SAMHD1 is recurrently mutated in T-cell prolymphocytic leukemia

Johansson, Patricia; Klein-Hitpaß, Ludger; Choidas, Axel; Habenberger, Peter; Mahboubi, Bijan; Kim, Baek; Bergmann, Anke; Scholtysik, René; Brauser, Martina; Lollies, Anna; Siebert, Reiner; Zenz, Thorsten; Dührsen, Ulrich; Küppers, Ralf; Dürig, Jan

doi:10.1038/s41408-017-0036-5

Cited by 58 publications

(80 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, we aimed to test the potential usefulness of our derived models for the identification of protein biomarkers by applying them on available RNA-Seq datasets from human total CD4 + T cells. We found data-sets for five different diseases [18][19][20][21]; asthma, allergic rhinitis, obesity-induced asthma, pro-lymphocytic leukaemia, and multiple sclerosis (MS), as well as corresponding controls. Because our models correlated well to protein abundances, we hypothesised that differential expression tests using the predicted proteins between patients and controls to be more sensitive than testing directly on the mRNA expression for all splice variants individually.…”

Section: Applying the Model To Clinical Datasets Revealed Potential Bmentioning

confidence: 99%

A validated strategy to infer protein biomarkers from RNA-Seq by combining multiple mRNA splice variants and time-delay

Magnusson

Rundquist

Kim

et al. 2019

Preprint

View full text Add to dashboard Cite

BackgroundProfiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. MethodsWe characterised time-series of primary human naïve CD4 + T cells during early T-helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We then performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time-delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-Seq datasets to evaluate biomarker discovery in five T-cell associated diseases, validating the findings for multiple sclerosis (MS) and asthma. ResultsThe new models demonstrated median correlations of mRNA-to-protein abundance of 0.79-0.94, significantly out-performing models not including the usage of multiple splice variants and time-delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was clinically validated in MS using two independent cohorts, for treatment response and prognosis. ConclusionOur splice variant and time-delay models substantially improved the prediction of protein abundance from mRNA data in three immune cell-types. The models provided valuable biomarker candidates, which were validated in clinical studies of MS and asthma. We propose that our strategy is generally applicable for biomarker discovery.MG initiated and supervised the study. RM and OR performed bioinformatics analyses, and RM performed the modelling. These analyses were led by MG, CA, JT, and DGC. OR performed experimental work on T-cell differentiation, which were supervised by CEN, MCJ, JE and MB. MJK and CHN performed the proteomics analysis, which was supervised by MSK. FP and JM recruited patients and collected clinical material, and SH performed and analysed the biomarker validation assays, which were led by IK, MCJ, and JE. All authors contributed to and approved the final draft for publication. mRNA expression levels. Mol Biosyst 2009, 5: 1512-26. 7. Vogel C, Marcotte EM: Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 2012, 13: 227-32. 8. Liu Y, Beyer A, Aebersold R: On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell 2016, 165: 535-50. 9. Zhao J, Qin B, Nikolay R, Spahn CMT, Zhang G: Translatomics: The Global View of Translation. Int J Mol Sci 2019, 20. 10. Wethmar K, Smink JJ, Leutz A: Upstream open reading frames: molecular switches in (patho)physiology. Bioessays 2010, 32: 885-93. 11. Floor SN, Doudna JA: Tunable protein synthesi...

show abstract

Section: Applying the Model To Clinical Datasets Revealed Potential Bmentioning

confidence: 99%

A validated strategy to infer protein biomarkers from RNA-Seq by combining multiple mRNA splice variants and time-delay

Magnusson

Rundquist

Kim

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Purine dNTPs increased more than pyrimidine dNTPs in transformed cells, 12,13 in primary fibroblasts, 8 and in whole mouse embryos. SAMHD1 mutations were first recognized as potential founding events in chronic lymphocytic leukemias 24 and later identified in additional forms of leukemia 25 and in solid tumors. 10 Besides the effects on dNTP pool composition, in normal human fibroblasts siRNA-silencing of SAMHD1 inhibits cell proliferation and disturbs the G1/S transition.…”

Section: Introductionmentioning

confidence: 99%

“…1,2,[21][22][23] The abnormal dNTP pools associated with SAMHD1 deficiency may contribute to cancer development. SAMHD1 mutations were first recognized as potential founding events in chronic lymphocytic leukemias 24 and later identified in additional forms of leukemia 25 and in solid tumors. 14,26 On this basis, SAMHD1 was suggested to function as a tumor suppressor.…”

