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

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

PROVIDER: PRJNA846769 | ENA |

REPOSITORIES: ENA

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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.

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

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

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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.

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

Project description:Data from engineered fifteen mouse strains fully encompassing MM genetic heterogeneity that recapitulate the key pathogenic elements in human MM development and multiple myeloma patients.

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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.

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Project description:Immune checkpoint inhibitors (ICI) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA-seq (scRNA-seq) from 31 melanoma tumors and novel computational methods to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is present prior to immunotherapy, characterizes cold niches, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in a mouse model when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI resistant cell states, identifies clinically predictive signatures, and forms a basis for the development of novel therapeutic strategies that could overcome immunotherapy resistance.

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KRAS drives immune evasion in a genetic model of pancreatic cancer [bulk RNA-seq]

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Functional genomic landscape of cancer-intrinsic immune evasion to cytotoxic T lymphocyte killing [CRISPRseq]

Project description:The genetic circuits that allow cancer cells to evade destruction by the host immune system remain poorly understood. To identify a phenotypically robust core set of genes and pathways that facilitate cancer cell-intrinsic evasion to cytotoxic T lymphocyte (CTL)-mediated killing, we performed genome-wide CRISPR screens across a panel of genetically diverse cancer models cultured in the presence of CTLs. We uncovered a core set of 182 genes whose individual perturbation leads to either cancer cell sensitivity or resistance to CTL toxicity. Systematic exploration of our dataset using genetic co-similarity reveals the hierarchical and coordinated nature by which genes and pathways act to orchestrate intrinsic CTL evasion, with discrete functional modules controlling the interferon response and tumor necrosis factor alpha (TNFa)-induced cytotoxicity emerging as dominant sub-phenotypes. Our data establish a central role for previously identified negative regulators of the Type II interferon response (e.g. Ptpn2, Socs1, Adar1) in mediating intrinsic CTL evasion and demonstrate a requirement for the lipid droplet related gene Fitm2 for maintaining cell fitness upon exposure to interferon gamma (IFNg). Additionally, we identify the autophagy pathway as a conserved mediator of cancer intrinsic CTL evasion, required to resist cytokine-mediated cytotoxicity caused by IFNg and TNFa. By mapping cytokine- and CTL-based genetic interactions, as well as in vivo CRISPR screens, we illuminate the pleiotropic nature by which autophagy acts to orchestrate intrinsic CTL evasion and highlight the importance of our observed effects within the tumor microenvironment. Collectively, our data expands our appreciation of the genetic circuits that contribute to cancer intrinsic immune evasion, highlighting the importance of leveraging systematic functional genomics approaches for furthering our understanding of this biology.

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