Project description:We have generated a collection of patient-derived xenograft (PDX) tumor models and characterized them at the molecular level to facilitate precision oncology.
Project description:Patient-derived xenografts (PDX) and organoids (PDO) have been shown to model clinical response to cancer therapy. However, it remains challenging to use these models to guide timely clinical decisions for cancer patients. Here we used droplet emulsion microfluidics with temperature control and dead-volume minimization to rapidly generate thousands of Micro- Organospheres (MOS) from low-volume patient tissues, which serve as an ideal patient-derived model for clinical precision oncology. A clinical study of newly diagnosed metastatic colorectal cancer (CRC) patients using a MOS-based precision oncology pipeline reliably predicted patient treatment outcome within 14 days, a timeline suitable for guiding treatment decisions in clinic. Furthermore, MOS capture original stromal cells and allow T cell penetration, providing a clinical assay for testing immuno-oncology (IO) therapies such as PD-1 blockade, bispecific antibodies, and T cell therapies on patient tumors.
Project description:We have generated a collection of patient-derived xenograft (PDX) tumor models and characterized them at the molecular level to facilitate precision oncology. Surgically resected HCC specimens were subcutaneously implanted in immunodeficient mice. Resulting xenografts were serially implanted to establish transplantable PDX models, which were sequentially subject to whole exome sequencing (WES), gene expression array, genome-wide human single nucleotide polymorphism (SNP) array 6.0, and serum a–fetoprotein (AFP) detection assay. The feasibility as a preclinical model was validated by efficacy studies using a standard-of-care (SOC) and a targeted agent, respectively.
Project description:Method: In this study we use single cell RNA sequencing (scRNA-seq) to distinguish HB tumor cells from non-tumor cells and to identify distinct tumor cell types that account for the heterogeneity of HB. We also use a novel method to grow HB tumor cells as patient-specific spheroids (PDS) and show how this can be used to predict treatment response and identify novel therapeutic targets. Results: This study establishes that tumor heterogeneity can be defined by the relative proportions of five distinct subtypes of tumor cells. Notably, patient-derived HB spheroid cultures predict differential responses to treatment based on the transcriptomic signature of each tumor, suggesting a path forward for precision oncology for these tumors. Conclusions: These results define HB tumor heterogeneity with single-cell resolution and demonstrate that patient-derived spheroids can be used to evaluate responses to chemotherapy.
Project description:Precision oncology has made significant advances in the last few years, mainly by targeting actionable mutations in cancer driver genes. However, the proportion of patients whose tumors can be targeted therapeutically remains limited. Recent studies have begun to explore the benefit of analyzing tumor transcriptomics data to guide patient treatment, raising the need for new approaches for systematically accomplishing that. Here we show that computationally derived genetic interactions can successfully predict patient response.
Project description:DNA methylation profiling of heterogeneous head and neck squamous cell carcinoma (HNSCC) cohorts has been reported to predict patient outcome. We investigated if a prognostic DNA methylation profile could be found in tumour tissue from a single uniform subsite, the oral tongue. The methylation status of 83 comprehensively annotated oral tongue squamous cell carcinoma (OTSCC) formalin-fixed paraffin-embedded (FFPE) samples from a single institution were examined with the Illumina HumanMethylation450K (HM450K) array. 83 FFPE primary OTSCC tumour samples were analysed in one experimental run.
