Similar Datasets
Project description:Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is a leading cause of cancer related mortality worldwide. HCC occurs typically from a background of chronic liver disease, caused by a spectrum of predisposing conditions. Tumour development is driven by the expansion of clones that accumulated progressive driver mutations, with hepatocytes the most likely cell of origin. However, the landscape of driver mutations in HCC is independent of the underlying aetiologies. Despite an increasing range of systemic treatment options for advanced HCC outcomes remain heterogeneous and typically poor. Emerging data suggest that drug efficacies depend on disease aetiology and genetic alterations. Exploring subtypes in preclinical models with human relevance will therefore be essential to advance precision medicine in HCC. We generated over twenty-five new genetically-driven in vivo and in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance, and metastasis to distant organs. We integrated transcriptomic data from the mouse models with human HCC data and identified four common human-mouse subtype clusters. The subtype clusters had distinct transcriptomic characteristics that aligned with histopathology. In a proof-of-principle analysis, we verified response to standard of care treatment and used a linked in vitro-in vivo pipeline to identify a promising therapeutic candidate, cladribine, that has not been linked to HCC treatment before. Cladribine acts in a highly effective subtype-specific manner in combination with standard of care therapy.
2024-12-02 | GSE275443 | GEO
Project description:Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is a leading cause of cancer related mortality worldwide. HCC occurs typically from a background of chronic liver disease, caused by a spectrum of predisposing conditions. Tumour development is driven by the expansion of clones that accumulated progressive driver mutations, with hepatocytes the most likely cell of origin. However, the landscape of driver mutations in HCC is independent of the underlying aetiologies. Despite an increasing range of systemic treatment options for advanced HCC outcomes remain heterogeneous and typically poor. Emerging data suggest that drug efficacies depend on disease aetiology and genetic alterations. Exploring subtypes in preclinical models with human relevance will therefore be essential to advance precision medicine in HCC. We generated over twenty-five new genetically-driven in vivo and in vitro HCC models. Our models represent multiple features of human HCC, including clonal origin, histopathological appearance, and metastasis to distant organs. We integrated transcriptomic data from the mouse models with human HCC data and identified four common human-mouse subtype clusters. The subtype clusters had distinct transcriptomic characteristics that aligned with histopathology. In a proof-of-principle analysis, we verified response to standard of care treatment and used a linked in vitro-in vivo pipeline to identify a promising therapeutic candidate, cladribine, that has not been linked to HCC treatment before. Cladribine acts in a highly effective subtype-specific manner in combination with standard of care therapy.
2024-12-02 | GSE273806 | GEO
Project description:Human-correlated genetic HCC organoid models identify combination therapy for precision medicine
| PRJNA1150928 | ENA
Project description:Human-correlated genetic HCC and HCC-organoid models identify combination therapy for precision medicine
| PRJNA1150922 | ENA
Project description:Human-correlated genetic HCC models identify combination therapy for precision medicine
| PRJNA1143392 | ENA
Project description:Trastuzumab (Herceptin), a monoclonal antibody directed against the human epidermal growth-factor receptor 2 (HER2), is the poster child for antibody-based targeted therapy in breast cancer. Pertuzumab, another humanized monoclonal antibody, binds to a different domain of HER2 and prevents the formation of HER2:HER3 dimers, which is the most potent heterodimer in the HER family. The combination of trastuzumab and pertuzumab has synergistic activity, and is associated with improved clinical outcomes. The US Food and Drug Administration (FDA) approved pertuzumab in combination with trastuzumab-based chemotherapy originally as first-line therapy for metastatic HER2-positive breast cancer in 2012, and more recently as neoadjuvant therapy for localized disease in 2013. Pertuzumab is the first neoadjuvant drug to receive accelerated approval by the FDA based on pathological complete response as the primary end point. In this article, we review the mechanism of action, pharmacokinetics, clinical efficacy, safety, and current role of pertuzumab in the management of breast cancer, as well as ongoing clinical trials and future directions regarding the utility of pertuzumab as a personalized therapeutic option for HER2-positive breast cancer. In the coming years, we anticipate increased utilization of neoadjuvant trials for drug development, biomarker discovery, and validation, and envision conduct of personalized breast cancer clinics in which therapies will be routinely selected based on genetic alterations in the tumor. Regardless of the targeted therapy combinations employed based on tumor genomic profile, trastuzumab and pertuzumab will likely continue to form the backbone of the personalized regimen for HER2-positive breast cancer.
