Patient-Derived Models of Prostate Cancer for Personalized Medicine
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PROVIDER: PRJNA1018661 | ENA |
REPOSITORIES: ENA
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Project description:Patient-derived lymphoma spheroids integrating immune tumor microenvironment as preclinical follicular lymphoma models for personalized medicine
| PRJNA1079718 | ENA
Project description:Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models
Natalie Kronik1¤, Yuri Kogan1, Moran Elishmereni1, Karin Halevi-Tobias1, Stanimir Vuk-Pavlovic ́2.,Zvia Agur1*.1Institute for Medical BioMathematics, Bene Ataroth, Israel,2College of Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
Abstract
Background:Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients.We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simplemathematical models.Methodology/Principal Findings:We developed a general mathematical model encompassing the basic interactions of avaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cellvaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes inprostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were dividedinto each patient’s training set and his validation set. The training set, used for model personalization, contained thepatient’s initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized modelswere simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set.The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients(the coefficient of determination between the predicted and observed PSA values wasR2= 0.972). The model could notaccount for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validatedpersonalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccinationregimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Conclusions/Significance:Using a few initial measurements, we constructed robust patient-specific models of PCaimmunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value andfeasibility of individualized model-suggested immunotherapy protocols.
2020-01-13 | MODEL2001130003 | BioModels
Project description:Patient-derived models of prostate cancer
| PRJNA1000652 | ENA
Project description:Patient-derived models of prostate cancer
| PRJNA1000653 | ENA
Project description:IGNITE: Genomic Medicine Implementation - The Personalized Medicine Program
| PRJNA606620 | ENA
Project description:IGNITE: Genomic Medicine Implementation - The Personalized Medicine Program
| PRJNA606621 | ENA
Project description:Background Follicular lymphoma (FL), the most common indolent non-Hodgkin’s Lymphoma, is a heterogeneous disease and a paradigm of the contribution of immune tumor microenvironment to disease onset, progression, and therapy resistance. Patient-derived models are scarce and fail to reproduce immune phenotypes and therapeutic responses. Methods To capture disease heterogeneity and microenvironment cues, we developed a patient-derived lymphoma spheroid (FL-PDLS) model culturing FL cells from lymph nodes (LN) with an optimized cytokine cocktail that mimics LN stimuli and maintains tumor cell viability. Results FL-PDLS, mainly composed of tumor B cells (60% on average) and autologous T cells (13% CD4 and 3% CD8 on average, respectively), rapidly organizes into patient-specific three-dimensional (3D) structures of three different morphotypes according to 3D imaging analysis. RNAseq analysis indicates that FL-PDLS reproduces FL hallmarks with the overexpression of cell cycle, BCR, or mTOR signaling related gene sets. FL-PDLS also recapitulates the exhausted immune phenotype typical of FL-LN, including expression of BTLA, TIGIT, PD-1, TIM-3, CD39 and CD73 on CD3+ T cells. These features render FL-PDLS an amenable system for immunotherapy testing. With this aim, we demonstrate that the combination of obinutuzumab (anti-CD20) and nivolumab (anti-PD1) reduces tumor load in a significant proportion of FL-PDLS. Interestingly, B cell depletion inversely correlates with the percentage of CD8+ cells positive for PD-1 and TIM-3. Conclusions In summary, FL-PDLS is a robust patient-derived 3D system that can be used as a tool to mimic FL pathology and to test novel immunotherapeutic approaches in a context of personalized medicine.
2024-02-28 | GSE256457 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE29996: Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine [Affymetrix SNP array data] GSE29998: Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine [Illumina mRNA expression array data] Refer to individual Series
2012-10-25 | E-GEOD-29999 | biostudies-arrayexpress
Project description:Developing animal models representating the cancer biology of advanced prostate cancer patients is challenging but essential for delivering individualized medical therapies. In an effort to develop patient derived xenograft (PDX) models, we took the metastatic site tissue from the rib lesion twice (ie, before and after enzalutamide treatment) over a twelve week period and implanted subcutaneously and under the renal capsule in immuno-deficient mice. To characterize and compare the genome and transcriptome landscapes of patient tumor tissues and the corresponding PDX models, we performed whole exome and transcriptome sequencing for metastatic tumor tissue as well as its derived PDXs. We demonstrated the feasibility of developping PDX models from patient who developed castrate-resistant prostate cancer. Our data suggested PDX models preserve the patient’s genomic and transcriptomic alterations in high fidelity, as illustrated by somatic mutation, copy number variation, gene fusion and gene expression. RNA sequencing of prostate cancer tumor tissue and derived xenograft using Illumina HiSeq 2000.
2015-06-30 | E-GEOD-70380 | biostudies-arrayexpress
Project description:Developing animal models representating the cancer biology of advanced prostate cancer patients is challenging but essential for delivering individualized medical therapies. In an effort to develop patient derived xenograft (PDX) models, we took the metastatic site tissue from the rib lesion twice (ie, before and after enzalutamide treatment) over a twelve week period and implanted subcutaneously and under the renal capsule in immuno-deficient mice. To characterize and compare the genome and transcriptome landscapes of patient tumor tissues and the corresponding PDX models, we performed whole exome and transcriptome sequencing for metastatic tumor tissue as well as its derived PDXs. We demonstrated the feasibility of developping PDX models from patient who developed castrate-resistant prostate cancer. Our data suggested PDX models preserve the patient’s genomic and transcriptomic alterations in high fidelity, as illustrated by somatic mutation, copy number variation, gene fusion and gene expression.
2015-06-30 | GSE70380 | GEO