevance: Australian Indigenous medicinal plants represent a valuable yet underexplored source of bioactive compounds with potential therapeutic relevance. The Iningai community of Central Queensland has traditionally used native plants to manage conditions associated with inflammation, pain, infection, and general illness. Scientific evaluation of these plants may provide evidence for their customary applications and identify bioactivities relevant to anticancer biodiscovery. Aim of the study: This study evaluated leaf and stem extracts of seven medicinal plants-Pittosporum angustifolium, Alphitonia excelsa, Calytrix microcoma, Geijera parviflora, Melaleuca uncinata, Gossypium australe, and Eucalyptus similis-traditionally used by the Iningai community, focusing on three biological processes relevant to cancer: oxidative stress, inflammation, and cellular proliferation. Materials and methods: Antioxidant activity was assessed using DPPH radical-scavenging and ferric reducing antioxidant power (FRAP) assays. Anti-inflammatory activity was evaluated in lipopolysaccharide (LPS)-stimulated THP-1 macrophage-like cells by profiling IFN-, TNF-, IL-6, IL-12, IL-18, and IL-23. Antiproliferative activity was assessed using MTT-based viability assays in human and murine liver cancer cell lines (Huh7, Hep3B, Hep-55.1c, and A52). Results: The extracts exhibited distinct biological activity profiles. G. parviflora stem and C. microcoma leaf extracts showed the strongest antioxidant activities, whereas P. angustifolium stem exhibited the weakest radical-scavenging capacity. Cytokine responses were extract-specific, with E. similis leaf extract demonstrating broad and pronounced suppression of multiple LPS-induced pro-inflammatory cytokines. Several extracts produced concentration-dependent reductions in liver cancer cell viability, with P. angustifolium stem exhibiting the most consistent and potent antiproliferative activity across the cell lines tested. Notably, strong antioxidant or anti-inflammatory activity did not necessarily correspond with antiproliferative activity. Conclusion: Australian Indigenous medicinal plant extracts demonstrated distinct antioxidant, immunomodulatory, and antiproliferative activities rather than uniform bioactivity across experimental systems. The divergent activities of G. parviflora, C. microcoma, E. similis, and P. angustifolium highlight the importance of integrated biological screening and support the value of Indigenous knowledge-guided biodiscovery. These plants represent promising sources for further investigation of selective bioactive compounds with potential relevance to anticancer drug discovery.

cancer biology↗

bioRxiv2026

A comprehensive benchmark of transcriptome-wide fusion detection using long-read RNA sequencing

Fusion transcripts contribute to cancer, inherited diseases, developmental disorders, and evolution. Long-read RNA sequencing enables direct sequencing of full-length transcripts, creating new opportunities to detect complex fusion architectures, including previously inaccessible multi-segmented fusion transcripts. However, accurate transcriptome-wide fusion detection remains challenging because existing methods struggle to distinguish genuine fusion events from technical artefacts. Here, we present a comprehensive benchmark of transcriptome-wide fusion detection using simulated datasets and transcriptomes from three cancer cell lines across Oxford Nanopore Technologies (ONT) cDNA, PCR-cDNA, and direct RNA sequencing, Pacific Biosciences (PacBio) Kinnex sequencing, Illumina short-read RNA sequencing, six long-read fusion callers, and multiple analysis strategies. False-positive fusion calls remained the dominant limitation across sequencing platforms and algorithms. Increasing sequencing depth improved recall but also amplified spurious fusion calls, whereas higher read-support thresholds improved precision at the expense of sensitivity. ONT PCR-cDNA sequencing combined with CTAT-LR-Fusion achieved the best overall balance between precision and recall, whereas JAFFAL was the only caller to reliably identify simulated tri-gene fusions. Consensus calling reduced false positives but markedly reduced sensitivity, with only one of 400 simulated fusions detected by all six callers. Breakpoint localisation emerged as a major limitation across all methods. Long-read sequencing consistently recovered more validated fusion transcripts than short-read sequencing, enabled detection of complex tri-gene fusions, and produced more biologically plausible fusion landscapes with fewer promiscuous gene partners. Collectively, our results establish the first comprehensive benchmarking framework for transcriptome-wide fusion detection, using long-read RNA sequencing, and provide practical guidance for selecting sequencing workflows and computational strategies, while identifying key priorities for future algorithm development.

bioinformatics↗

bioRxiv2026

Advancing Liver Cancer Precision Medicine with TARGET-SL

Background and AimsA major goal of personalised liver oncology is the ability to make targeted predictions about cancer-specific toxicity, however there are limited methods available. To address this, we validated the performance of our bioinformatics framework, TARGET-SL, through ex vivo drug screening. MethodsUsing TARGET-SL we predicted gain of function (GOF), loss of function (LOF) and synthetic lethal (SL) genetic events, and corresponding drug candidates. We validated drug predictions across hepatocellular carcinoma (HCC) cell lines, and a cohort of HCC and cholangiocarcinoma (CCA) patient-derived organoids (PDOs). ResultsFor HCC cells and PDOs we found 37.5% and 25% of the respective selected compounds induced unique target-specific growth inhibition based on genetic biomarkers, suggesting novel biomarker-driven drug sensitivities. ConclusionsOur analyses demonstrate TARGET-SLs potential to enhance personalized drug screening for liver cancer, by focusing on genetically informed targets. This will reduce experimental costs and accelerate the pace of therapeutic discovery. Impact and ImplicationsPrimary liver cancer (PLC) is a cancer with poor prognosis, and current therapies increase survival only for a minority of patients. Through the application of TARGET-SL we can predict, for each patient, the essential genes and corresponding small molecule inhibitors. These data support further investigation in larger patient cohorts and offer the possibility to specify new small molecule inhibitors and to repurpose current drugs for PLC treatment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/725819v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@10cb252org.highwire.dtl.DTLVardef@8f3049org.highwire.dtl.DTLVardef@ab4467org.highwire.dtl.DTLVardef@17f9d3_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITARGET-SL can predict gene and drug sensitivities for cell lines and patient-derived organoids C_LIO_LIThis may reduce drug screening costs and accelerate the pace of therapeutic discovery. C_LIO_LITARGET-SL may assist in the repurposing of current drugs and their rapid translation for primary liver cancer C_LIO_LITARGET-SL is tumour-type agnostic, and therefore may have application in other cancers with poor prognosis C_LI

cancer biology↗