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Faigle, W.

Publications and source records attributed to Faigle, W..

3 recordsLinked to original sources

Oncogenic fusions induce an extensive cancer-restricted cryptic proteome in Ewing sarcoma

How tumors generate a cryptic "dark" proteome absent from healthy tissues, and whether it can be reversibly activated, remains unclear. In Ewing sarcoma, all tumors are driven by EWSR1::ETS fusions, making it a tractable model to study dark proteome activation. Integrating matched transcriptomes, translatomes and proteomes from 48 patient tumors with long-read RNA sequencing, single-cell Ribo-seq, proteomics and immunopeptidomics in cell line models, we show that EWSR1::FLI1 acts as a reversible switch recurrently inducing hundreds of cancer-specific neoproteins. Many arise from canonical coding regions via intragenic transcription start sites, generating truncated or out-of-frame neoproteins. We uncover a fusion-dependent increase in ribosomal readthrough into poly(A) tails, generating a stable 80-amino-acid TRPM4 neoprotein that, despite lacking a stop codon, is the most abundant tumor-specific microprotein across patients. Proteomic, immunopeptidomic, and immunofluorescence analyses validate neoproteins as tumor-restricted antigens, revealing a therapeutically actionable cancer dark proteome controlled by one oncogenic fusion.

cancer biology↗

Specific killing of Ewing sarcoma by TCR-T cells targeting public neogene-encoded antigens

EWSR1::FLI1, the oncogenic chimeric transcription factor driving Ewing sarcoma (EwS)induces expression of exquisitely EwS-specific neogenes (Ew_NGs) through neomorphic binding and transcription activation at GGAA microsatellites in genomic regions that are silent in normal tissues. We show that peptides encoded by Ew_NGs are presented on HLA-I complexes on EwS cells. The cytokine secretion of CD8+ T cells specific for Ew_NG-encoded HLA-I-bound peptides is activated by all HLA-I-matched EwS cells but not by non-EwS cells. These T cells kill EwS cells in an HLA-I restricted manner. This cytotoxicity is dependent on the expression of EWSR1::FLI1 and of the corresponding Ew_NG. It can be reproduced by transduction of the TCR into donor T cells (TCR-T) which kill EwS cells in vivo. Moreover, we show that neither off target nor allogeneic activation are observed with TCR-T thus paving the way for cell therapy in relapsed/resistant EwS patients for which therapeutic options are very limited. Statement of significanceThe chimeric transcription factor EWSR1::FLI1 generates tumor-specific neogenes encoding neoantigen presented by the HLA-I molecules of Ewing cells. Neoantigen-specific CD8+ T-cell clones and engineered TCR-T cells can selectively recognize and kill EwS tumor cells in vitro and in vivo.

immunology↗

Prediction of combination therapies based on topological modeling of the immune signaling network in Multiple Sclerosis

Signal transduction deregulation is a hallmark of many complex diseases, including Multiple Sclerosis (MS). Here, we performed ex vivo multiplexed phosphoproteomic assays in PBMCs from 180 MS patients either untreated or treated with fingolimod, natalizumab, interferon-beta, glatiramer acetate or the experimental therapy epigallocatechin gallate (EGCG), and from 60 matched healthy controls. Fitting a bespoke literature-derived network of MS-related pathways using logic modeling yielded a signaling network specific for each patient. Patient models were merged to characterize healthy-, disease- and drug-specific signaling networks. We defined a co-druggability score based on the topology for each drugs network. We used this score to identify kinase interactions whose activity could be reverted to a "healthy-like" status by combination therapy. We predicted several combinations with approved MS drugs. Specifically, TAK1 kinase, involved in TGF-B, toll-like receptor, B-cell receptor and response to inflammation pathways was found to be highly deregulated and co-druggable with four MS drugs. One of these predicted combinations, Fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Our approach based on patient-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases. One sentence summaryA new approach to predict combination therapies based on modeling signaling architecture using phosphoproteomics from patients with Multiple Sclerosis characterizes deregulated signaling pathways and reveals new therapeutic targets and drug combinations.

systems biology↗