Dataset Information

Multilayered proteomics uncovers Notch/PKC targeting to overcome resistance to Notch inhibition in acute lymphoblastic leukemia

ABSTRACT: NOTCH1 is a crucial oncogenic driver in T-cell acute lymphoblastic leukemia (T-ALL), making it an attractive therapeutic target. However, the success of targeted therapy using γ-secretase inhibitors (GSIs), small molecules blocking Notch cleavage and subsequent activation, has been limited due to toxicity and development of resistance, thus restricting their clinical efficacy. Here we systematically compare GSI resistant and sensitive cell states by quantitative mass spectrometry-based phosphoproteomics, using complementary models of resistance, including T-ALL patient-derived xenografts (PDX) models. Our datasets reveal common mechanisms of GSI resistance, including a distinct kinase signature that involves protein kinase C delta (PKCδ). We demonstrate that the PKC inhibitor sotrastaurin enhances the anti-leukemic activity of GSI in PDX models and completely abrogates the development of acquired GSI resistance “in-vitro”. Overall, we highlight the potential of proteomics to dissect alterations in cellular signaling and identify druggable pathways in cancer.

INSTRUMENT(S): Orbitrap Exploris 480, Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Spleen, Cell Suspension Culture, T Cell

DISEASE(S): Acute Leukemia

SUBMITTER: Giulia Franciosa

LAB HEAD: Jesper Velgaard Olsen

PROVIDER: PXD018744 | Pride | 2021-03-08

REPOSITORIES: Pride

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Publications

Proteomics of resistance to Notch1 inhibition in acute lymphoblastic leukemia reveals targetable kinase signatures.

Franciosa Giulia G Smits Jos G A JGA Minuzzo Sonia S Martinez-Val Ana A Indraccolo Stefano S Olsen Jesper V JV

Nature communications 20210504 1

Notch1 is a crucial oncogenic driver in T-cell acute lymphoblastic leukemia (T-ALL), making it an attractive therapeutic target. However, the success of targeted therapy using γ-secretase inhibitors (GSIs), small molecules blocking Notch cleavage and subsequent activation, has been limited due to development of resistance, thus restricting its clinical efficacy. Here, we systematically compare GSI resistant and sensitive cell states by quantitative mass spectrometry-based phosphoproteomics, usin ...[more]

PMID: 33947863

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Project description:Engraftment of primary pancreas ductal adenocarcinomas (PDAC) in mice to generate patient derived xenograft (PDX) models is a promising platform to for biological and therapeutic studies in this disease. However, these models are still incompletely characterized. Here, we measured the impact of the murine environment on the gene expression of the engrafted human tumoral cells. We have analyzed gene expression profiles from 35 new PDX models and compared them with previously published microarray data from PDAC and hepatocellular carcinoma (HCC). Our results showed that PDX models derived from PDAC, or HCC, were clearly different to the cell lines derived from the same cancer tissues. Indeed, PDAC- and HCC-derived cell lines are indistinguishable one from the other based in their gene expression profiles. In contrast, the transcriptomes of PDAC and HCC PDX models are clearly different and more similar to their original tumor than to PDX models from the other tumor type. Interestingly, the main differences between pancreatic PDX models and human PDAC is the expression of genes involved in pathways related with extracellular matrix interactions and cell cycle regulation likely reflecting the adaptations of the tumors to the new environment. Furthermore, most of these differences are detected in the first passages after the tumor engraftment, indicating early phases of the adaptation process. In conclusion, different from conventional cancer cell lines, PDX models of PDAC retain similar gene expression profiles of PDAC. Expression changes are mainly related to genes involved in stromal pathways likely reflecting the adaptation to new environments. We also provide evidence of the stability of gene expression patterns over subsequent passages. We have analyzed gene expression profiles from 35 new PDX models and compared them with previously published in GEO microarray data. We used PDX models, primary tumors and cell lines from PDAC and hepatocellular carcinoma. All these public data were re-process in order to compare with our 35 samples

Dataset Information

Multilayered proteomics uncovers Notch/PKC targeting to overcome resistance to Notch inhibition in acute lymphoblastic leukemia

Publications

Proteomics of resistance to Notch1 inhibition in acute lymphoblastic leukemia reveals targetable kinase signatures.

Similar Datasets

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