Section: Introductionmentioning

confidence: 99%

SAMHD1‐deficient fibroblasts from Aicardi‐Goutières Syndrome patients can escape senescence and accumulate mutations

et al. 2019

View full text Add to dashboard Cite

In mammalian cells, the catabolic activity of the dNTP triphosphohydrolase SAMHD1 sets the balance and concentration of the four dNTPs. Deficiency of SAMHD1 leads to unequally increased pools and marked dNTP imbalance. Imbalanced dNTP pools increase mutation frequency in cancer cells, but it is not known if the SAMHD1induced dNTP imbalance favors accumulation of somatic mutations in nontransformed cells. Here, we have investigated how fibroblasts from Aicardi-Goutières Syndrome (AGS) patients with mutated SAMHD1 react to the constitutive pool imbalance characterized by a huge dGTP pool. We focused on the effects on dNTP pools, cell cycle progression, dynamics and fidelity of DNA replication, and efficiency of UV-induced DNA repair. AGS fibroblasts entered senescence prematurely or upregulated genes involved in G1/S transition and DNA replication. The normally growing AGS cells exhibited unchanged DNA replication dynamics and, when quiescent, faster rate of excision repair of UV-induced DNA damages. To investigate whether the lack of SAMHD1 affects DNA replication fidelity, we compared de novo mutations in AGS and WT cells by exome next-generation sequencing. Somatic variant analysis indicated a mutator phenotype suggesting that SAMHD1 is a caretaker gene whose deficiency is per se mutagenic, promoting genome instability in nontransformed cells.

show abstract

“…SAMHD1 mutations are present in several types of cancer and in many cases result in reduced mRNA and protein levels (Clifford et al, 2014; Johansson et al, 2018; Rentoft et al, 2016). We therefore wished to explore our finding of dN-induced cell death in the context of malignant disease.…”

Section: Resultsmentioning

confidence: 99%

“…SAMHD1 mutations cause Aicardi-Goutières syndrome, a rare autoinflammatory disease characterised by chronic production of type I interferons, a family of cytokines typically upregulated only during acute virus infection (Crow and Manel, 2015; Rice et al, 2009). Furthermore, mutations in the SAMHD1 gene have been found in several types of cancer, including colorectal cancer and leukaemias (Clifford et al, 2014; Johansson et al, 2018; Rentoft et al, 2016; Schuh et al, 2012). It is possible that inactivation of SAMHD1 provides transformed cells with a growth advantage simply due to elevated dNTP levels.…”

Section: Introductionmentioning

confidence: 99%

SAMHD1 limits the efficacy of forodesine in leukaemia by protecting cells against cytotoxicity of dGTP

Davenne

Klintman

Sharma

et al. 2020

Preprint

View full text Add to dashboard Cite

Immucillin H, chronic lymphocytic leukaemia 28 29 Running title 30 SAMHD1 protects against dGTP toxicity 31 -2 -Graphical Abstract 32 33 34 Highlights 35 • SAMHD1-deficient cells die upon exposure to deoxyribonucleosides (dNs) 36 • Deoxyguanosine (dG) is the most toxic dN, inducing apoptosis in cells lacking 37 SAMHD1 38 • SAMHD1-mutated leukaemic cells can be killed by dG and the PNP-inhibitor 39 forodesine 40 41 In Brief 42 SAMHD1 degrades deoxyribonucleoside triphosphates (dNTPs), the building blocks 43 of DNA. Davenne et al. found that SAMHD1 protects cells against dNTP imbalances. 44Exposure of SAMHD1-deficient cells to deoxyguanosine (dG) results in increased 45 intracellular dGTP levels and subsequent apoptosis. This can be exploited to 46 selectively kill cancer cells that acquired SAMHD1 mutations. 47 -3 - Summary 48The anti-leukaemia agent forodesine causes cytotoxic overload of intracellular 49 deoxyguanosine triphosphate (dGTP) but is efficacious only in a subset of patients. 50We report that SAMHD1, a phosphohydrolase degrading deoxyribonucleoside 51 triphosphates (dNTPs), protected cells against the effects of dNTP imbalances. 52 SAMHD1-deficient cells induced intrinsic apoptosis upon provision of 53deoxyribonucleosides, particularly deoxyguanosine (dG). Moreover, dG and 54 forodesine acted synergistically to kill cells lacking SAMHD1. Using mass cytometry, 55we found that these compounds killed SAMHD1-deficient malignant cells from patients 56 with chronic lymphocytic leukaemia (CLL). Normal cells and CLL cells from patients 57 without SAMHD1 mutation were unaffected. We therefore propose to use forodesine 58 as a precision medicine for leukaemia, stratifying patients by SAMHD1 genotype or 59 expression. 60

show abstract

SAMHD1 is recurrently mutated in T-cell prolymphocytic leukemia

Cited by 58 publications

References 49 publications

A validated strategy to infer protein biomarkers from RNA-Seq by combining multiple mRNA splice variants and time-delay

A validated strategy to infer protein biomarkers from RNA-Seq by combining multiple mRNA splice variants and time-delay

SAMHD1‐deficient fibroblasts from Aicardi‐Goutières Syndrome patients can escape senescence and accumulate mutations

SAMHD1 limits the efficacy of forodesine in leukaemia by protecting cells against cytotoxicity of dGTP

Contact Info

Product

Resources

About