| S-EPMC3977457 | biostudies-literature
Project description:Rare monogenic disorders often share molecular etiologies involved in the pathogenesis of common diseases. Congenital disorders of glycosylation (CDG) and deglycosylation (CDDG) are rare pediatric disorders with symptoms that range from mild to life threatening. A biological mechanism shared among CDG and CDDG as well as more common neurodegenerative diseases such as Alzheimer's disease and amyotrophic lateral sclerosis, is endoplasmic reticulum (ER) stress. We developed isogenic human cellular models of two types of CDG and the only known CDDG to discover drugs that can alleviate ER stress. Systematic phenotyping confirmed ER stress and identified elevated autophagy among other phenotypes in each model. We screened 1049 compounds and scored their ability to correct aberrant morphology in each model using an agnostic cell-painting assay based on >300 cellular features. This primary screen identified multiple compounds able to correct morphological phenotypes. Independent validation shows they also correct cellular phenotypes and alleviate each of the ER stress markers identified in each model. Many of the active compounds are associated with microtubule dynamics, which points to new therapeutic opportunities for both rare and more common disorders presenting with ER stress, such as Alzheimer's disease and amyotrophic lateral sclerosis.
| S-EPMC8342410 | biostudies-literature
Project description:Li Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in TP53. TP53 is the most common mutated gene in human cancer, occurring in 30-50% of glioblastomas (GBM). Here, we highlight a precision medicine platform to identify potential targets for a GBM patient with LFS. We used a comparative transcriptomics approach to identify genes that are uniquely overexpressed in the LFS GBM patient relative to a cancer compendium of 12,747 tumor RNA sequencing data sets, including 200 GBMs. STAT1 and STAT2 were identified as being significantly overexpressed in the LFS patient, indicating ruxolitinib, a Janus kinase 1 and 2 inhibitors, as a potential therapy. The LFS patient had the highest level of STAT1 and STAT2 expression in an institutional high-grade glioma cohort of 45 patients, further supporting the cancer compendium results. To empirically validate the comparative transcriptomics pipeline, we used a combination of adherent and organoid cell culture techniques, including ex vivo patient-derived organoids (PDOs) from four patient-derived cell lines, including the LFS patient. STAT1 and STAT2 expression levels in the four patient-derived cells correlated with levels identified in the respective parent tumors. In both adherent and organoid cultures, cells from the LFS patient were among the most sensitive to ruxolitinib compared to patient-derived cells with lower STAT1 and STAT2 expression levels. A spheroid-based drug screening assay (3D-PREDICT) was performed and used to identify further therapeutic targets. Two targeted therapies were selected for the patient of interest and resulted in radiographic disease stability. This manuscript supports the use of comparative transcriptomics to identify personalized therapeutic targets in a functional precision medicine platform for malignant brain tumors.
| S-EPMC8699481 | biostudies-literature
Project description:Organoids are in vitro self-assembling, organ-like, three-dimensional cellular structures that stably retain key characteristics of the respective organs. Organoids can be generated from healthy or pathological tissues derived from patients. Cancer organoid culture platforms have several advantages, including conservation of the cellular composition that captures the heterogeneity and pharmacotypic signatures of the parental tumor. This platform has provided new opportunities to fill the gap between cancer research and clinical outcomes. Clinical trials have been performed using patient-derived organoids (PDO) as a tool for personalized medical decisions to predict patients' responses to therapeutic regimens and potentially improve treatment outcomes. Living organoid biobanks encompassing several cancer types have been established, providing a representative collection of well-characterized models that will facilitate drug development. In this review, we highlight recent developments in the generation of organoid cultures and PDO biobanks, in preclinical drug discovery, and methods to design a functional organoid-on-a-chip combined with microfluidic. In addition, we discuss the advantages as well as limitations of human organoids in patient-specific therapy and highlight possible future directions.
| S-EPMC8755639 | biostudies-literature
Project description:In this paper, we have used an agent-based stochastic tumor growth model and presented a mathematical and theoretical perspective to cancer therapy. This perspective can be used to theoretical study of precision medicine and combination therapy in individuals. We have conducted a series of in silico combination therapy experiments. Based on cancer drugs and new findings of cancer biology, we hypothesize relationships between model parameters which in some cases represent individual genome characteristics and cancer drugs, i.e., in our approach, therapy players are delegated by biologically reasonable parameters. In silico experiments showed that combined therapies are more effective when players affect tumor via different mechanisms and have different physical dimensions. This research presents for the first time an algorithm as a theoretical viewpoint for the prediction of effectiveness and classification of therapy sets.
| S-EPMC7471815 | biostudies-literature