Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Pre-clinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma

ABSTRACT: Pre-clinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma

PROVIDER: PRJNA912227 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Pre-clinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma

Project description:Multiple myeloma (MM) is a neoplasia of bone marrow (BM) plasma cells that remains largely incurable with current treatment. Advancing therapeutic discoveries has been hampered by the lack of genetically heterogeneous models of MM. To circumvent this limitation, we engineered fifteen mouse models carrying combinations of eight MM genetic drivers, which fulfilled the pathogenesis of human disease including progression from premalignant states under immune surveillance. Integrative analyses of ⁓500 mice and ⁓1,000 patients revealed a MAPK-MYC genetic pathway that regulates time to progression and immune escape mechanisms across genetically heterogeneous tumors. During progression, MYC-dependent and independent remodeling of the BM microenvironment divided MM into immune categories with predominance of selected T-cell subpopulations, which dictated immunotherapy responses. Experimental targeting of the cytotoxic or immunosuppressive T-cell states observed in refractory MM patients enhanced immunotherapy effectiveness. Our resource enabled characterization of MM cell-intrinsic and immunological traits at unprecedented levels, which will accelerate the translation of personalized immunotherapy.

2023-01-31 | GSE220997 | GEO

Pre-clinical models of genetically heterogeneous multiple myeloma reveal immune evasion mechanisms and predict clinical immunotherapy outcomes

Project description:Pre-clinical models of genetically heterogeneous multiple myeloma reveal immune evasion mechanisms and predict clinical immunotherapy outcomes

| PRJNA846769 | ENA

Mouse models of genetically heterogeneous multiple myeloma reveal mechanisms of immune evasion that predict clinical immunotherapy responses

Project description:Mouse models of genetically heterogeneous multiple myeloma reveal mechanisms of immune evasion that predict clinical immunotherapy responses

| PRJNA845532 | ENA

Preclinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma.

Project description:The historical lack of preclinical models reflecting the genetic heterogeneity of multiple myeloma (MM) hampers the advance of therapeutic discoveries. To circumvent this limitation, we screened mice engineered to carry eight MM lesions (NF-κB, KRAS, MYC, TP53, BCL2, cyclin D1, MMSET/NSD2 and c-MAF) combinatorially activated in B lymphocytes following T cell-driven immunization. Fifteen genetically diverse models developed bone marrow (BM) tumors fulfilling MM pathogenesis. Integrative analyses of ∼500 mice and ∼1,000 patients revealed a common MAPK-MYC genetic pathway that accelerated time to progression from precursor states across genetically heterogeneous MM. MYC-dependent time to progression conditioned immune evasion mechanisms that remodeled the BM microenvironment differently. Rapid MYC-driven progressors exhibited a high number of activated/exhausted CD8+ T cells with reduced immunosuppressive regulatory T (Treg) cells, while late MYC acquisition in slow progressors was associated with lower CD8+ T cell infiltration and more abundant Treg cells. Single-cell transcriptomics and functional assays defined a high ratio of CD8+ T cells versus Treg cells as a predictor of response to immune checkpoint blockade (ICB). In clinical series, high CD8+ T/Treg cell ratios underlie early progression in untreated smoldering MM, and correlated with early relapse in newly diagnosed patients with MM under Len/Dex therapy. In ICB-refractory MM models, increasing CD8+ T cell cytotoxicity or depleting Treg cells reversed immunotherapy resistance and yielded prolonged MM control. Our experimental models enable the correlation of MM genetic and immunological traits with preclinical therapy responses, which may inform the next-generation immunotherapy trials.

| S-EPMC10033443 | biostudies-literature

Pre-clinical models of genetically heterogeneous multiple myeloma reveal immune evasion mechanisms and predict clinical immunotherapy outcomes

Project description:The historical lack of pre-clinical models reflecting the genetic heterogeneity of multiple myeloma (MM) hampers advancing therapeutic discoveries. To circumvent this limitation, we have generated fifteen genetically diverse models that developed bone marrow tumors fulfilling MM pathogenesis. Transcriptomic analysis of tumor plasma cells revealed that MYC activation regulates time to progression. Bone marrow cell composition analysis classified myeloma tumors into immune-cold and inflamed categories, which condition immune checkpoint blockade responses in mouse multiple myeloma models.

2023-03-17 | GSE205644 | GEO

Prediction of neurological outcomes shortly after cardiac arrest

2013-06-01 | GSE29540 | GEO

Prediction of neurological outcomes shortly after cardiac arrest

Project description:Prediction of neurological outcomes shortly after cardiac arrest would represent a major breakthrough. We tested the ability of gene expression profiles of blood cells to predict outcome in cardiac arrest patients. 35 consecutive cardiac arrest patients treated with therapeutic hypothermia (33Â°C for 24h) were included in this prospective monocentre study. Cerebral Performance Category (CPC) was determined at discharge and 6 months later. All patients had blood sampling at the end of hypothermia. Gene expression profiles of blood cells were determined using 25,000~gene microarray in two groups of patients: good outcome (CPC 1-2) and bad outcome (CPC 3-5).

2013-06-01 | E-GEOD-29540 | biostudies-arrayexpress

Data-driven prediction of colonization outcomes for complex microbial communities

Project description:Data-driven prediction of colonization outcomes for complex microbial communities

| PRJEB60398 | ENA

Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes

Project description:Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes

| PRJNA646065 | ENA

Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes [WGBS]

Project description:Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes [WGBS]

| PRJNA645920 | ENA