Biology subjects

Leong, L.

Publications and source records attributed to Leong, L..

6 recordsLinked to original sources

bioRxiv2026

Engineering CAR-Vδ2 T cells to boost persistence and anti-tumor function

Chimeric antigen receptor (CAR)-modified V{delta}2 T cells are an attractive therapeutic cell platform for cancer immunotherapy. However, their clinical efficacy is limited by short in vivo persistence due to insufficient cytokine support and high susceptibility to activation-induced cell death (AICD). Through comparison of membrane-bound (mb) cytokines, we identified mbIL-18 to support superior anti-tumor activity of CAR-V{delta}2 T cells in vitro and in vivo. To reduce constitutive surface exposure of IL-18 and enable antigen-driven signal 3, we fused MyD88 - the key IL-18R signaling mediator - to an extracellular domain of Fas (Fas88). Antigen stimulation-induced FasL engagement of Fas88 triggered IL-18 signaling while simultaneously protecting V{delta}2 T cells from AICD. Fas88-armed human CAR-V{delta}2 T cells produced superior yet stimulation-dependent in vivo expansion and functional persistence in xenograft models of hematologic and solid malignancies. Together, these findings highlight the importance of IL-18 signaling and AICD resistance for CAR-V{delta}2 T cell activity, enabling a single-transgene modification to limit inflammatory risk and facilitate clinical translation.

synthetic biology↗

bioRxiv2025

Combined antagonism of Oncostatin M (OSM) and Interleukin 6 (IL-6) provides both anti-fibrotic and anti-inflammatory benefit in pulmonary fibrosis

Interstitial lung diseases (ILDs), including idiopathic pulmonary fibrosis (IPF) and systemic sclerosis-associated ILD (SSc-ILD), are irreversible fibrosing diseases with a mean survival time of less than 5 years for IPF. Poorly understood etiology and complex pathogenesis of these diseases have hampered the identification and development of effective therapeutics. Existing treatments can slow progressive fibrosis and lung function decline but do not stop it entirely, resulting in a minimal impact on patient survival. Thus, novel therapeutic interventions are needed. Tocilizumab, an anti-IL-6 receptor antibody, was recently approved by FDA for the treatment of SSc-ILD based on evidence demonstrating a reduction in the rate of lung function decline. In this study, we have characterized an IL-6-driven feed-forward myeloid axis contributing to lung inflammation providing a mechanistic hypothesis for tocilizumab. Concomitantly we have identified an oncostatin M (OSM) orchestrated lung injury response contributing to epithelial and endothelial cell disruption, myofibroblast activation and fibrosis. Despite dysregulated expression of IL11 in IPF, SSc-ILD, and murine models of fibrosis, we found no evidence for a pro-fibrotic role for IL-11 in vitro or in vivo. Instead, in pre-clinical models of IPF we demonstrate that antagonism of OSM alone, or to a greater degree in combination with IL-6, reduced lung fibrosis and inflammation. Translating these observations, we validated gp130:OSMR, rather than gp130:LIFR, as the dominant human receptor complex used by OSM, identifying OSMR as a potential therapeutic target to stall fibrosis.

immunology↗

bioRxiv2020

Cell-free DNA as a biomarker for prostate cancer: elevated concentration and decreased fragment size

PurposeProstate cancer is the most commonly diagnosed neoplasm in American men. Although existing biomarkers may detect localized prostate cancer, additional strategies are necessary for improving detection and identifying aggressive disease that may require further intervention. One promising, minimally invasive biomarker is cell-free DNA (cfDNA), which consist of short DNA fragments released into circulation by dying or lysed cells that may reflect underlying cancer. Here we investigated whether differences in cfDNA concentration and cfDNA fragment size could improve the sensitivity for detecting more advanced and aggressive prostate cancer. Materials and MethodsThis study included 268 individuals: 34 healthy controls, 112 men with localized prostate cancer who underwent radical prostatectomy (RP), and 122 men with metastatic castration-resistant prostate cancer (mCRPC). Plasma cfDNA concentration and fragment size were quantified with the Qubit 3.0 and the 2100 Bioanalyzer. The potential relationship between cfDNA concentration or fragment size and localized or mCRPC prostate cancer was evaluated with descriptive statistics, logistic regression, and area under the curve analysis with cross-validation. ResultsPlasma cfDNA concentrations were elevated in mCRPC patients in comparison to localized disease (OR5 ng/mL = 1.34, P = 0.027) or to being a control (OR5 ng/mL = 1.69, P = 0.034). Decreased average fragment size was associated with an increased risk of localized disease compared to controls (OR5bp = 0.77, P = 0.0008). ConclusionThis study suggests that cfDNA concentration and average cfDNA fragment size may provide a quick, cost-effective approach to help determine which patients will benefit most from further screening and/or disease monitoring to help improve prostate cancer outcomes.

cancer biology↗

bioRxiv2020

CytofRUV: Removing unwanted variation to integrate multiple CyTOF datasets

Mass cytometry (CyTOF) is a technology that has revolutionised single cell biology. One illuminating application of CyTOF has been in understanding the mechanisms of blood cancer resistance to therapy. Longitudinal studies of clinical cohorts during drug treatment provide a deeper understanding of the molecular changes that underlie sensitivity or resistance to treatment in each patient. However, understanding the biological impact of a cancer drug in such studies necessitates the integration of multiple CyTOF batches. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches which includes an R-Shiny application with diagnostics plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.

bioinformatics↗

bioRxiv2020

A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer

BackgroundCell-free DNAs (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings. MethodsWhole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (<200bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panels ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy. ResultsThe panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2,500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci. ConclusionMachine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients.

cancer biology↗

bioRxiv2020

Cross-Cancer Evaluation of Polygenic Risk Scores for 17 Cancer Types in Two Large Cohorts

Genetic factors that influence etiologic mechanisms shared across cancers could affect the risk of multiple cancer types. We investigated polygenic risk score (PRS)-specific pleiotropy across 17 cancers in two large population-based cohorts. The study population included European ancestry individuals from the Genetic Epidemiology Research on Adult Health and Aging cohort (16,012 cases, 50,552 controls) and the UK Biobank (48,969 cases, 359,802 controls). We selected known independent risk variants from published GWAS to construct a PRS for each cancer type. Within cohorts, each PRS was evaluated in multivariable logistic regression models with respect to the cancer for which it was developed and each other cancer type. Results were then meta-analyzed across cohorts. In the UK Biobank, each PRS was additionally evaluated relative to 20 cancer risk factors or biomarkers. All PRS replicated associations with their corresponding cancers (p<0.05). Eleven cross-cancer associations - ten positive and one inverse - were found after correction for multiple testing (p<0.05/17=0.0029). Two cancer pairs showed bidirectional associations; the melanoma PRS was positively associated with oral cavity/pharyngeal cancer and vice versa, whereas the lung cancer PRS was positively associated with oral cavity/pharyngeal cancer, and the oral cavity/pharyngeal cancer PRS was inversely associated with lung cancer. We identified 65 associations between a cancer PRS and non-cancer phenotype. In this study examining cross-cancer PRS associations in two cohorts unselected for phenotype, we validated known and uncovered novel patterns of pleiotropy. Our results have the potential to inform investigations of risk prediction, shared etiology, and precision cancer prevention strategies. STATEMENT OF SIGNIFICANCEBy examining cross-cancer polygenic risk score associations, we validated known and uncovered novel patterns of pleiotropy. Our results may inform investigations of risk prediction, shared etiology, and precision prevention strategies.

genetics↗