Patient-derived xenograft platform for metastatic melanoma
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PROVIDER: PRJNA297786 | ENA |
REPOSITORIES: ENA
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Project description:Patient-derived xenograft platform for metastatic melanoma: RNA sequencing of 95 melanoma PDX samples
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Project description:The therapeutic landscape of melanoma is rapidly changing. While targeted inhibitors yield significant responses, their clinical benefit is often limited by the early onset of drug resistance. This motivates the pursuit to establish more durable clinical responses, by developing combinatorial therapies. But while potential new combinatorial targets steadily increase in numbers, they cannot possibly all be tested in patients. Similarly, while genetically engineered mouse melanoma models have great merit, they do not capture the enormous genetic diversity and heterogeneity typical in human melanoma. Furthermore, whereas in vitro studies have many advantages, they lack the presence of micro-environmental factors, which can have a profound impact on tumor progression and therapy response. This prompted us to develop an in vivo model for human melanoma that allows for studying the dynamics of tumor progression and drug response, with concurrent evaluation and optimization of new treatment regimens. Here, we present a collection of patient-derived xenografts (PDX), derived from BRAFV600E, NRASQ61 or BRAFWT/NRASWT melanoma metastases. The BRAFV600E PDX melanomas were acquired both prior to treatment with the BRAF inhibitor vemurafenib and after resistance had occurred, including six matched pairs. We find that PDX resemble their human donors’ melanomas regarding biomarkers, chromosomal aberrations, RNA expression profiles, mutational spectrum and targeted drug resistance patterns. Mutations, previously identified to cause resistance to BRAF inhibitors, are captured in PDX derived from resistant melanomThis melanoma PDX platform represents a comprehensive public resource to study both fundamental and translational aspects of melanoma progression and treatment in a physiologically relevant setting.
2016-05-27 | GSE73737 | GEO
Project description:Although high clinical response rates are seen for immune checkpoint blockade (ICB) of metastatic melanoma, both intrinsic and acquired ICB resistance remain formidable challenges. Combination ICB shows improved clinical benefit, but is associated with severe adverse events and exceedingly high cost. Therefore, there is a dire need to stratify individual patients for their likelihood of responding to either anti-PD-1 or anti-CTLA-4 monotherapy, or the combination. Since it is conceivable that ICB responses are influenced by both tumor cell-intrinsic and stromal factors, we hypothesized that a predictive classifier ought to mirror both of these distinct features. We used a panel of melanoma patient-derived xenografts (PDX), in which human stromal cells upon transplantation are naturally replaced by their murine counterparts, to computationally subtract PDX RNA expression signals from those in patients’ melanomas. We thus derived both “Stromal immune” (SIM) and tumor cell-specific “Tumor-autonomous inflammation” (TAF) signatures. Here we report that the SIM signature predicts response to anti-CTLA-4 but not anti-PD-1 treatment, whereas the tumor TAF signature predicts response to anti-PD-1 but not anti-CTLA-4. Moreover, when used in conjunction, the signatures accurately predict response in two independent patient cohorts treated with the anti-CTLA-4 + anti-PD-1 combination. These signatures may be clinically exploited for personalized treatment advice based on the predicted benefit from either anti-CTLA-4 or anti-PD-1 monotherapy or their combination.
2020-04-27 | GSE129127 | GEO
Project description:The therapeutic landscape of melanoma is rapidly changing. While targeted inhibitors yield significant responses, their clinical benefit is often limited by the early onset of drug resistance. This motivates the pursuit to establish more durable clinical responses, by developing combinatorial therapies. But while potential new combinatorial targets steadily increase in numbers, they cannot possibly all be tested in patients. Similarly, while genetically engineered mouse melanoma models have great merit, they do not capture the enormous genetic diversity and heterogeneity typical in human melanoma. Furthermore, whereas in vitro studies have many advantages, they lack the presence of micro-environmental factors, which can have a profound impact on tumor progression and therapy response. This prompted us to develop an in vivo model for human melanoma that allows for studying the dynamics of tumor progression and drug response, with concurrent evaluation and optimization of new treatment regimens. Here, we present a collection of patient-derived xenografts (PDX), derived from BRAFV600E, NRASQ61 or BRAFWT/NRASWT melanoma metastases. The BRAFV600E PDX melanomas were acquired both prior to treatment with the BRAF inhibitor vemurafenib and after resistance had occurred, including six matched pairs. We find that PDX resemble their human donors’ melanomas regarding biomarkers, chromosomal aberrations, RNA expression profiles, mutational spectrum and targeted drug resistance patterns. Mutations, previously identified to cause resistance to BRAF inhibitors, are captured in PDX derived from resistant melanomThis melanoma PDX platform represents a comprehensive public resource to study both fundamental and translational aspects of melanoma progression and treatment in a physiologically relevant setting.
2016-05-27 | GSE66539 | GEO
Project description:Identify therapeutic vulnerabilities of palbociclib resistance in metastatic breast cancer patient-derived xenograft models and identify key biomarkers that correlate with development of resistance to inform new treatment directions
2023-07-10 | GSE229235 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2016-05-26 | GSE73738 | GEO
Project description:Establishment of palbociclib resistant metastatic breast cancer patient-derived xenograft models
| PRJNA953614 | ENA
Project description:Although remission rates for metastatic melanoma are generally very poor, some patients can survive for prolonged periods following metastasis. We used gene expression profiling, mitotic index (MI), and quantification of tumor infiltrating leukocytes (TILs) and CD3+ cells in metastatic lesions to search for a molecular basis for this observation and to develop improved methods for predicting patient survival. We identified a group of 266 genes associated with postrecurrence survival. Genes positively associated with survival were predominantly immune response related (e.g., ICOS, CD3d, ZAP70, TRAT1, TARP, GZMK, LCK, CD2, CXCL13, CCL19, CCR7, VCAM1) while genes negatively associated with survival were cell proliferation related (e.g., PDE4D, CDK2, GREF1, NUSAP1, SPC24). Identification of genes associated with survival of metastatic melanoma Survival Analysis was performed using Statistical Analysis of Microarrays B D denotes same patient with multiple reccurences
2009-11-30 | E-GEOD-19234 | biostudies-arrayexpress
Project description:The phenomenon that metastatic lesion developed on injured sites has long been recognized in a number of cancers, such as melanoma. The factors associated with wound healing that attract circulating tumor cells have remained unknown, however. A patient with acral lentiginous melanoma presented with a metastatic lesion that appeared 1 month after trauma. To explore the molecular mechanism underlying the promotion of wound metastasis in melanoma, we performed microarray analysis of the metastatic lesions (n = 2) and the primary lesions (n = 3) of the patient. Using Human Genome U133 Plus 2.0 array, we compared global gene expression profiles of tissues derived from the patient’s primary (n = 3) and wound metastatic (n = 2) lesions to search for particular biological functions in genes of which expression intensities were increased in the wound metastasic lesions of melanoma.
2015-06-26 | E-GEOD-62837 | biostudies-arrayexpress
Project description:We profiled the tumor heterogeneity of individual primary tumor and matched metastatic cells of breast cancer using a set of patient derived xenograft models with different metastatic potentials. This data set gives insights into the transcriptional tumor heterogeneity, the transcriptional differences between primary tumor and matched metastatic cells and how these are changing in regard to varying metastatic phenotypes.
2022-08-15 | GSE211146 | GEO